CN110705770A - Photovoltaic power prediction optimization method and device for photovoltaic power station - Google Patents

Photovoltaic power prediction optimization method and device for photovoltaic power station Download PDF

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CN110705770A
CN110705770A CN201910916344.XA CN201910916344A CN110705770A CN 110705770 A CN110705770 A CN 110705770A CN 201910916344 A CN201910916344 A CN 201910916344A CN 110705770 A CN110705770 A CN 110705770A
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prediction
photovoltaic power
data
power
station
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CN110705770B (en
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江长明
柳玉
杨健
牛四清
李丹
谢旭
邓立
喻乐
郭万舒
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
North China Grid Co Ltd
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
North China Grid Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector

Abstract

The invention provides a photovoltaic power prediction optimization method and device for a photovoltaic power station, wherein the method comprises the following steps: acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station; acquiring a total photovoltaic power prediction error according to the prediction data and the measured data; obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data; obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data; and each key link of photovoltaic power prediction is optimized according to errors caused by each key link, so that the power prediction precision can be effectively improved.

Description

Photovoltaic power prediction optimization method and device for photovoltaic power station
Technical Field
The invention relates to the technical field of new energy power generation control, in particular to a photovoltaic power prediction optimization method and device for a photovoltaic power station.
Background
Solar energy is an intermittent, random and fluctuating natural resource, and when the permeability of the solar energy exceeds a certain proportion, the safe operation of a power system can be seriously influenced. The photovoltaic power prediction of a photovoltaic power station is a core technology for guaranteeing safe and reliable operation of a high-proportion new energy power system, and a power grid dispatching department makes dispatching plans of various power sources according to the predicted photovoltaic power, namely, the photovoltaic power generation is brought into a conventional power generation plan so as to better manage and utilize the photovoltaic power generation, so that the photovoltaic power prediction precision is directly related to the problems of power grid peak regulation, unit combination, unit economic operation and the like.
However, the current prediction level of photovoltaic power generation output cannot meet the actual operation requirement of a power system; in order to improve the photovoltaic power prediction precision, factors influencing photovoltaic power prediction can be analyzed by evaluating photovoltaic power prediction errors, and photovoltaic power prediction is adjusted according to the influencing factors, so that the photovoltaic power prediction precision is improved.
The existing photovoltaic power prediction error evaluation technology is mainly used for carrying out a macroscopic overall result based on a prediction result and an actual result, errors caused in each key link of photovoltaic power prediction cannot be analyzed quantitatively, corresponding optimization measures cannot be carried out in a targeted mode, and improvement of power prediction precision is not facilitated.
Disclosure of Invention
The invention provides a photovoltaic power prediction optimization method, a photovoltaic power prediction optimization device, an electronic device and a computer-readable storage medium for a photovoltaic power station, which can at least partially solve the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a method for predictive optimization of photovoltaic power for a photovoltaic power plant is provided, including:
acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station;
acquiring a total photovoltaic power prediction error according to the prediction data and the measured data;
obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data;
obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
Further, the prediction data comprises: the station predicts the power, and the measured data comprises: actual power of the station;
the obtaining of the total photovoltaic power prediction error according to the prediction data and the measured data includes:
and subtracting the actual power of the station from the predicted power of the station to obtain the total predicted error of the photovoltaic power.
Further, the prediction data comprises: station predicted power; the measured data includes: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;
the obtaining equivalent prediction data according to the optical-to-electrical conversion model, the measured data and the operating data includes:
calculating equivalent predicted power under the condition of accurate starting capacity according to the station predicted power, the planned starting capacity and the actual starting capacity;
and obtaining equivalent prediction power under an accurate optical resource condition according to the actually measured meteorological data of the station, the optical-electrical conversion model, the planned starting-up capacity and the rated installed capacity.
Further, the calculating the equivalent predicted power under the condition of the accurate boot capacity according to the station predicted power, the planned boot capacity and the actual boot capacity includes:
and multiplying the quotient of dividing the actual starting capacity by the planned starting capacity by the station predicted power to obtain the equivalent predicted power under the condition of accurate starting capacity.
Further, the obtaining of the equivalent predicted power under the accurate light resource condition according to the station actually measured meteorological data, the optical-electrical conversion model, the planned startup capacity, and the rated installed capacity includes:
inputting the actually measured meteorological data of the station into the photoelectric conversion model to obtain predicted generated energy;
and multiplying the quotient of dividing the planned starting capacity by the rated installed capacity by the predicted power generation amount to obtain the equivalent predicted power under the accurate optical resource condition.
