CN113487100B - Global accurate prediction method and system for photovoltaic power generation output - Google Patents

Global accurate prediction method and system for photovoltaic power generation output Download PDF

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CN113487100B
CN113487100B CN202110815398.4A CN202110815398A CN113487100B CN 113487100 B CN113487100 B CN 113487100B CN 202110815398 A CN202110815398 A CN 202110815398A CN 113487100 B CN113487100 B CN 113487100B
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张江丰
苏烨
尹峰
邓宇豪
郑可轲
孙坚栋
卢敏
陈文进
丁伟聪
陈巍文
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State Grid Zhejiang Electric Power Research Institute Co., Ltd.
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Hangzhou E Energy Electric Power Technology Co Ltd
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

本发明公开了一种光伏发电出力全局精准预测方法及系统。本发明采用的技术方案为：将区域电网内每个光伏电站的单个功率预测结果进行数值累加，得到区域电网光伏发电出力全局预测值；生成区域电网光伏电站的坐标分布图；根据区域电网内的每个光伏电站的预测出力与实际出力之差，计算区域电网内光伏电站出力之间的时空相关性，建立预测误差分布时序图；计算光伏出力误差移动的前后位置，并估计出下一时刻的预测误差；结合地理坐标信息及各光伏电站所在区域的气象信息，计算出光伏电站预测误差的相关系数，修正下一时刻的预测误差。本发明在融合地理气象信息的基础上实现光伏发电出力全局精准预测，提升了电网的光伏消纳空间和运行稳定性。

The invention discloses a method and system for global accurate forecasting of photovoltaic power generation output. The technical solution adopted in the present invention is: the single power prediction results of each photovoltaic power station in the regional power grid are numerically accumulated to obtain the global forecast value of the photovoltaic power generation output of the regional power grid; the coordinate distribution map of the photovoltaic power station in the regional power grid is generated; The difference between the predicted output of each photovoltaic power station and the actual output, calculate the time-space correlation between the output of photovoltaic power stations in the regional power grid, and establish a time series diagram of the forecast error distribution; calculate the front and rear positions of the photovoltaic output error movement, and estimate the next moment Forecast error: Combined with geographical coordinate information and meteorological information in the area where each photovoltaic power station is located, calculate the correlation coefficient of photovoltaic power station prediction error, and correct the prediction error at the next moment. The present invention realizes global accurate prediction of photovoltaic power generation output on the basis of fusion of geographical and meteorological information, and improves the photovoltaic consumption space and operation stability of the power grid.

Description

光伏发电出力全局精准预测方法及系统Photovoltaic power output global accurate prediction method and system

技术领域technical field

本发明涉及光伏发电出力全局精准预测领域，具体地说是一种融合地理气象信息的光伏发电出力全局精准预测方法及系统。The invention relates to the field of global accurate forecasting of photovoltaic power generation output, in particular to a method and system for global precise forecasting of photovoltaic power generation output integrated with geographical and meteorological information.

背景技术Background technique

光伏发电输出功率受到太阳辐射、云层遮挡等气象因素的影响，具有较强的间歇性和随机性，因此提高光伏出力全局预测精度对区域电力系统全局性的安全稳定和经济运行都具有重要意义。The output power of photovoltaic power generation is affected by meteorological factors such as solar radiation and cloud cover, and has strong intermittency and randomness. Therefore, improving the global prediction accuracy of photovoltaic output is of great significance to the overall safety, stability and economic operation of the regional power system.

目前，国内外对光伏出力预测的研究主要集中在通过物理建模或者统计方法对单个电站进行预测，相关成果已经十分成熟，但对于区域性光伏功率预测的相关研究却很少。大规模的光伏电站及分布式光伏并网，将会为整个区域电力系统的电力调度、调峰带来一定的困难。因此，全局性光伏功率预测研究就显得更为重要。At present, domestic and foreign research on photovoltaic output forecasting mainly focuses on forecasting a single power station through physical modeling or statistical methods. The relevant results are very mature, but there are few related studies on regional photovoltaic power forecasting. Large-scale photovoltaic power plants and distributed photovoltaic grid connection will bring certain difficulties to the power dispatching and peak regulation of the entire regional power system. Therefore, the global photovoltaic power forecasting research is even more important.

