CN103208029A - Super-short-term power prediction method based on clearance model for photovoltaic power station - Google Patents
Super-short-term power prediction method based on clearance model for photovoltaic power station Download PDFInfo
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Abstract
The invention provides a super-short-term power prediction method based on a clearance model for a photovoltaic power station. The method comprises the following steps: data preprocessing; model training; and super-short-term power prediction for a photovoltaic power station according to the model. According to the super-short-term power prediction method based on the clearance model, the clearance model of the photovoltaic power station by combination of the clearance model of solar irradiance with output characteristics of the photovoltaic power station, the theoretical maximum output of the photovoltaic power station at specific time can be calculated, and the super-short-term power prediction model is established by employing normalization data and combining with autoregression time sequences on the basis of the theoretical maximum output, accordingly, the prediction accuracy is effectively improved.
Description
Technical field
The invention belongs to photovoltaic and be transported to electro-technical field, be specifically related to the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model.
Background technology
Different with conventional power supply, the output power of photovoltaic plant has undulatory property and intermittent characteristics.The photovoltaic plant output power is predicted the Real-Time Scheduling operation of photovoltaic generation being included in electrical network is to guarantee one of important measures of stabilization of power grids economical operation.
Ultrashort phase power prediction is mainly the real-time power network scheduling foundation is provided, and dynamically updates the power prediction result to improve precision of prediction.The time parameter of ultrashort phase power prediction requires: the active power of (1) rolling forecast output in following 0-4 hour, upgraded once in per 15 minutes; (2) temporal resolution is 15 minutes.
At present, less to the research of the ultrashort phase power prediction of solar energy power generating.The method that adopts is that cloud atlas is monitored the cloud layer in the photovoltaic plant sky via satellite at present, dynamically variation and the operation of identification cloud layer, and then dope the ground solar irradiance, predict the generating situation of photovoltaic plant then according to forecast model, this method needs a large amount of real-time meteorological datas as foundation still in conceptual phase; In addition, according to the continuation of atmosphere in the short time, adopt Time Series Method, according to historical power data and real power data, the rule of mining data itself, real-time carries out rolling forecast to following power.Said method can be realized the ultrashort phase power prediction of photovoltaic plant to a certain extent, but does not consider that photovoltaic plant is in concrete theoretical maximum output constantly.
Summary of the invention
For overcoming above-mentioned defective, the invention provides the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model, can calculate photovoltaic plant in concrete theoretical maximum output constantly in conjunction with the headroom model of solar irradiance and the photovoltaic plant headroom model of photovoltaic plant power producing characteristics acquisition, on the basis of theoretical maximum output, the ultrashort phase power prediction model that adopts normalization data and set up in conjunction with auto-regressive time series effectively improves prediction accuracy.
For achieving the above object, the invention provides the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model, its improvements are that described method comprises the steps:
(1). the data pre-service;
(2). model training;
(3). according to model, the ultrashort phase power of prediction photovoltaic plant.
In the optimal technical scheme provided by the invention, described step 1 comprises the steps:
(1-1). the historical active power data in collection photovoltaics power station;
(1-2). historical active power data are done analysis and arrangement, obtain historical active power data set P.
In second optimal technical scheme provided by the invention, in described step 1-1, described historical active power data comprise: 1 year complete historical data.
In the 3rd optimal technical scheme provided by the invention, in described step 1-2, by being done analysis and arrangement, historical active power data obtain misdata and missing data.
In the 4th optimal technical scheme provided by the invention, misdata is carried out deletion action.
In the 5th optimal technical scheme provided by the invention, with the mode that adjacent data replaces missing data is carried out completion.
In the 6th optimal technical scheme provided by the invention, described step 2 comprises the steps:
(2-1). calculate at data set P place photovoltaic plant in the time period every 15 minutes theoretical maximum output according to the headroom model of photovoltaic plant, obtain theoretical maximum output data set PT;
(2-2). calculate P and the ratio of PT at corresponding moment data value, obtain normalized historical active power data PN;
(2-3). PN is set up the auto-regressive time series model M.
In the 7th optimal technical scheme provided by the invention, described step 2-3 comprises the steps:
(a). data are carried out stationary test;
(b). model is decided rank;
(c). model is carried out parameter estimation.
In the 8th optimal technical scheme provided by the invention, in described step a, the historical active power data of the normalization of photovoltaic plant are tested and handled, guarantee that data are stably.
In the 9th optimal technical scheme provided by the invention, in described step b, calculate auto-correlation and the partial correlation coefficient of the historical active power data of photovoltaic plant normalization, determine the exponent number of auto-regressive time series model according to result of calculation.
In the tenth optimal technical scheme provided by the invention, in described step c, adopt least square method to be obtained from the parameter of regression time series model.