Further, the key links include: a numerical weather forecasting link, a photoelectric conversion model link and a correction link;
the obtaining of the error caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data comprises:
obtaining errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
obtaining errors caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;
and subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total photovoltaic power prediction error to obtain the error caused by the optical-electrical conversion model link.
In a second aspect, a photovoltaic power prediction optimization apparatus for a photovoltaic power plant is provided, including:
the data acquisition module is used for acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of the photovoltaic power station;
the photovoltaic power prediction total error acquisition module is used for acquiring a photovoltaic power prediction total error according to the prediction data and the measured data;
the equivalent prediction data acquisition module is used for acquiring equivalent prediction data according to the photoelectric conversion model, the measured data and the operating data;
the error decoupling evaluation module is used for obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and the prediction optimization module optimizes each key link of photovoltaic power prediction according to errors caused by each key link.
Further, the prediction data comprises: the station predicts the power, and the measured data comprises: actual power of the station;
the photovoltaic power prediction total error obtaining module comprises:
and the photovoltaic power prediction total error obtaining unit is used for obtaining the photovoltaic power prediction total error by subtracting the actual power of the station from the station prediction power.
Further, the prediction data comprises: station predicted power; the measured data includes: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;
the equivalent prediction data acquisition module comprises:
the first equivalent prediction power obtaining unit is used for calculating equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity;
and the second equivalent prediction power acquisition unit is used for acquiring equivalent prediction power under an accurate optical resource condition according to the actually measured meteorological data of the station, the optical-electrical conversion model, the planned startup capacity and the rated installed capacity.
Further, the first equivalent prediction power obtaining unit includes:
and the first calculation subunit multiplies the station predicted power by the quotient of dividing the actual startup capacity by the planned startup capacity to obtain the equivalent predicted power under the condition of accurate startup capacity.
Further, the second equivalent prediction power obtaining unit includes:
the predicted power generation amount obtaining subunit inputs the actually measured meteorological data of the station into the optical-electric conversion model to obtain predicted power generation amount;
and the second calculating subunit multiplies the predicted power generation amount by the quotient of dividing the planned starting capacity by the rated installed capacity to obtain equivalent predicted power under the accurate light resource condition.
Further, the key links include: a numerical weather forecasting link, a photoelectric conversion model link and a correction link;
the error decoupling evaluation module comprises:
the numerical weather forecast link error evaluation unit is used for obtaining errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
the correction link error evaluation unit is used for obtaining errors caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;
and the photoelectric conversion model link error evaluation unit subtracts the error caused by the numerical weather forecast link and the error caused by the correction link according to the total photovoltaic power prediction error to obtain the error caused by the photoelectric conversion model link.
In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above-mentioned photovoltaic power prediction optimization method for a photovoltaic power plant when executing the program.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method for predictive optimization of photovoltaic power for a photovoltaic power plant.
The invention provides a photovoltaic power prediction optimization method, a photovoltaic power prediction optimization device, electronic equipment and a computer-readable storage medium for a photovoltaic power station, wherein the method comprises the following steps: acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station; acquiring a total photovoltaic power prediction error according to the prediction data and the measured data; obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data; obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data; optimizing each key link of photovoltaic power prediction according to errors caused by each key link, wherein the errors of each key link of power prediction are quantitatively and finely analyzed by utilizing multi-source data such as a photo-electric conversion model, prediction data, actual measurement data, operation data and the like, further accurately positioning the weak prediction link of the field station with poor prediction level, purposefully optimizing each key link of photovoltaic power prediction according to the errors caused by each key link, and developing corresponding optimization measures in a targeted manner to efficiently improve the power prediction level of the photovoltaic power station.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. In the drawings:
FIG. 1 is a schematic flow diagram of a method for predictive optimization of photovoltaic power for a photovoltaic power plant in an embodiment of the invention;
FIG. 2 illustrates the main steps of power prediction of a photovoltaic power plant in an embodiment of the present invention;
FIG. 3 shows a schematic representation of a NWP product production flow diagram in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a statistical model of optical-to-electrical conversion in an embodiment of the invention;
fig. 5 shows the specific steps of step S300 in fig. 1;
fig. 6 shows the specific steps of step S400 in fig. 1;
FIG. 7 illustrates a schematic diagram of photovoltaic power prediction optimization in an embodiment of the present invention;
fig. 8 is a block diagram of a photovoltaic power prediction optimization apparatus for a photovoltaic power plant according to an embodiment of the present invention;
fig. 9 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The existing photovoltaic power prediction error evaluation technology is mainly used for carrying out a macroscopic overall result based on a prediction result and an actual result, errors caused in each key link of photovoltaic power prediction cannot be analyzed quantitatively, corresponding optimization measures cannot be carried out in a targeted mode, and improvement of power prediction precision is not facilitated.