综上所述，如何提供一种光伏发电出力全局精准预测方法，在实现对全局光伏出力预测的基础上，融合区域电网的地理气象信息，修正并减少区域电网对光伏出力的预测误差，是本领域技术人员亟待解决的问题。To sum up, how to provide a global accurate prediction method for photovoltaic power generation output, on the basis of realizing the global photovoltaic output prediction, integrate the geographical and meteorological information of the regional power grid, and correct and reduce the prediction error of the regional power grid for photovoltaic output is the key Problems to be solved urgently by those skilled in the art.

发明内容Contents of the invention

有鉴于此，本发明的目的是通过弥补现有技术领域和体系中的不足，提供一种融合地理气象信息的光伏发电出力全局精准预测方法及系统，在融合地理气象信息的基础上实现光伏发电出力全局精准预测，以提升电网的光伏消纳空间和运行稳定性。In view of this, the purpose of the present invention is to provide a method and system for global accurate prediction of photovoltaic power generation output integrated with geographical and meteorological information by making up for the deficiencies in the existing technical field and system, and to realize photovoltaic power generation on the basis of integrated geographical and meteorological information. Contribute to global accurate forecasting to improve the photovoltaic consumption space and operational stability of the power grid.

为实现上述目的，本发明提供如下的技术方案：光伏发电出力全局精准预测方法，其包括：In order to achieve the above purpose, the present invention provides the following technical solution: a method for global accurate forecasting of photovoltaic power generation output, which includes:

步骤1)，将区域电网内每个光伏电站的单个功率预测结果进行数值累加，得到区域电网光伏发电出力全局预测值；Step 1), the individual power prediction results of each photovoltaic power station in the regional power grid are numerically accumulated to obtain the global forecast value of the photovoltaic power generation output of the regional power grid;

步骤2)，获取区域电网内每个光伏电站的经纬度坐标信息，生成区域电网光伏电站的坐标分布图；Step 2), obtaining the latitude and longitude coordinate information of each photovoltaic power station in the regional power grid, and generating a coordinate distribution map of the photovoltaic power station in the regional power grid;

步骤3)，获取区域电网内每个光伏电站的气象信息；Step 3), obtaining the meteorological information of each photovoltaic power station in the regional power grid;

步骤4)，计算区域电网内的每个光伏电站的预测出力与实际出力之差；Step 4), calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;

步骤5)，根据区域电网内的每个光伏电站的预测出力与实际出力之差，计算区域电网内光伏电站出力之间的时空相关性，建立预测误差分布时序图；Step 5), according to the difference between the predicted output of each photovoltaic power station in the regional power grid and the actual output, calculate the time-space correlation between the output of photovoltaic power plants in the regional power grid, and establish a time series diagram of the distribution of prediction errors;

步骤6)，将光伏电站的预测误差标准化处理后，标记在坐标分布图中各光伏电站的相应位置上；Step 6), after standardizing the prediction error of the photovoltaic power station, mark it on the corresponding position of each photovoltaic power station in the coordinate distribution diagram;

步骤7)，根据误差时间序列的相关性，计算出光伏出力误差移动的前后位置，即计算出误差的来源方向与距离，并估计出下一时刻的预测误差；Step 7), according to the correlation of the error time series, calculate the front and rear positions of the photovoltaic output error movement, that is, calculate the source direction and distance of the error, and estimate the prediction error at the next moment;

步骤8)，结合地理坐标信息及各光伏电站所在区域的气象信息，计算出光伏电站预测误差的相关系数；Step 8), combining the geographical coordinate information and the meteorological information of the area where each photovoltaic power station is located, calculates the correlation coefficient of the prediction error of the photovoltaic power station;

步骤9)，根据计算出的相关系数，修正步骤7)中下一时刻的预测误差。In step 9), the prediction error at the next moment in step 7) is corrected according to the calculated correlation coefficient.

进一步地，步骤3)中的气象信息包括太阳辐照度、风速、风向、云层大小和云层风向。Further, the meteorological information in step 3) includes solar irradiance, wind speed, wind direction, cloud size and cloud wind direction.

进一步地，步骤6)中，预测误差Perr的计算式如下：Further, in step 6), the calculation formula of prediction error P err is as follows:

Perr＝(Ppre-Preal)/PN (1)P err ＝(P pre -P real )/P N (1)

式中，Ppre为预测功率，Prea1为实测功率，PN为光伏电站的额定功率。In the formula, P pre is the predicted power, P rea1 is the measured power, and P N is the rated power of the photovoltaic power station.