In the more preferably technical scheme provided by the invention, described step 3 comprises the steps:
(3-1). the headroom model according to photovoltaic plant calculates the theoretical maximum output of photovoltaic plant in certain period;
(3-2). calculate and obtain the historical active power normalized value of a period of time in being engraved in when comprising prediction;
(3-3). with historical active power normalized value input M, obtain the normalized predicted value T of active power of ultrashort phase;
(3-4). T exerted oneself with corresponding theory constantly multiply each other, obtain power prediction result of ultrashort phase of photovoltaic plant.
Provided by the invention second more preferably in the technical scheme, in described step 3-1, calculates in prediction first three day, predicts the same day and predict in the back three days period theoretical maximum output every 15 minutes.
The provided by the invention the 3rd more preferably in the technical scheme, in described step 3-1, the headroom model of photovoltaic plant is: based on the headroom model, utilize the opto-electronic conversion model of photovoltaic plant, adopt a kind of photovoltaic plant that calculates of data fitting method foundation at the concrete model of the theoretical maximum active power of output constantly.
The provided by the invention the 4th more preferably in the technical scheme, in described step 3-3, obtains the normalized predicted value T of active power in following 4 hours.
The provided by the invention the 5th more preferably in the technical scheme, and the value of predicted value T is spaced apart: carry out value between 0 to 4 hour and every 15 minutes.
Compared with the prior art, the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model provided by the invention will be based on the theoretical maximum output of the photovoltaic plant of headroom model as the important data source of the ultrashort phase power prediction of photovoltaic plant; And utilize historical real power with the ratio of theoretical maximum output constantly as the seasonal effect in time series training sample.
Description of drawings
Fig. 1 is the schematic flow sheet based on the ultrashort phase power forecasting method of the photovoltaic plant of headroom model.
Fig. 2 exerts oneself and actual synoptic diagram of exerting oneself every 15 minutes theories for photovoltaic plant one day.
Embodiment
The headroom model
The instantaneous intensity of solar radiation in section, exoatmosphere is only relevant with intensity of solar radiation and the solar radiation direction of aeropause, and these can obtain by the relevant formula accurate Calculation of uranology.Suppose under the situation that fine, no cloud layer block, calculate and set up the relational expression model between the instantaneous intensity of solar radiation in instantaneous intensity of solar radiation near the ground and section, exoatmosphere, just can extrapolate the theoretical maximum instantaneous solar radiation near the ground of the concrete moment in real time according to this model, this model is the headroom model.
The headroom model of photovoltaic plant
Based on the headroom model, utilize the opto-electronic conversion model of photovoltaic plant, adopt the method for data fitting to set up a kind of photovoltaic plant that can calculate at the concrete model of the theoretical maximum active power of output constantly.
Time Series Method
Time Series Method is that applied statistical method is set up corresponding mathematics model, comes predicted data development in future trend according to the time series data that observes.Time series analysis is one of quantitative forecasting technique, its ultimate principle: one is to recognize that the continuity of things development, utilizes past data to infer the development trend of things; The 2nd, the randomness of consideration things development.
As shown in Figure 1, the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model, can calculate photovoltaic plant in concrete theoretical maximum output constantly in conjunction with the headroom model of solar irradiance and the photovoltaic plant headroom model of photovoltaic plant power producing characteristics acquisition, on the basis of theoretical maximum output, the ultrashort phase power prediction model that adopts normalization data and set up in conjunction with auto-regressive time series effectively improves prediction accuracy.
For achieving the above object, the invention provides the ultrashort phase power forecasting method of a kind of photovoltaic plant based on the headroom model, comprise the steps:
(1). the data pre-service;
(2). model training;
(3). according to model, the ultrashort phase power of prediction photovoltaic plant.
Described step 1 comprises the steps:
(1-1). the historical active power data in collection photovoltaics power station;
(1-2). historical active power data are done analysis and arrangement, obtain historical active power data set P.
In described step 1-1, described historical active power data comprise: 1 year complete historical data.
In described step 1-2, by being done analysis and arrangement, historical active power data obtain misdata and missing data.
Misdata is carried out deletion action.
With the mode that adjacent data replaces missing data is carried out completion.
Described step 2 comprises the steps:
(2-1). calculate at data set P place photovoltaic plant in the time period every 15 minutes theoretical maximum output according to the headroom model of photovoltaic plant, obtain theoretical maximum output data set PT;
(2-2). calculate P and the ratio of PT at corresponding moment data value, obtain normalized historical active power data PN;
(2-3). PN is set up the auto-regressive time series model M.
Described step 2-3 comprises the steps:
(a). data are carried out stationary test;
(b). model is decided rank;
(c). model is carried out parameter estimation.