In order to at least partially solve the technical problems in the prior art, the embodiment of the invention provides a photovoltaic power prediction optimization method for a photovoltaic power station, which utilizes multi-source data such as a light-electricity conversion model, prediction data, measured data, operation data and the like to quantitatively and finely analyze errors of each key link of power prediction, further accurately positions weak prediction links of a field station with a poor prediction level, purposefully optimizes each key link of photovoltaic power prediction according to errors caused by each key link, and develops corresponding optimization measures in a targeted manner to efficiently improve the photovoltaic power prediction level of the photovoltaic power station.
Fig. 1 is a schematic flow chart of a photovoltaic power prediction optimization method for a photovoltaic power plant in an embodiment of the present invention. As shown in fig. 1, the photovoltaic power prediction optimization method for a photovoltaic power plant may include the following:
step S100: and acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of the photovoltaic power station.
Wherein, the optical-electric conversion model can be provided by a power forecasting manufacturer.
The prediction data includes: station predicted power, etc. The prediction process of the station predicted power sequentially comprises three key links of numerical weather forecast, light-electric power conversion and prediction result correction according to a business process.
Specifically, referring to fig. 2, firstly, a numerical weather forecast product is generated by using a mesoscale meteorological model for a global initial field; then, inputting meteorological elements related to optical resources in the numerical weather forecast product into an optical-electric conversion model to obtain predicted power generation amount; and finally, correcting the prediction result according to operation information such as a unit maintenance plan and the like to obtain the station prediction power.
The Numerical Weather forecast (hereinafter referred to as NWP) is a method for predicting the atmospheric motion state and Weather phenomenon in a certain period of time by performing Numerical calculation through a large-scale computer under the condition of certain initial value and side value according to the actual atmospheric condition and solving the fluid mechanics and thermodynamics equation system describing the Weather evolution process.
Station NWP belongs to station refined forecast, the production flow is shown in figure 3, firstly, a global weather forecast station is downloaded from a Towei weather institution; then, carrying out data format standardization processing on the global atmospheric forecast field to enable the global atmospheric forecast field to have conditions for driving mesoscale numerical weather forecast mode software to run, and completing all preparation work before mode running; and finally, the power prediction service provider operates mesoscale numerical weather mode software according to the prediction requirements of the specific geographic coordinates of the power prediction service provider to complete downscaling calculation of the local target area, and finally, the atmospheric states of the geographic area where the photovoltaic power station is located at different moments in the future are obtained.
The light-electricity conversion model is a mathematical model for describing the relation between the light resource meteorological elements and the active power of the photovoltaic power generation equipment. In actual production, due to the influence of factors such as weather conditions and unit power generation performance, the light irradiance and the electric power often present a complex mapping relationship, and in order to ensure the power prediction accuracy, a complex and variable unit operation condition is generally considered to carry out refined modeling, so that a light-electricity conversion model with high applicability is obtained. The embodiment of the invention can adopt a statistical model to realize a photoelectric conversion model, which is essentially characterized in that a statistical method is utilized to match the physical causal relationship between the input (including NWP, historical data and the like) and the predicted power of a system, the principle is shown in figure 4, and the meteorological elements (such as irradiance, environmental temperature, wind speed and the like) of the NWP, the meteorological elements of the historical NWP and the operation data (such as power, actually measured irradiance, actually measured temperature and the like) of a historical photovoltaic power station are input into the statistical model to obtain the predicted power of the photovoltaic power station, and the statistical model can be realized by adopting technologies of piecewise linear regression, Kalman filtering, neural network, support vector machine and the like.
And the prediction result correction is to correct the predicted power calculated by the photovoltaic power station according to the planned startup capacity and obtain the final prediction result. The prediction result correction is a management link closely related to artificial experience, and is mainly finished by the power prediction responsibility of the photovoltaic power station: firstly, the optical power prediction is linked with the scheduled maintenance work, and the starting capacity, the starting time and the predicted output are reasonably corrected in a power prediction system in a manual input mode according to the number of units involved in the scheduled maintenance work; and secondly, considering that special climates such as haze and the like can cause outage capacity of the photovoltaic power generation unit, and performing experience correction on the prediction result in a manual input mode.
Step S200: and acquiring a total photovoltaic power prediction error according to the prediction data and the measured data.
Wherein, the measured data includes: station actual power, etc.
Specifically, the total photovoltaic power prediction error is obtained by subtracting the actual station power from the station predicted power, that is, the calculation mode is as shown in formula (1).
