CN114091316A - Regional photovoltaic power generation capacity calculation method - Google Patents

Regional photovoltaic power generation capacity calculation method Download PDF

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CN114091316A
CN114091316A CN202111354593.8A CN202111354593A CN114091316A CN 114091316 A CN114091316 A CN 114091316A CN 202111354593 A CN202111354593 A CN 202111354593A CN 114091316 A CN114091316 A CN 114091316A
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吕清泉
马明
张睿骁
张健美
陶钰磊
高鹏飞
张彦琪
张珍珍
李津
周强
王定美
张金平
朱红路
蒋婷婷
史淯城
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power Co Ltd
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Abstract

The invention relates to a regional photovoltaic power generation amount calculation method, which comprises the following steps: the method comprises the steps of collecting historical operation data and historical meteorological data of a photovoltaic power station; establishing a photovoltaic system operation temperature prediction model by utilizing a BP neural network optimized by a PSO particle swarm algorithm; a step ofCalculating instantaneous power of photovoltaic power stationP(ii) a Fourth, photovoltaic system efficiency is considered, and estimated generated energy of photovoltaic power station within a certain period of time in future is obtainedE. The method and the device predict the actual optimal working position of the photovoltaic system by using the irradiance data in the numerical weather forecast and the predicted operating temperature, thereby estimating the generated energy in a certain period of time in the future, not only providing a certain reference for the scheduling of the power system, but also avoiding the problems caused by the severe environment of the photovoltaic power station and the difficulty in collecting and transmitting the actual operating data of the system, and having a certain practical significance.