进一步地，步骤7)中，下一时刻的预测误差为：Further, in step 7), the prediction error at the next moment is:

P′err(x,y,t+Δt)＝Perr(x-vxΔt,y-vyΔt,t) (2)P' err (x,y,t+Δt)=P err (xv x Δt,yv y Δt,t) (2)

式中，Perr(x,y,t)表示t时刻的误差分布图中(x,y)位置处的预测误差，vx和vy分别表示风速在x和y方向上的分量。In the formula, P err (x, y, t) represents the prediction error at the position (x, y) in the error distribution diagram at time t, and v x and v y represent the components of wind speed in the x and y directions, respectively.

进一步地，步骤8)中，光伏电站预测误差的相关系数如下：Further, in step 8), the correlation coefficient of the prediction error of the photovoltaic power plant is as follows:

式中，S1(t)为相关系数较低，表现为弱相关时的修正误差，此时修正误差保持不变S1(t)＝Perr(x,y,t)，其中x，y为待修正电站的位置；In the formula, S1(t) is the correction error when the correlation coefficient is low, which is shown as weak correlation. At this time, the correction error remains unchanged S1(t)=P err (x,y,t), where x and y are the Correct the location of the power station;

S2(t)为强相关时的修正误差，此时根据误差时间序列相关性最高点的位置的当前误差作为修正误差，S2(t)＝Perr(x1,y1,t-Δt)，x1，y1为误差时间序列相关性最高点的位置；S2(t) is the corrected error when there is strong correlation. At this time, the current error at the position of the highest correlation point of the error time series is used as the corrected error, S2(t)=P err (x 1 ,y 1 ,t-Δt), x 1 , y 1 is the position of the highest point of error time series correlation;

当相关系数介于弱相关与强相关之间时，则通过线性融合的方式来修正：When the correlation coefficient is between weak correlation and strong correlation, it is corrected by linear fusion:

α＝-2.5ρ+2 (4)α＝-2.5ρ+2 (4)

α是一个从1到0减小的变量，当ρ<0.4时，则通过S1计算修正误差；随着相关系数提高，误差移动模式逐渐清晰，则提高S2分量的权重；当ρ>0.8时，完全采用S2计算修正误差。α is a variable that decreases from 1 to 0. When ρ<0.4, the correction error is calculated through S1; as the correlation coefficient increases and the error movement mode becomes clearer, the weight of the S2 component is increased; when ρ>0.8, The correction error is completely calculated using S2.

进一步地，步骤9)中，下一时刻的预测误差的修正值如下：Further, in step 9), the correction value of the prediction error at the next moment is as follows:

P′pre(t+Δt)＝Ppre(t+Δt)+P′err(t+Δt)·PN (5)。P' pre (t+Δt)=P pre (t+Δt)+ P'err (t+Δt)·P N (5).

本发明采用的另一种技术方案为：光伏发电出力全局精准预测系统，其包括：Another technical solution adopted by the present invention is: a global precise prediction system for photovoltaic power generation output, which includes:

光伏发电出力全局预测单元，将区域电网内每个光伏电站的单个功率预测结果进行数值累加，得到区域电网光伏发电出力全局预测值；The global forecasting unit of photovoltaic power generation output is used to accumulate the individual power forecast results of each photovoltaic power station in the regional power grid to obtain the global forecast value of photovoltaic power generation output in the regional power grid;

坐标分布图生成单元，获取区域电网内每个光伏电站的经纬度坐标信息，生成区域电网光伏电站的坐标分布图；A coordinate distribution map generation unit, which obtains the latitude and longitude coordinate information of each photovoltaic power station in the regional power grid, and generates a coordinate distribution map of the photovoltaic power station in the regional power grid;

气象信息获取单元，获取区域电网内每个光伏电站的气象信息；The meteorological information acquisition unit acquires the meteorological information of each photovoltaic power station in the regional power grid;

预测出力与实际出力差计算单元，计算区域电网内的每个光伏电站的预测出力与实际出力之差；The predicted output and actual output difference calculation unit calculates the difference between the predicted output and actual output of each photovoltaic power station in the regional power grid;