Etotal＝Ppredict-Pactual＝ENWP+Emodel+Erevise(1)
EtotalPredicting total error, P, for photovoltaic powerpredictPredicting power, P, for a stationactualFor station real power, ENWP、EmodelAnd EreviseThe unit is MW, and the unit is NWP link error, photoelectric conversion model link error and prediction result correction link error respectively.
Step S300: obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data;
wherein equivalent prediction data is obtained by introducing weather and starting-up mode information and the like.
Specifically, the actual measurement data is brought into the photoelectric conversion model, equivalent prediction data under the actual measurement condition can be obtained, and the equivalent prediction data under the actual working condition can be obtained according to the operation data.
Step S400: and obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data.
Specifically, errors caused by each key link are obtained according to equivalent prediction data under actual measurement conditions, equivalent prediction data and prediction power under actual working conditions and by combining the total photovoltaic power prediction error.
Step S500: and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
Specifically, the key links of photovoltaic power prediction include: a numerical weather forecasting link, a model link and a correction link. And optimizing each key link in a targeted manner according to errors caused by each link so as to improve the power prediction precision.
When the numerical weather forecast error is found to be high, the optimization of the numerical weather forecast link comprises the following steps:
(1) and carrying out atmospheric mode parametric tuning.
The atmospheric mode subgrid physical process parameterization scheme captures a physical process which cannot be analyzed by a numerical mode in an explicit mode, is the key for improving the medium-short term forecasting skills in a scale of 10-100km, and is also the key for customizing a numerical weather forecasting mode according to the characteristics of a forecasting object. The climate and meteorological features of different areas, and even the layout of observation points, all affect the mode optimization results. And aiming at main meteorological prediction indexes of the new energy convergence region, a customized grid encryption and parameterization scheme is realized based on a statistical regression and deep learning model, and then a new energy mesoscale forecasting mode is optimized.
(2) Establishing a multi-source meteorological value grid observation platform, and researching an attack and customs meteorological value ensemble forecasting technology.
Fusion access and management of full meteorological element multi-source observation information are achieved through a meteorological grid numerical observation platform, and the problems of few new energy meteorological observation data and poor quality are solved. The new energy weather ensemble forecasting of the power grid is developed, a plurality of weather forecasting modes and a plurality of initial values are adopted for forecasting respectively, the influence of uncertainty of model parameters and the initial values on a final result is reduced, and the probability of occurrence of extreme errors is reduced.
When the optical-to-electrical conversion model error is found to be high, the optimization of the optical-to-electrical conversion model includes:
(1) based on a new energy ubiquitous information platform, the sample size of the full-time-space scale of photoelectric operation is expanded, the model is continuously updated by utilizing on-line monitoring data, abnormal point interference is eliminated, and the data quality of a modeling sample is improved.
(2) And a statistical method such as deep learning is adopted, and the statistical relationship and the light resource fluctuation physical cause-and-effect relationship are combined, so that the model precision is improved.
(3) And correcting errors of the photoelectric power prediction result based on the non-stationary characteristic of the power prediction result in a period of time, and improving the prediction effect of the photoelectric output climbing event.
When the error proportion of the error correction link is higher, the optimization of the error correction link comprises the following steps:
(1) and linking the maintenance plan with the power prediction, and inputting planned startup capacity and startup stop time information influenced by maintenance work into the power prediction system in advance.
(2) The power generation equipment is accurately operated and maintained, and unplanned outage is reduced.
(3) And developing the prediction of the unplanned shutdown of the equipment.
In summary, according to the photovoltaic power prediction optimization method for the photovoltaic power station provided by the embodiment of the invention, by using the multi-source data such as the photo-electric conversion model, the prediction data, the measured data and the operation data, the error of each key link of the power prediction is quantitatively and finely analyzed, so that the weak prediction link of the field station with the poorer prediction level is accurately positioned, each key link of the photovoltaic power prediction is purposefully optimized according to the error caused by each key link, and the corresponding optimization measures are pertinently developed to efficiently improve the power prediction level of the photovoltaic power station.
For example, aiming at the key deviation event, the error and the proportion of each link of the power prediction in the deviation time period are calculated, the cause of the predicted deviation is analyzed, and each key link of the power prediction is optimized in a targeted manner.
For example, the prediction deviation caused by the special weather event is often represented as large prediction deviation in a Model link, the early warning application of the special weather event is developed, the post analysis is changed into the early warning, an early warning Model of extreme weather such as cold tide, strong wind, icing and the like is established, and the early warning risk and the early warning level of various special weather events are given by combining NWP information.
In addition, by carrying out long-period error evaluation of years, seasons and months, the station with poor prediction level and weak prediction links thereof can be accurately positioned, targeted assistance guidance is carried out, and the photovoltaic power prediction level is improved.