Description

Regional photovoltaic power generation capacity calculation method
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a regional photovoltaic power generation amount calculation method.
Background
In recent years, with the rapid development of photovoltaic power generation, the installed photovoltaic capacity is increasing day by day, and the task of supplying power by sharing the load of a photovoltaic power generation access power grid and a traditional power station is widely applied. The estimation and calculation of the generated energy of the photovoltaic power station in a certain period of time in the future plays an increasingly important role in grid-connected planning, operation and decision of the photovoltaic power station. Because photovoltaic power generation has obvious uncertainty and periodicity, when the photovoltaic power generation is connected to a power grid in a large scale, the photovoltaic power generation has great influence on safe operation and scheduling of the power grid. Therefore, the accurate estimation of the power generation amount of the photovoltaic power station can provide beneficial reference for the scheduling of the power system and the formulation of the power generation plan, and is also an important index for checking the economic benefit of the photovoltaic power station.
In a research stage of power station planning design, the estimation of the power generation capacity of a photovoltaic power station mainly calculates the power generation capacity through the annual average exposure, and a solar irradiance and photovoltaic power generation model is established to realize calculation. In the building operation stage of the power station, the calculation of the power generation capacity of the photovoltaic power station mainly comprises the steps of building a solar irradiance and photovoltaic power station power model according to a photovoltaic power generation mechanism and directly calculating the power generation capacity. Meanwhile, in the actual operation process of the photovoltaic power station, due to the problems of external environment and equipment, the accurate collection and transmission of the actual operation data of the system are difficult to achieve.
Disclosure of Invention
The invention aims to provide a regional photovoltaic power generation amount calculation method for effectively solving the problem of accurate collection and transmission of actual operation data.
In order to solve the above problems, the method for calculating the photovoltaic power generation capacity of the area, provided by the invention, comprises the following steps:
the method comprises the steps of collecting historical operation data and historical meteorological data of a photovoltaic power station;
establishing a photovoltaic system operation temperature prediction model by utilizing a BP neural network optimized by a PSO particle swarm optimization:
firstly, meteorological data including actually measured solar irradiance, ambient temperature, ambient humidity and wind speed are used as input, actually measured operating temperature is used as output, a BP neural network operating temperature prediction model is established by utilizing the BP neural network, and training is carried out;
inputting meteorological data in NWP numerical weather forecast into a trained BP neural network operation temperature prediction model to obtain a predicted operation temperature, comparing the predicted operation temperature with an actually measured value, and analyzing errors;
optimizing the threshold and the weight of the BP neural network by adopting a PSO particle swarm algorithm, gradually optimizing the parameters of a prediction model by taking the minimum prediction error as an objective function, and obtaining the optimal threshold and the optimal weight to obtain an optimized photovoltaic system operation temperature prediction model;
calculating the instantaneous power of the photovoltaic power stationP：
Figure RE-RE-DEST_PATH_IMAGE001
Wherein:
Figure RE-516699DEST_PATH_IMAGE002
；
Figure RE-RE-DEST_PATH_IMAGE003
；
Figure RE-510063DEST_PATH_IMAGE004
；
Figure RE-RE-DEST_PATH_IMAGE005
；
in the formula:
Figure RE-703541DEST_PATH_IMAGE006
for the actual optimum operating current, unit a;
Figure RE-RE-DEST_PATH_IMAGE007
for the actual optimum operating voltage, in units of V;nthe number of the photovoltaic modules is;I m the optimal working current under the standard condition of the photovoltaic cell is unit A;U m the optimal working voltage under the standard condition of the photovoltaic cell is unit A;Sis NWP irradiance data, unit W/m2；S ref Irradiance under standard conditions, unit W/m2；a，b，cIs a compensation coefficient;Tas predicted operating temperature, in units;T ref is the temperature under standard conditions, in units;etaking 2.718 as a natural logarithm base number;
fourth, photovoltaic system efficiency is considered, and estimated generated energy of photovoltaic power station within a certain period of time in future is obtainedE：
Figure RE-378236DEST_PATH_IMAGE008
；
In the formula:min parts per month;dthe number of days;η85.35% is taken for the comprehensive efficiency of photovoltaic power generation.
The photovoltaic power station historical operation data comprises power data and array operation temperature data in the actual operation process of the photovoltaic power station; the historical meteorological data refers to meteorological data comprising solar irradiance, ambient temperature, ambient humidity and wind speed and corresponding NWP numerical weather forecast data.
Compared with the prior art, the invention has the following advantages:
the method establishes a prediction model of the operation temperature of the photovoltaic system according to the weather forecast information of the geographic position of the photovoltaic power station, predicts the actual optimal working position of the photovoltaic system by using the irradiance data in the numerical weather forecast and the predicted operation temperature, thereby estimating the generated energy in a certain period of time in the future, not only providing a certain reference for the scheduling of the power system, but also avoiding the problems caused by the severe environment of the photovoltaic power station and the difficulty in collecting and transmitting the actual operation data of the system, and having a certain practical significance.
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The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a model for predicting the operating temperature of the BP neural network according to the present invention.
FIG. 3 is a graph of measured power versus calculated power for seven consecutive days in an embodiment of the present invention.
Detailed Description
As shown in fig. 1, a regional photovoltaic power generation amount calculation method includes the following steps:
the method comprises the steps of collecting historical operation data and historical meteorological data of a photovoltaic power station; the historical operation data of the photovoltaic power station comprises power data and array operation temperature data in the actual operation process of the photovoltaic power station; the historical meteorological data refers to meteorological data comprising solar irradiance, ambient temperature, ambient humidity and wind speed and corresponding NWP numerical weather forecast data.
Establishing a photovoltaic system operation temperature prediction model by utilizing a BP neural network optimized by a PSO particle swarm optimization:
firstly, meteorological data including actually measured solar irradiance, ambient temperature, ambient humidity and wind speed are used as input, the actually measured operating temperature is used as output, a BP neural network is utilized to establish a BP neural network operating temperature prediction model (shown in figure 2), and training is carried out;