预测误差分布时序图建立单元，根据区域电网内的每个光伏电站的预测出力与实际出力之差，计算区域电网内光伏电站出力之间的时空相关性，建立预测误差分布时序图；The prediction error distribution time sequence diagram building unit calculates the time-space correlation between the output of the photovoltaic power station in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;

预测误差坐标标记单元，将光伏电站的预测误差标准化处理后，标记在坐标分布图中各光伏电站的相应位置上；The prediction error coordinate marking unit, after standardizing the prediction error of the photovoltaic power station, marks it on the corresponding position of each photovoltaic power station in the coordinate distribution diagram;

下一时刻预测误差估计单元，根据误差时间序列的相关性，计算出光伏出力误差移动的前后位置，即计算出误差的来源方向与距离，并估计出下一时刻的预测误差；The prediction error estimation unit at the next moment calculates the front and rear positions of the photovoltaic output error movement according to the correlation of the error time series, that is, calculates the source direction and distance of the error, and estimates the prediction error at the next moment;

预测误差相关系数计算单元，结合地理坐标信息及各光伏电站所在区域的气象信息，计算出光伏电站预测误差的相关系数；The prediction error correlation coefficient calculation unit combines the geographical coordinate information and the meteorological information of the area where each photovoltaic power station is located to calculate the correlation coefficient of the photovoltaic power station prediction error;

下一时刻预测误差修正单元，根据计算出的相关系数，修正下一时刻的预测误差。The next time prediction error correction unit corrects the next time prediction error based on the calculated correlation coefficient.

本发明具有的有益效果如下：本发明对光伏单站未来预测误差进行估计，从而实现对光伏电站预测功率的提前修正。尤其在云层移动遮挡导致的光伏电站出力大幅下降时刻，利用区域多光伏电站的出力数据交互影响，本发明能够通过对光伏电站预测功率的提前修正，使预测功率具有更好的跟踪性与准确性。本发明能够简单且准确有效地在融合地理气象信息的基础上实现光伏发电出力全局精准预测，提升了电网的光伏消纳空间和运行稳定性。The beneficial effects of the present invention are as follows: the present invention estimates the future forecast error of a photovoltaic single station, thereby realizing the advance correction of the forecasted power of the photovoltaic power station. Especially at the moment when the output of photovoltaic power plants drops sharply due to cloud cover movement, the present invention can make the predicted power have better tracking and accuracy through the early correction of the predicted power of photovoltaic power plants by using the interactive influence of output data of multiple photovoltaic power plants in the region . The present invention can simply, accurately and effectively realize the global accurate prediction of the output of photovoltaic power generation on the basis of the fusion of geographical and meteorological information, and improves the photovoltaic consumption space and operation stability of the power grid.

附图说明Description of drawings

图1为本发明实施例1光伏发电出力全局精准预测方法的流程图；Fig. 1 is a flow chart of the global accurate prediction method of photovoltaic power generation output in Embodiment 1 of the present invention;

图2为本发明实施例1中区域电网光伏电站的坐标分布图；FIG. 2 is a coordinate distribution diagram of a photovoltaic power station in a regional power grid in Embodiment 1 of the present invention;

图3为本发明实施例2光伏发电出力全局精准预测系统的结构框图。Fig. 3 is a structural block diagram of a global precise prediction system for photovoltaic power generation output according to Embodiment 2 of the present invention.

具体实施方式Detailed ways

以下结合说明书附图对本发明作进一步详细说明。The present invention will be described in further detail below in conjunction with the accompanying drawings.

实施例1Example 1

本实施例提供一种光伏发电出力全局精准预测方法，如图1所示，其步骤如下：This embodiment provides a global accurate prediction method for photovoltaic power generation output, as shown in Figure 1, the steps are as follows:

1)将区域电网内每个光伏电站的单个功率预测结果进行数值累加，得到区域电网光伏发电出力全局预测值。1) The individual power forecast results of each photovoltaic power station in the regional grid are numerically accumulated to obtain the global forecast value of photovoltaic power generation output in the regional grid.

2)获取区域电网内每个光伏电站的经纬度坐标信息，生成区域电网光伏电站的坐标分布图，如图2所示。2) Obtain the latitude and longitude coordinate information of each photovoltaic power station in the regional power grid, and generate a coordinate distribution map of the photovoltaic power station in the regional power grid, as shown in Figure 2.