In an optional embodiment, the photovoltaic power prediction optimization method for a photovoltaic power plant may further include: and preprocessing the acquired data, wherein the preprocessing comprises filtering, denoising and the like.
In an optional embodiment, the measured data further comprises: the station measured meteorological data (such as part or all of station predicted total irradiance, station predicted direct irradiance, station predicted scattered irradiance, station predicted wind speed, station predicted wind direction, station predicted air temperature and station predicted air pressure) and the like, and the operation data comprises the following steps: planned startup capacity, actual startup capacity, rated installed capacity, and the like; referring to fig. 5, this step S300 may include the following:
step S310: and calculating the equivalent predicted power under the condition of accurate starting capacity according to the station predicted power, the planned starting capacity and the actual starting capacity.
Specifically, the equivalent predicted power under the accurate startup capacity condition is obtained by multiplying the quotient of the actual startup capacity divided by the planned startup capacity by the station predicted power.
Wherein, the following formula is adopted to realize:
Pcapacity＝Ppredict×(Cactual÷Cscheduling)
Pcapacityrepresenting equivalent predicted power, P, under accurate boot-up capacity conditionspredictRepresenting the predicted power of the station, CactualRepresenting the actual boot capacity, CschedulingRepresenting the planned boot capacity.
Step S320: and obtaining equivalent prediction power under an accurate optical resource condition according to the actually measured meteorological data of the station, the optical-electrical conversion model, the planned starting-up capacity and the rated installed capacity.
Specifically, the actually measured meteorological data of the station are input into the optical-electric conversion model to obtain the predicted power generation amount; and then, multiplying the quotient of dividing the planned starting capacity by the rated installed capacity by the predicted power generation amount to obtain the equivalent predicted power under the accurate light resource condition.
Wherein, the following formula is adopted to realize:
Psun＝f(Vactual)×(Cscheduling÷Crated)
Psunrepresenting the equivalent predicted power, V, under accurate optical resource conditionsactualTo representMeasured weather data of a station, f () representing an optical-to-electrical conversion model, CschedulingIndicating the planned boot capacity, CratedIndicating the rated installed capacity.
In an alternative embodiment, the key links include: a numerical weather forecasting link, a photoelectric conversion model link and a correction link; referring to fig. 6, this step S400 may include the following:
step S410: and obtaining the error caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition.
Wherein, referring to fig. 7, the following is used:
ENWP＝Ppredict-Psun
ENWPindicating NWP link error, PpredictPredicting power, P, for a stationsunAnd the equivalent predicted power under the condition of accurate optical resources is represented.
Step S420: and obtaining the error caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition.
Wherein, the following formula is adopted to realize:
Erevise＝Ppredict-Pcapacity
Erevisecorrecting link errors for prediction results, PpredictPredicting power, P, for a stationcapacityRepresents the equivalent predicted power under the condition of accurate boot capacity,
step S430: and subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total photovoltaic power prediction error to obtain the error caused by the optical-electrical conversion model link.
Wherein, the following formula is adopted to realize:
Emodel＝Etotal-ENWP-Erevise
Etotalpredicting the total error for the photovoltaic power, ENWP、EmodelAnd EreviseRespectively NWP link error, optical-electricAnd converting model link errors and correcting predicted results.
In an optional embodiment, the photovoltaic power prediction optimization method for a photovoltaic power plant may further include: and calculating the ratio of the error of each key link in the total error of the photovoltaic power prediction.
In summary, the photovoltaic power prediction optimization method for the photovoltaic power station provided by the embodiment of the invention performs fine evaluation on power prediction errors at any time period or moment or key link, realizes accurate monitoring on the new energy power prediction operation condition, accurately positions the problem site and analyzes the prediction error composition by quantitatively analyzing the error contribution ratio of each link, namely quantitatively evaluates the large deviation event of the photovoltaic power station, calculates the error contribution ratio of each link in the prediction deviation event, and accurately analyzes the cause of the prediction deviation event; by developing long-period error evaluation of years, seasons and months, weak prediction links of stations with poor prediction levels can be accurately positioned, the prediction method is purposefully improved, corresponding optimization measures are developed in a targeted mode, and the power prediction level of the photovoltaic power station is effectively improved.
In order to help those skilled in the art to better understand the embodiments of the present invention, the following takes a specific photovoltaic power plant as an example, and the steps of the photovoltaic power prediction optimization method for a photovoltaic power plant provided by the embodiments of the present invention are described in detail:
(1) the method comprises the steps of obtaining a light-electricity conversion model and station predicted power provided by a power prediction factory, actual station power acquired by station acquisition equipment, actually measured meteorological data of the station, planned startup capacity, actual startup capacity, rated startup capacity and the like in station operation data.