wherein: output of BP neural networkyCan be expressed as:
Figure RE-RE-DEST_PATH_IMAGE009
in the formula:p i is as followsiAn input quantity;ithe number of output quantities;w i,j is as followsiFirst of input quantityjThe connection weight of each hidden layer node;ba threshold for a neuron in the network;fis a transfer function.
Inputting meteorological data in NWP numerical weather forecast into a trained BP neural network operation temperature prediction model to obtain a predicted operation temperature, comparing the predicted operation temperature with an actually measured value, and analyzing errors;
and thirdly, optimizing the threshold and the weight of the BP neural network by adopting a PSO particle swarm algorithm, gradually optimizing the parameters of the prediction model by taking the minimum prediction error as an objective function, and obtaining the optimal threshold and the weight so as to achieve a better effect, namely obtaining the optimized photovoltaic system operation temperature prediction model.
Wherein: and optimizing the threshold and the weight of the BP neural network according to the fitness function by utilizing a particle swarm algorithm. By comparing the fitness function of each iteration particle, the particle speed and position are updated to obtain the optimal threshold and weight。The velocity and position update formula for the particles is as follows:
Figure RE-839304DEST_PATH_IMAGE010
Figure RE-RE-DEST_PATH_IMAGE011
in the formula:
Figure RE-206831DEST_PATH_IMAGE012
are particlesiIn the first placekFirst in +1 iterationsdVelocity in dimension;
Figure RE-RE-DEST_PATH_IMAGE013
is the inertial weight;
Figure RE-822358DEST_PATH_IMAGE014
are particlesiIn the first placekIn the second iterationdVelocity in dimension;kis the iteration number;c 1、c 2is a learning factor;
Figure RE-RE-DEST_PATH_IMAGE015
、
Figure RE-35165DEST_PATH_IMAGE016
random numbers which are uniformly distributed in the (0,1) interval;Pbest id is a particle individualiAn optimal position;
Figure RE-RE-DEST_PATH_IMAGE017
are particlesiIn the first placekIn the second iterationdA position in a dimension;Gbest kd globally optimal position for the whole particle swarm;
Figure RE-678636DEST_PATH_IMAGE018
particlesiIn the first placekFirst in +1 iterationsdThe position in the dimension.
Calculating the instantaneous power of the photovoltaic power stationP：
Figure RE-951485DEST_PATH_IMAGE001
Wherein:
Figure RE-680407DEST_PATH_IMAGE002
；
Figure RE-759221DEST_PATH_IMAGE003
；
Figure RE-696347DEST_PATH_IMAGE004
；
Figure RE-467994DEST_PATH_IMAGE005
；
in the formula:
Figure RE-356315DEST_PATH_IMAGE006
for the actual optimum operating current, unit a;
Figure RE-238821DEST_PATH_IMAGE007
for the actual optimum operating voltage, in units of V;nthe number of the photovoltaic modules is;I m the optimal working current under the standard condition of the photovoltaic cell is unit A;U m the optimal working voltage under the standard condition of the photovoltaic cell is unit A;Sis NWP irradiance data, unit W/m2；S ref Irradiance under standard conditions, unit W/m2；a，b，cIs a compensation coefficient;Tas predicted operating temperature, in units;T ref is the temperature under standard conditions, in units;efor natural log base, take 2.718.
I m 、U m The parameters during actual operation are calculated from the operation temperature and the irradiance.
Fourth, photovoltaic system efficiency is considered, and estimated generated energy of photovoltaic power station within a certain period of time in future is obtainedE：
Figure RE-591304DEST_PATH_IMAGE008
；
In the formula:min parts per month;dthe number of days;ηthe comprehensive efficiency of photovoltaic power generation is achieved.
The embodiment takes annual data of a national key laboratory photovoltaic demonstration test power station of a new energy power system of North China Power university as an example, and the data sampling time is 15 minutes. The installed capacity of the test power station is 10KW, 3 string groups are formed by arranging photovoltaic modules with the model number of JKM245P-60-I and the rated power of 245W and the power of 3 multiplied by 13. Optimum operating voltageV m 30.2V, optimum working currentI m At 8.12A, open circuit voltageV oc 37.4V, short-circuit currentI sc It was 8.69A.
A regional photovoltaic power generation amount calculation method comprises the following steps:
the method comprises the steps of collecting historical operating data and historical meteorological data of the photovoltaic power station.
And establishing a photovoltaic system operation temperature prediction model by utilizing a BP neural network optimized by a PSO particle swarm optimization.
Calculating the instantaneous power of the photovoltaic power stationP：
Figure RE-RE-DEST_PATH_IMAGE019
Photovoltaic power station instantaneous powerPIn the calculation formula, the compensation coefficientaTaking the temperature of 0.0025/DEG C,btaking the temperature of 0.0005/DEG C,c0.00288/° C;T ref =25℃；S ref =1000W/m2(ii) a Number of componentsnIs 39. According to the solar irradiance data in the NWP numerical weather forecast of the test power stationSAnd step two, the predicted component operating temperature dataTAnd the instantaneous power of the photovoltaic power station can be calculatedP。
And selecting a calculation result of calculating for 7 continuous days, comparing the calculation result with the actually measured power, and making a comparison curve chart, such as a graph shown in figure 3, wherein the moment when the power is 0 is removed. As can be seen from fig. 3, the variation trends of the two are substantially the same, which illustrates that the method of the present invention can effectively estimate the instantaneous power of the photovoltaic power station.
Fourth, photovoltaic system efficiency is considered, and estimated generated energy of photovoltaic power station within a certain period of time in future is obtainedE：
Considering the influence factors of the comprehensive efficiency of the photovoltaic system, the losses of an alternating current and direct current power distribution room and a power transmission line are about 3% of the electric quantity, and a correction coefficient is 97%; the efficiency of the inverter is generally 95% -98%, and 95% is taken; the operating temperature loss, the efficiency of the photovoltaic cell, varies with the temperature at which it operates. Generally speaking, the working temperature loss is about 2.5%, and the correction coefficient is 97.5%; and uncertain factors such as solar radiation loss, maximum power point tracking precision influence, power grid absorption and the like, wherein the corresponding correction coefficient is 95%.
Thus, the overall efficiency of the photovoltaic system
Figure RE-471536DEST_PATH_IMAGE020
。
Figure RE-RE-DEST_PATH_IMAGE021
The calculation results of the power generation amount of the selected photovoltaic power station for 7 continuous days are shown in table 1:
TABLE 1 calculation of the amount of power generation for 7 consecutive days
Figure RE-847153DEST_PATH_IMAGE022
Wherein: the weather conditions are good in days 1, 2, 3 and 6, and the calculation result is close to the actual result; the weather conditions in days 4, 5 and 7 are worse, and the calculation result is slightly larger than the actual result, but the difference is smaller. The method has good precision in estimating the generating capacity of the photovoltaic power station only through meteorological conditions, avoids the problems caused by severe environment of the photovoltaic power station and difficulty in collecting and transmitting actual operation data of the system, and has certain practical significance.