In described step a, the historical active power data of the normalization of photovoltaic plant are tested and handled, guarantee that data are stably.
In described step b, calculate auto-correlation and the partial correlation coefficient of the historical active power data of photovoltaic plant normalization, determine the exponent number of auto-regressive time series model according to result of calculation.
In described step c, adopt least square method to be obtained from the parameter of regression time series model.
Described step 3 comprises the steps:
(3-1). the headroom model according to photovoltaic plant calculates the theoretical maximum output of photovoltaic plant in certain period;
(3-2). calculate and obtain the historical active power normalized value of a period of time in being engraved in when comprising prediction;
(3-3). with historical active power normalized value input M, obtain the normalized predicted value T of active power of ultrashort phase;
(3-4). T exerted oneself with corresponding theory constantly multiply each other, obtain power prediction result of ultrashort phase of photovoltaic plant.
In described step 3-1, calculate in prediction first three day, predict the same day and predict in the back three days period theoretical maximum output every 15 minutes.
In described step 3-1, the headroom model of photovoltaic plant is: based on the headroom model, utilize the opto-electronic conversion model of photovoltaic plant, adopt a kind of photovoltaic plant that calculates of data fitting method foundation at the concrete model of the theoretical maximum active power of output constantly.
In described step 3-3, obtain the normalized predicted value T of active power in following 4 hours.
The value of predicted value T is spaced apart: carry out value between 0 to 4 hour and every 15 minutes.
What need statement is that content of the present invention and embodiment are intended to prove the practical application of technical scheme provided by the present invention, should not be construed as the restriction to protection domain of the present invention.Those skilled in the art can do various modifications, be equal to and replace or improve inspired by the spirit and principles of the present invention.But these changes or modification are all in the protection domain that application is awaited the reply.
Claims (16)
1. the ultrashort phase power forecasting method of the photovoltaic plant based on the headroom model is characterized in that described method comprises the steps:
(1). the data pre-service;
(2). model training;
(3). according to model, the ultrashort phase power of prediction photovoltaic plant.
2. method according to claim 1 is characterized in that, described step 1 comprises the steps:
(1-1). the historical active power data in collection photovoltaics power station;
(1-2). historical active power data are done analysis and arrangement, obtain historical active power data set P.
3. method according to claim 2 is characterized in that, in described step 1-1, described historical active power data comprise: 1 year complete historical data.
4. method according to claim 2 is characterized in that, in described step 1-2, obtains misdata and missing data by historical active power data are done analysis and arrangement.
5. method according to claim 4 is characterized in that, misdata is carried out deletion action.
6. method according to claim 4 is characterized in that, with the mode that adjacent data replaces missing data is carried out completion.
7. method according to claim 1 is characterized in that, described step 2 comprises the steps:
(2-1). calculate at data set P place photovoltaic plant in the time period every 15 minutes theoretical maximum output according to the headroom model of photovoltaic plant, obtain theoretical maximum output data set PT;
(2-2). calculate P and the ratio of PT at corresponding moment data value, obtain normalized historical active power data PN;
(2-3). PN is set up the auto-regressive time series model M.
8. method according to claim 7 is characterized in that, described step 2-3 comprises the steps:
(a). data are carried out stationary test;
(b). model is decided rank;
(c). model is carried out parameter estimation.
9. method according to claim 8 is characterized in that, in described step a, the historical active power data of the normalization of photovoltaic plant is tested and is handled, and guarantees that data are stably.
10. method according to claim 8 is characterized in that, in described step b, calculates auto-correlation and the partial correlation coefficient of the historical active power data of photovoltaic plant normalization, determines the exponent number of auto-regressive time series model according to result of calculation.
11. method according to claim 8 is characterized in that, in described step c, adopts least square method to be obtained from the parameter of regression time series model.
12. method according to claim 1 is characterized in that, described step 3 comprises the steps:
(3-1). the headroom model according to photovoltaic plant calculates the theoretical maximum output of photovoltaic plant in certain period;
(3-2). calculate and obtain the historical active power normalized value of a period of time in being engraved in when comprising prediction;
(3-3). with historical active power normalized value input M, obtain the normalized predicted value T of active power of ultrashort phase;
(3-4). T exerted oneself with corresponding theory constantly multiply each other, obtain power prediction result of ultrashort phase of photovoltaic plant.
13. method according to claim 12 is characterized in that, in described step 3-1, calculates in prediction first three day, predicts the same day and predict in the back three days period theoretical maximum output every 15 minutes.
14. method according to claim 12, it is characterized in that, in described step 3-1, the headroom model of photovoltaic plant is: based on the headroom model, utilize the opto-electronic conversion model of photovoltaic plant, adopt a kind of photovoltaic plant that calculates of data fitting method foundation at the concrete model of the theoretical maximum active power of output constantly.