(2) Subtracting the actual power of the station from the predicted power of the station to obtain the total predicted error of the photovoltaic power;
(3) multiplying the actual starting capacity by the planned starting capacity by the station predicted power to obtain equivalent predicted power under the condition of accurate starting capacity;
(4) inputting the actually measured meteorological data of the station into the photoelectric conversion model to obtain predicted generated energy;
(5) and multiplying the predicted power generation amount by the quotient of dividing the planned starting capacity by the rated starting capacity to obtain the equivalent predicted power under the accurate optical resource condition.
(6) Obtaining errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
(7) subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total photovoltaic power prediction error to obtain the error caused by the optical-electrical conversion model link;
(8) and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
The sequence of the method steps provided in the embodiment of the present invention is not the only sequence of the embodiment of the present invention, and the related steps may be exchanged according to the need or may be performed in parallel, so as to increase the flexibility and adaptability of the embodiment of the present invention.
It should be noted that, in the embodiment of the present invention, the meteorological data includes: the station predicts the total irradiance, and on the basis, in order to further improve the prediction accuracy, the method may further include: one or more of a station predicted total irradiance, a station predicted direct irradiance, a station predicted diffuse irradiance, a station predicted wind speed, a station predicted wind direction, a station predicted air temperature, a station predicted air pressure, etc.
Based on the same inventive concept, the embodiment of the present application further provides a photovoltaic power prediction optimization apparatus for a photovoltaic power station, which can be used to implement the method described in the foregoing embodiment, as described in the following embodiment. Because the principle of solving the problems of the photovoltaic power prediction optimization device for the photovoltaic power station is similar to that of the method, the implementation of the photovoltaic power prediction optimization device for the photovoltaic power station can refer to the implementation of the method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 8 is a block diagram of a photovoltaic power prediction optimization apparatus for a photovoltaic power plant according to an embodiment of the present invention; as shown in fig. 8, the photovoltaic power prediction optimization apparatus for a photovoltaic power plant includes: the device comprises a data acquisition module 10, a photovoltaic power prediction total error acquisition module 20, an equivalent prediction data acquisition module 30, an error decoupling evaluation module 40 and a prediction optimization module 50.
The data acquisition module 10 acquires a photovoltaic-to-electrical conversion model, prediction data, actual measurement data, and operation data of the photovoltaic power station.
Wherein, the optical-electric conversion model can be provided by a power forecasting manufacturer.
The prediction data includes: station predicted power, etc. The prediction process of the station predicted power sequentially comprises three key links of numerical weather forecast, light-electric power conversion and prediction result correction according to a business process.
Specifically, firstly, generating a numerical weather forecast product by utilizing a meteorological mesoscale mode for a global initial field; then, inputting meteorological elements related to optical resources in the numerical weather forecast product into an optical-electric conversion model to obtain predicted power generation amount; and finally, correcting the prediction result according to operation information such as a unit maintenance plan and the like to obtain the station prediction power.
The photovoltaic power prediction total error obtaining module 20 obtains a photovoltaic power prediction total error according to the prediction data and the measured data.
Wherein, the measured data includes: station actual power, etc. The photovoltaic power prediction total error acquisition module 20 includes: and the photovoltaic power prediction total error obtaining unit is used for obtaining the photovoltaic power prediction total error by subtracting the actual power of the station from the station prediction power.
Specifically, the total photovoltaic power prediction error is obtained by subtracting the actual station power from the station predicted power, that is, the calculation mode is as shown in formula (1).
Etotal＝Ppredict-Pactual＝ENWP+Emodel+Erevise(1)
EtotalPredicting total error, P, for photovoltaic powerpredictPredicting power, P, for a stationactualFor station real power, ENWP、EmodelAnd EreviseThe unit is MW, and the unit is NWP link error, photoelectric conversion model link error and prediction result correction link error respectively.
The equivalent prediction data obtaining module 30 obtains equivalent prediction data according to the photoelectric conversion model, the measured data and the operating data;
wherein equivalent prediction data is obtained by introducing weather and starting-up mode information and the like.
Specifically, the actual measurement data is brought into the photoelectric conversion model, equivalent prediction data under the actual measurement condition can be obtained, and the equivalent prediction data under the actual working condition can be obtained according to the operation data.
And the error decoupling evaluation module 40 obtains errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data.
Specifically, errors caused by each key link are obtained according to equivalent prediction data under actual measurement conditions, equivalent prediction data and prediction power under actual working conditions and by combining the total photovoltaic power prediction error.