Claims (2)

1. A regional photovoltaic power generation amount calculation method comprises the following steps:
the method comprises the steps of collecting historical operation data and historical meteorological data of a photovoltaic power station;
establishing a photovoltaic system operation temperature prediction model by utilizing a BP neural network optimized by a PSO particle swarm optimization:
firstly, meteorological data including actually measured solar irradiance, ambient temperature, ambient humidity and wind speed are used as input, actually measured operating temperature is used as output, a BP neural network operating temperature prediction model is established by utilizing the BP neural network, and training is carried out;
inputting meteorological data in NWP numerical weather forecast into a trained BP neural network operation temperature prediction model to obtain a predicted operation temperature, comparing the predicted operation temperature with an actually measured value, and analyzing errors;
optimizing the threshold and the weight of the BP neural network by adopting a PSO particle swarm algorithm, gradually optimizing the parameters of a prediction model by taking the minimum prediction error as an objective function, and obtaining the optimal threshold and the optimal weight to obtain an optimized photovoltaic system operation temperature prediction model;
calculating the instantaneous power of the photovoltaic power stationP：
Figure RE-RE-RE-DEST_PATH_IMAGE001
Wherein:
Figure RE-RE-99255DEST_PATH_IMAGE002
；
Figure RE-RE-RE-DEST_PATH_IMAGE003
；
Figure RE-RE-16395DEST_PATH_IMAGE004
；
Figure RE-RE-RE-DEST_PATH_IMAGE005
；
in the formula:
Figure RE-RE-223562DEST_PATH_IMAGE006
for the actual optimum operating current, unit a;
Figure RE-RE-RE-DEST_PATH_IMAGE007
for the actual optimum operating voltage, in units of V;nthe number of the photovoltaic modules is;I m the optimal working current under the standard condition of the photovoltaic cell is unit A;U m the optimal working voltage under the standard condition of the photovoltaic cell is unit A;Sis NWP irradiance data, unit W/m2；S ref Irradiance under standard conditions, unit W/m2；a，b，cIs a compensation coefficient;Tas predicted operating temperature, in units;T ref is the temperature under standard conditions, in units;etaking 2.718 as a natural logarithm base number;
fourth, photovoltaic system efficiency is considered and a certain future photovoltaic system efficiency is obtainedPhotovoltaic power station pre-estimated generated energy in period of timeE：
Figure RE-RE-618771DEST_PATH_IMAGE008
；
In the formula:min parts per month;dthe number of days;η85.35% is taken for the comprehensive efficiency of photovoltaic power generation.
2. The regional photovoltaic power generation amount calculation method according to claim 1, characterized by comprising: the photovoltaic power station historical operation data comprises power data and array operation temperature data in the actual operation process of the photovoltaic power station; the historical meteorological data refers to meteorological data comprising solar irradiance, ambient temperature, ambient humidity and wind speed and corresponding NWP numerical weather forecast data.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115333100A (en) * 2022-10-12 2022-11-11 四川中电启明星信息技术有限公司 Roof photovoltaic power generation power cooperative control method and system
CN116186852A (en) * 2023-02-14 2023-05-30 西南科技大学 Photovoltaic-greening roof design method and system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105184678A (en) * 2015-09-18 2015-12-23 齐齐哈尔大学 Method for constructing photovoltaic power station generation capacity short-term prediction model based on multiple neural network combinational algorithms
CN106156455A (en) * 2015-03-26 2016-11-23 中国能源建设集团新疆电力设计院有限公司 A kind of photovoltaic generating system generated energy computational methods based on all the period of time analog integration
CN110909310A (en) * 2019-11-26 2020-03-24 广州地铁设计研究院股份有限公司 Photovoltaic short-term power generation capacity prediction method and system based on model parameter optimization
KR20200057821A (en) * 2018-11-13 2020-05-27 주식회사 에코시안 solar photovoltatic power generation forecasting apparatus and method based on big data analysis
CN111210095A (en) * 2020-03-12 2020-05-29 深圳前海微众银行股份有限公司 Power generation amount prediction method, device, equipment and computer readable storage medium
CN111353653A (en) * 2020-03-13 2020-06-30 大连理工大学 Photovoltaic output short-term interval prediction method
CN113469426A (en) * 2021-06-23 2021-10-01 国网山东省电力公司东营供电公司 Photovoltaic output power prediction method and system based on improved BP neural network