3)获取区域电网内每个光伏电站的气象信息，比如太阳辐照度、风速、云层大小、云层风向等信息。3) Obtain the meteorological information of each photovoltaic power station in the regional power grid, such as solar irradiance, wind speed, cloud size, cloud wind direction and other information.

4)计算区域电网内的每个光伏电站的预测出力与实际出力之差。4) Calculate the difference between the predicted output and the actual output of each photovoltaic power station in the regional grid.

5)根据区域电网内的每个光伏电站的预测出力与实际出力之差，计算区域电网内光伏电站出力之间的时空相关性，建立预测误差分布时序图。5) According to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, calculate the time-space correlation between the output of photovoltaic power stations in the regional power grid, and establish a time series diagram of the prediction error distribution.

6)将光伏电站的预测误差标准化处理后，标记在坐标分布图中各光伏电站的相应位置上；预测误差Perr计算式如下：6) After the prediction error of the photovoltaic power station is standardized, mark it on the corresponding position of each photovoltaic power station in the coordinate distribution map; the calculation formula of the prediction error P err is as follows:

Perr＝(Ppre-Preal)/PN (1)P err ＝(P pre -P real )/P N (1)

式中，Ppre为预测功率，Preal为实测功率，PN为该电站的额定功率。In the formula, P pre is the predicted power, P real is the measured power, and P N is the rated power of the power station.

7)根据误差时间序列的相关性，搜索匹配出光伏出力误差移动的前后位置，计算出误差的来源方向与距离，并估计出下一时候的预测误差；7) According to the correlation of the error time series, search and match the front and rear positions of the photovoltaic output error movement, calculate the source direction and distance of the error, and estimate the forecast error at the next time;

下一时刻的预测误差：The prediction error for the next moment:

P′err(x,y,t+Δt)＝Perr(x-vxΔt,y-vyΔt,t) (2)P' err (x,y,t+Δt)=P err (xv x Δt,yv y Δt,t) (2)

式中，Perr(x,y,t)表示t时刻的误差分布图中(x，y)位置处的误差值，vx和vy分别表示风速在x和y方向上的分量。In the formula, P err (x, y, t) represents the error value at the position (x, y) in the error distribution diagram at time t, and v x and v y represent the components of wind speed in the x and y directions, respectively.

8)结合地理坐标信息及各光伏电站所在区域的气象信息，比如云层的风向和风速测量数据计算出光伏电站预测误差的相关系数；8) Combining the geographical coordinate information and the meteorological information of the area where each photovoltaic power station is located, such as the wind direction and wind speed measurement data of the cloud layer, calculate the correlation coefficient of the prediction error of the photovoltaic power station;

式中，S1(t)为相关系数较低，表现为弱相关时的修正误差，此时修正误差保持不变S1(t)＝Perr(x,y,t)，其中x，y为待修正电站的位置；In the formula, S1(t) is the correction error when the correlation coefficient is low, which is shown as weak correlation. At this time, the correction error remains unchanged S1(t)=P err (x,y,t), where x and y are the Correct the location of the power station;

S2(t)为强相关时的修正误差，此时根据误差来源地点此时刻的误差作为修正误差，S2(t)＝Perr(x1,y1,t-Δt)，x1，y1为误差时间序列相关性最高点的位置。S2(t) is the corrected error when there is strong correlation. At this time, the error of the source of the error at this moment is used as the corrected error, S2(t)=P err (x 1 ,y 1 ,t-Δt), x 1 , y 1 is the position of the highest point of error time series correlation.

当相关系数介于弱相关与强相关之间时，则通过线性融合的方式来修正：When the correlation coefficient is between weak correlation and strong correlation, it is corrected by linear fusion:

α＝-2.5ρ+2 (4)α＝-2.5ρ+2 (4)

α是一个从1到0减小的变量，当ρ≤0.4时，则通过S1计算修正误差；随着相关系数提高，误差移动模式逐渐清晰，则提高S2分量的权重；当ρ≥0.8时，完全采用S2方法计算修正误差。α is a variable that decreases from 1 to 0. When ρ≤0.4, the correction error is calculated through S1; as the correlation coefficient increases and the error movement mode becomes clear, the weight of the S2 component is increased; when ρ≥0.8, The corrected error is calculated entirely using the S2 method.