The prediction optimization module 50 optimizes each critical link of the photovoltaic power prediction according to the error caused by each critical link.
Specifically, the key links of photovoltaic power prediction include: a numerical weather forecasting link, a model link and a correction link. And optimizing each key link in a targeted manner according to errors caused by each link so as to improve the power prediction precision.
When the numerical weather forecast error is found to be high, the optimization of the numerical weather forecast link comprises the following steps:
(1) and carrying out atmospheric mode parametric tuning.
The atmospheric mode subgrid physical process parameterization scheme captures a physical process which cannot be analyzed by a numerical mode in an explicit mode, is the key for improving the medium-short term forecasting skills in a scale of 10-100km, and is also the key for customizing a numerical weather forecasting mode according to the characteristics of a forecasting object. The climate and meteorological features of different areas, and even the layout of observation points, all affect the mode optimization results. And aiming at main meteorological prediction indexes of the new energy convergence region, a customized grid encryption and parameterization scheme is realized based on a statistical regression and deep learning model, and then a new energy mesoscale forecasting mode is optimized.
(2) Establishing a multi-source meteorological value grid observation platform, and researching an attack and customs meteorological value ensemble forecasting technology.
Fusion access and management of full meteorological element multi-source observation information are achieved through a meteorological grid numerical observation platform, and the problems of few new energy meteorological observation data and poor quality are solved. The new energy weather ensemble forecasting of the power grid is developed, a plurality of weather forecasting modes and a plurality of initial values are adopted for forecasting respectively, the influence of uncertainty of model parameters and the initial values on a final result is reduced, and the probability of occurrence of extreme errors is reduced.
When the optical-to-electrical conversion model error is found to be high, the optimization of the optical-to-electrical conversion model includes:
(1) based on a new energy ubiquitous information platform, the sample size of the full-time-space scale of photoelectric operation is expanded, the model is continuously updated by utilizing on-line monitoring data, abnormal point interference is eliminated, and the data quality of a modeling sample is improved.
(2) And a statistical method such as deep learning is adopted, and the statistical relationship and the light resource fluctuation physical cause-and-effect relationship are combined, so that the model precision is improved.
(3) And correcting errors of the photoelectric power prediction result based on the non-stationary characteristic of the power prediction result in a period of time, and improving the prediction effect of the photoelectric output climbing event.
When the error proportion of the error correction link is higher, the optimization of the error correction link comprises the following steps:
(1) and linking the maintenance plan with the power prediction, and inputting planned startup capacity and startup stop time information influenced by maintenance work into the power prediction system in advance.
(2) The power generation equipment is accurately operated and maintained, and unplanned outage is reduced.
(3) And developing the prediction of the unplanned shutdown of the equipment.
In summary, the photovoltaic power prediction optimization device for the photovoltaic power station provided by the embodiment of the invention utilizes the multi-source data such as the photo-electric conversion model, the prediction data, the measured data and the operation data to quantitatively and finely analyze the error of each key link of the power prediction, so as to accurately position the weak prediction link of the field station with the poorer prediction level, and purposefully optimize each key link of the photovoltaic power prediction according to the error caused by each key link, thereby facilitating the subsequent development of corresponding optimization measures to efficiently improve the power prediction level of the photovoltaic power station.
For example, aiming at the key deviation event, the error and the proportion of each link of the power prediction in the deviation time period are calculated, the cause of the predicted deviation is analyzed, and each key link of the power prediction is optimized in a targeted manner.
For example, the prediction deviation caused by the special weather event is often represented as large prediction deviation in a Model link, the early warning application of the special weather event is developed, the post analysis is changed into the early warning, an early warning Model of extreme weather such as cold tide, strong wind, icing and the like is established, and the early warning risk and the early warning level of various special weather events are given by combining NWP information.
In addition, by carrying out long-period error evaluation of years, seasons and months, the station with poor prediction level and weak prediction links thereof can be accurately positioned, targeted assistance guidance is carried out, and the photovoltaic power prediction level is improved.
In an optional embodiment, the photovoltaic power prediction optimization apparatus for a photovoltaic power plant may further include: and the preprocessing module is used for preprocessing the acquired data, and the preprocessing comprises filtering, denoising and the like.
In an alternative embodiment, the prediction data comprises: station predicted power; the measured data includes: the station actually-measured meteorological data comprise the following operating data: planned boot capacity, actual boot capacity, and rated boot capacity; the equivalent prediction data obtaining module 30 includes: first and second equivalent prediction power acquisition units
And the first equivalent prediction power acquisition unit calculates equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity.