15. method according to claim 12 is characterized in that, in described step 3-3, obtains the normalized predicted value T of active power in following 4 hours.
16. method according to claim 15 is characterized in that, the value of predicted value T is spaced apart: carry out value between 0 to 4 hour and every 15 minutes.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016120502A1 (en) * | 2015-01-30 | 2016-08-04 | Universidad Publica De Navarra | Method and system for emulating the power generated by a set of distributed photovoltaic systems |
| CN105956713A (en) * | 2016-05-18 | 2016-09-21 | 中国电力科学研究院 | New energy annual/monthly electric quantity plan making method |
| CN106709585A (en) * | 2015-07-20 | 2017-05-24 | 周锡卫 | System for realizing ultra-short-term photovoltaic power prediction by using real-time shooting |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769788A (en) * | 2009-12-29 | 2010-07-07 | 青海国泰节能技术研究院 | Method for forecasting optical output power and electric energy production of photovoltaic power station |
| CN101969207A (en) * | 2010-09-16 | 2011-02-09 | 国网电力科学研究院 | Photovoltaic ultra-short term power predicting method based on satellite remote sensing and meteorology telemetry technology |
| CN102281016A (en) * | 2011-06-09 | 2011-12-14 | 国网电力科学研究院 | Clear-sky photovoltaic super-short-term power forecast method based on real-time radiation acquisition technology |
-
2013
- 2013-03-11 CN CN201310076634.0A patent/CN103208029B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101769788A (en) * | 2009-12-29 | 2010-07-07 | 青海国泰节能技术研究院 | Method for forecasting optical output power and electric energy production of photovoltaic power station |
| CN101969207A (en) * | 2010-09-16 | 2011-02-09 | 国网电力科学研究院 | Photovoltaic ultra-short term power predicting method based on satellite remote sensing and meteorology telemetry technology |
| CN102281016A (en) * | 2011-06-09 | 2011-12-14 | 国网电力科学研究院 | Clear-sky photovoltaic super-short-term power forecast method based on real-time radiation acquisition technology |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016120502A1 (en) * | 2015-01-30 | 2016-08-04 | Universidad Publica De Navarra | Method and system for emulating the power generated by a set of distributed photovoltaic systems |
| CN106709585A (en) * | 2015-07-20 | 2017-05-24 | 周锡卫 | System for realizing ultra-short-term photovoltaic power prediction by using real-time shooting |
| CN106897784A (en) * | 2015-12-18 | 2017-06-27 | 中国电力科学研究院 | A kind of distributed photovoltaic power generation power forecasting method based on line modeling |
| WO2017125006A1 (en) * | 2016-01-19 | 2017-07-27 | 李元辉 | Rhythm controllable method of electronic musical instrument, and improvement of karaoke thereof |
| CN105956713A (en) * | 2016-05-18 | 2016-09-21 | 中国电力科学研究院 | New energy annual/monthly electric quantity plan making method |
| CN108388956A (en) * | 2018-01-18 | 2018-08-10 | 华北电力大学 | Consider the photovoltaic power prediction technique of attenuation |
| CN109767353A (en) * | 2019-01-14 | 2019-05-17 | 国网江苏省电力有限公司苏州供电分公司 | A kind of photovoltaic power generation power prediction method based on probability-distribution function |
| CN109767353B (en) * | 2019-01-14 | 2020-12-18 | 国网江苏省电力有限公司苏州供电分公司 | Photovoltaic power generation power prediction method based on probability distribution function |
| CN110265996A (en) * | 2019-02-26 | 2019-09-20 | 国网吉林省电力有限公司 | It is a kind of suitable for photovoltaic/wind power prediction time characteristic scale modeling method |
| CN110265996B (en) * | 2019-02-26 | 2023-04-07 | 国网吉林省电力有限公司 | Time characteristic scale modeling method suitable for photovoltaic/wind power prediction |
| CN110929845A (en) * | 2019-11-12 | 2020-03-27 | 北京明略软件系统有限公司 | Model training method and device, and method and device for estimating radiation intensity |
| CN111414582A (en) * | 2020-03-12 | 2020-07-14 | 广西电网有限责任公司 | Photovoltaic theoretical power calculation method, device, equipment and storage medium |
| CN112700050A (en) * | 2021-01-04 | 2021-04-23 | 南京南瑞继保电气有限公司 | Method and system for predicting ultra-short-term 1 st point power of photovoltaic power station |
| CN112700050B (en) * | 2021-01-04 | 2022-07-22 | 南京南瑞继保电气有限公司 | Method and system for predicting ultra-short-term 1 st point power of photovoltaic power station |
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