9)根据计算出的相关系数，修正第(5)步所计算出的误差；9) Correct the error calculated in step (5) according to the calculated correlation coefficient;

下一时刻的预测值误差的修正值：The corrected value of the predicted value error at the next moment:

P′pre(t+Δt)＝Ppre(t+Δt)+P′err(t+Δt)·PN (5)。P' pre (t+Δt)=P pre (t+Δt)+ P'err (t+Δt)·P N (5).

实施例2Example 2

本实施例提供一种光伏发电出力全局精准预测系统，如图3所示，其包括：This embodiment provides a global accurate prediction system for photovoltaic power generation output, as shown in Figure 3, which includes:

光伏发电出力全局预测单元，将区域电网内每个光伏电站的单个功率预测结果进行数值累加，得到区域电网光伏发电出力全局预测值；The global forecasting unit of photovoltaic power generation output is used to accumulate the individual power forecast results of each photovoltaic power station in the regional power grid to obtain the global forecast value of photovoltaic power generation output in the regional power grid;

坐标分布图生成单元，获取区域电网内每个光伏电站的经纬度坐标信息，生成区域电网光伏电站的坐标分布图；A coordinate distribution map generation unit, which obtains the latitude and longitude coordinate information of each photovoltaic power station in the regional power grid, and generates a coordinate distribution map of the photovoltaic power station in the regional power grid;

气象信息获取单元，获取区域电网内每个光伏电站的气象信息；The meteorological information acquisition unit acquires the meteorological information of each photovoltaic power station in the regional power grid;

预测出力与实际出力差计算单元，计算区域电网内的每个光伏电站的预测出力与实际出力之差；The predicted output and actual output difference calculation unit calculates the difference between the predicted output and actual output of each photovoltaic power station in the regional power grid;

预测误差分布时序图建立单元，根据区域电网内的每个光伏电站的预测出力与实际出力之差，计算区域电网内光伏电站出力之间的时空相关性，建立预测误差分布时序图；The prediction error distribution time sequence diagram building unit calculates the time-space correlation between the output of the photovoltaic power station in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;

预测误差坐标标记单元，将光伏电站的预测误差标准化处理后，标记在坐标分布图中各光伏电站的相应位置上；The prediction error coordinate marking unit, after standardizing the prediction error of the photovoltaic power station, marks it on the corresponding position of each photovoltaic power station in the coordinate distribution diagram;

下一时刻预测误差估计单元，根据误差时间序列的相关性，计算出光伏出力误差移动的前后位置，即计算出误差的来源方向与距离，并估计出下一时刻的预测误差；The prediction error estimation unit at the next moment calculates the front and rear positions of the photovoltaic output error movement according to the correlation of the error time series, that is, calculates the source direction and distance of the error, and estimates the prediction error at the next moment;

预测误差相关系数计算单元，结合地理坐标信息及各光伏电站所在区域的气象信息，计算出光伏电站预测误差的相关系数；The prediction error correlation coefficient calculation unit combines the geographical coordinate information and the meteorological information of the area where each photovoltaic power station is located to calculate the correlation coefficient of the photovoltaic power station prediction error;

下一时刻预测误差修正单元，根据计算出的相关系数，修正下一时刻的预测误差。The next time prediction error correction unit corrects the next time prediction error based on the calculated correlation coefficient.

预测误差坐标标记单元中，预测误差Perr的计算式如下：In the prediction error coordinate marking unit, the calculation formula of the prediction error P err is as follows:

Perr＝(Ppre-Preal)/PN (1)P err ＝(P pre -P real )/P N (1)

式中，Ppre为预测功率，Preal为实测功率，PN为光伏电站的额定功率。In the formula, P pre is the predicted power, P real is the measured power, and P N is the rated power of the photovoltaic power station.

下一时刻预测误差估计单元中，下一时刻的预测误差为：In the prediction error estimation unit at the next moment, the prediction error at the next moment is:

P′err(x,y,t+Δt)＝Perr(x-vxΔt,y-vyΔt,t) (2)P' err (x,y,t+Δt)=P err (xv x Δt,yv y Δt,t) (2)

式中，Perr(x,y,t)表示t时刻的误差分布图中(x,y)位置处的预测误差，vx和vy分别表示风速在x和y方向上的分量。In the formula, P err (x, y, t) represents the prediction error at the position (x, y) in the error distribution diagram at time t, and v x and v y represent the components of wind speed in the x and y directions, respectively.