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106156455A (en) * 2015-03-26 2016-11-23 中国能源建设集团新疆电力设计院有限公司 A kind of photovoltaic generating system generated energy computational methods based on all the period of time analog integration
CN105184678A (en) * 2015-09-18 2015-12-23 齐齐哈尔大学 Method for constructing photovoltaic power station generation capacity short-term prediction model based on multiple neural network combinational algorithms
KR20200057821A (en) * 2018-11-13 2020-05-27 주식회사 에코시안 solar photovoltatic power generation forecasting apparatus and method based on big data analysis
CN110909310A (en) * 2019-11-26 2020-03-24 广州地铁设计研究院股份有限公司 Photovoltaic short-term power generation capacity prediction method and system based on model parameter optimization
CN111210095A (en) * 2020-03-12 2020-05-29 深圳前海微众银行股份有限公司 Power generation amount prediction method, device, equipment and computer readable storage medium
CN111353653A (en) * 2020-03-13 2020-06-30 大连理工大学 Photovoltaic output short-term interval prediction method
CN113469426A (en) * 2021-06-23 2021-10-01 国网山东省电力公司东营供电公司 Photovoltaic output power prediction method and system based on improved BP neural network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张佳伟;张自嘉;: "基于PSO-BP神经网络的短期光伏系统发电预测", 可再生能源, no. 08, 20 August 2012 (2012-08-20), pages 33 - 37 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115333100A (en) * 2022-10-12 2022-11-11 四川中电启明星信息技术有限公司 Roof photovoltaic power generation power cooperative control method and system
CN115333100B (en) * 2022-10-12 2022-12-16 四川中电启明星信息技术有限公司 Roof photovoltaic power generation power cooperative control method and system
CN116186852A (en) * 2023-02-14 2023-05-30 西南科技大学 Photovoltaic-greening roof design method and system
CN116186852B (en) * 2023-02-14 2023-09-26 西南科技大学 Photovoltaic-greening roof design method and system

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