预测误差相关系数计算单元中，光伏电站预测误差的相关系数如下：In the prediction error correlation coefficient calculation unit, the correlation coefficient of the photovoltaic power plant prediction error is as follows:

式中，S1(t)为相关系数较低，表现为弱相关时的修正误差，此时修正误差保持不变S1(t)＝Perr(x,y,t)，其中x，y为待修正电站的位置；In the formula, S1(t) is the correction error when the correlation coefficient is low, which is shown as weak correlation. At this time, the correction error remains unchanged S1(t)=P err (x,y,t), where x and y are the Correct the location of the power station;

S2(t)为强相关时的修正误差，此时根据误差时间序列相关性最高点的位置的当前误差作为修正误差，S2(t)＝Perr(x1,y1,t-Δt)，x1，y1为误差时间序列相关性最高点的位置；S2(t) is the corrected error when there is strong correlation. At this time, the current error at the position of the highest correlation point of the error time series is used as the corrected error, S2(t)=P err (x 1 ,y 1 ,t-Δt), x 1 , y 1 is the position of the highest point of error time series correlation;

当相关系数介于弱相关与强相关之间时，则通过线性融合的方式来修正：When the correlation coefficient is between weak correlation and strong correlation, it is corrected by linear fusion:

α＝-2.5ρ+2 (4)α＝-2.5ρ+2 (4)

α是一个从1到0减小的变量，当ρ≤0.4时，则通过S1计算修正误差；随着相关系数提高，误差移动模式逐渐清晰，则提高S2分量的权重；当ρ≥0.8时，完全采用S2计算修正误差。α is a variable that decreases from 1 to 0. When ρ≤0.4, the correction error is calculated through S1; as the correlation coefficient increases and the error movement mode becomes clear, the weight of the S2 component is increased; when ρ≥0.8, The correction error is completely calculated using S2.

下一时刻预测误差修正单元中，下一时刻的预测误差的修正值如下：In the forecast error correction unit at the next moment, the correction value of the forecast error at the next moment is as follows:

P′pre(t+Δt)＝Ppre(t+Δt)+P′err(t+Δt)·PN (5)。P' pre (t+Δt)=P pre (t+Δt)+ P'err (t+Δt)·P N (5).

此处所描述的具体实施例仅用以解释发明的一部分实施例，而不是全部的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的其他实施例，都属于本发明保护的范围。The specific embodiments described here are only used to explain a part of the embodiments of the invention, rather than all the embodiments. Other embodiments obtained by those of ordinary skill in the art without creative work belong to the protection of the present invention. scope.

Claims (9)

1. The global accurate prediction method for the photovoltaic power generation output is characterized by comprising the following steps:
step 1), carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a global prediction value of the photovoltaic power generation output of the regional power grid;
step 2), acquiring longitude and latitude coordinate information of each photovoltaic power station in the regional power grid, and generating a coordinate distribution diagram of the photovoltaic power stations of the regional power grid;
step 3), weather information of each photovoltaic power station in the regional power grid is obtained;
step 4), calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
step 5), calculating the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishing a predicted error distribution timing diagram;
step 6), marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the standardization processing of the prediction errors of the photovoltaic power stations;
step 7), calculating the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculating the source direction and distance of the error, and estimating the prediction error of the next moment;
step 8), calculating the correlation coefficient of the photovoltaic power station prediction error by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
step 9), correcting the prediction error of the next moment in the step 7) according to the calculated correlation coefficient;
in step 8), the correlation coefficient of the photovoltaic power plant prediction error is as follows:
wherein P is e ′ rr (t+Δt) is the prediction error at the next time after correction, S1 (t) is the correction error at the time of weak correlation, where the correction error remains unchanged S1 (t) =p, with a low correlation coefficient err (x, y, t), wherein x, y is the position of the plant to be corrected; p (P) err (x, y, t) represents the prediction error at the (x, y) position in the error profile at time t;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the point of highest correlation of the error time series is regarded as the correction error, S2 (t) =P err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, S2 is completely adopted to calculate correction errors.
2. The method of claim 1, wherein the meteorological information in step 3) includes solar irradiance, wind speed, wind direction, cloud size, and cloud wind direction.
3. The global accurate prediction method of photovoltaic power generation output according to claim 1, wherein in step 6), the prediction error P is err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the photovoltaic power station.
4. The global accurate prediction method of photovoltaic power generation output according to claim 3, wherein in step 7), the prediction error at the next moment is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
in the formula, v x And v y Representing the components of wind speed in the x and y directions, respectively.
5. The global accurate prediction method of photovoltaic power generation output according to claim 3, wherein in step 9), the correction value of the prediction error at the next time is as follows:
P′ pre (t+Δt)＝P pre (t+Δt)+P′ err (t+Δt)·P N (5)。
6. photovoltaic power generation output global accurate prediction system, its characterized in that includes:
the photovoltaic power generation output global prediction unit is used for carrying out numerical accumulation on single power prediction results of each photovoltaic power station in the regional power grid to obtain a regional power grid photovoltaic power generation output global prediction value;
the coordinate distribution map generation unit is used for obtaining longitude and latitude coordinate information of each photovoltaic power station in the regional power grid and generating a coordinate distribution map of the photovoltaic power station of the regional power grid;
the meteorological information acquisition unit is used for acquiring meteorological information of each photovoltaic power station in the regional power grid;
the predicted output and actual output difference calculation unit is used for calculating the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid;
the prediction error distribution time sequence diagram establishing unit calculates the time-space correlation between the output of the photovoltaic power stations in the regional power grid according to the difference between the predicted output and the actual output of each photovoltaic power station in the regional power grid, and establishes a prediction error distribution time sequence diagram;
the prediction error coordinate marking unit is used for marking the corresponding positions of the photovoltaic power stations in the coordinate distribution diagram after the prediction error of the photovoltaic power stations is standardized;
the prediction error estimation unit at the next moment calculates the front and back positions of the photovoltaic output error movement according to the correlation of the error time sequence, namely calculates the source direction and distance of the error, and estimates the prediction error at the next moment;
the prediction error correlation coefficient calculation unit is used for calculating the correlation coefficient of the prediction error of the photovoltaic power station by combining the geographic coordinate information and the meteorological information of the area where each photovoltaic power station is located;
a prediction error correction unit for correcting the prediction error of the next time according to the calculated correlation coefficient;
in the prediction error correlation coefficient calculation unit, the correlation coefficient of the prediction error of the photovoltaic power station is as follows:
wherein P' err (t+Δt) is the prediction error at the next time after correction, S1 (t) is the correction error at the time of weak correlation, where the correction error remains unchanged S1 (t) =p, with a low correlation coefficient err (x, y, t), wherein x, y is the position of the plant to be corrected; p (P) err (x, y, t) represents the prediction error at the (x, y) position in the error profile at time t;
s2 (t) is a correction error in the case of strong correlation, and at this time, the current error based on the position of the highest point of correlation of the error time series is regarded as the correction error, S2 (t) =p err (x 1 ,y 1 ,t-Δt)，x 1 ，y 1 The position of the highest point of the error time sequence correlation;
when the correlation coefficient is between the weak correlation and the strong correlation, the correction is performed by a linear fusion mode:
α＝-2.5ρ+2 (4)
alpha is a variable which decreases from 1 to 0, and when ρ is less than or equal to 0.4, a correction error is calculated through S1; along with the improvement of the correlation coefficient, the error movement mode is gradually clear, and the weight of the S2 component is improved; when rho is more than or equal to 0.8, S2 is completely adopted to calculate correction errors.
7. The global accurate prediction system for photovoltaic power generation output according to claim 6, wherein in the prediction error coordinate marking unit, the prediction error P err The formula of (2) is as follows:
P err ＝(P pre -P real )/P N (1)
wherein P is pre To predict power, P real To actually measure power, P N Is the rated power of the photovoltaic power station.
8. The global accurate prediction system for photovoltaic power generation output according to claim 7, wherein in the prediction error estimation unit at the next time, the prediction error at the next time is:
P′ err (x,y,t+Δt)＝P err (x-v x Δt,y-v y Δt,t) (2)
in the formula, v x And v y Representing the components of wind speed in the x and y directions, respectively.
9. The global accurate prediction system for photovoltaic power generation output according to claim 7, wherein in the prediction error correction unit at the next time, the correction value of the prediction error at the next time is as follows:
P p ′ re (t+Δt)＝P pre (t+Δt)+P e ′ rr (t+Δt)·P N (5)。
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