Wherein the first equivalent prediction power obtaining unit includes: and the first calculation subunit multiplies the station predicted power by the quotient of dividing the actual startup capacity by the planned startup capacity to obtain the equivalent predicted power under the condition of accurate startup capacity.
Wherein, the following formula is adopted to realize:
Pcapacity＝Ppredict×(Cactual÷Cscheduling)
Pcapacityrepresenting equivalent predicted power, P, under accurate boot-up capacity conditionspredictRepresenting the predicted power of the station, CactualRepresenting the actual boot capacity, CschedulingRepresenting the planned boot capacity.
And the second equivalent prediction power obtaining unit obtains equivalent prediction power under an accurate optical resource condition according to the field station actual measurement meteorological data, the optical-electric conversion model, the planned startup capacity and the rated startup capacity.
Wherein the second equivalent prediction power obtaining unit includes: a predicted power generation amount acquisition subunit and a second calculation subunit.
The predicted power generation capacity obtaining subunit inputs the actually measured meteorological data of the station into the optical-electric conversion model to obtain predicted power generation capacity;
and the second calculating subunit multiplies the predicted power generation amount by the quotient of dividing the planned starting capacity by the rated starting capacity to obtain equivalent predicted power under the accurate optical resource condition.
Wherein, the following formula is adopted to realize:
Psun＝f(Vactual)×(Cscheduling÷Crated)
Psunrepresenting the equivalent predicted power, V, under accurate optical resource conditionsactualPresentation fieldMeasured weather data in a station, f () representing an optical-to-electrical conversion model, CschedulingIndicating the planned boot capacity, CratedIndicating the rated installed capacity.
In an alternative embodiment, the key links include: a numerical weather forecasting link, a photoelectric conversion model link and a correction link; the error decoupling evaluation module 40 includes: the system comprises a numerical weather forecast link error evaluation unit, a correction link error evaluation unit and a photoelectric conversion model link error evaluation unit.
An error evaluation unit of the numerical weather forecast link obtains errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
wherein, the following formula is adopted to realize:
ENWP＝Ppredict-Psun
ENWPindicating NWP link error, PpredictPredicting power, P, for a stationsunAnd the equivalent predicted power under the condition of accurate optical resources is represented.
A correction link error evaluation unit obtains an error caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;
wherein, the following formula is adopted to realize:
Erevise＝Ppredict-Pcapacity
Erevisecorrecting link errors for prediction results, PpredictPredicting power, P, for a stationcapacityRepresents the equivalent predicted power under the condition of accurate boot capacity,
and the error evaluation unit of the optical-to-electrical conversion model step subtracts the error caused by the numerical weather forecast step and the error caused by the correction step according to the total photovoltaic power prediction error to obtain the error caused by the optical-to-electrical conversion model step.
Wherein, the following formula is adopted to realize:
Emodel＝Etotal-ENWP-Erevise
Etotalpredicting the total error for the photovoltaic power, ENWP、EmodelAnd EreviseThe method comprises the steps of respectively correcting a NWP link error, a photoelectric conversion model link error and a prediction result error.
In an optional embodiment, the photovoltaic power prediction optimization apparatus for a photovoltaic power plant may further include: and the error ratio calculation module is used for calculating the ratio of the error of each key link in the total error of the photovoltaic power prediction.
In summary, the photovoltaic power prediction optimization device for the photovoltaic power station provided by the embodiment of the invention performs fine evaluation on power prediction errors at any time interval or moment or in a key link, realizes accurate monitoring on the power prediction operation condition of new energy, accurately positions a problem site and analyzes the prediction error composition of the problem site by quantitatively analyzing the error contribution ratio of each link, can quantitatively evaluate a large deviation event of the photovoltaic power station, calculates the error contribution ratio of each link in the prediction deviation event, and accurately analyzes the cause of the prediction deviation event; by developing long-period error evaluation of years, seasons and months, weak prediction links of stations with poor prediction levels can be accurately positioned, the prediction method is purposefully improved, corresponding optimization measures are developed in a targeted mode, and the power prediction level of the photovoltaic power station is effectively improved.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the program:
acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station;
acquiring a total photovoltaic power prediction error according to the prediction data and the measured data;
obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data;
obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
From the above description, the electronic device provided by the embodiment of the invention can be used for quantitatively and finely analyzing errors of each key link of power prediction, so as to accurately position weak prediction links of the field station with poor prediction level, and correspondingly develop corresponding optimization measures to efficiently improve the power prediction level of the photovoltaic power station.
Referring now to FIG. 9, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.
As shown in fig. 9, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the invention includes a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station;
acquiring a total photovoltaic power prediction error according to the prediction data and the measured data;
obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data;
obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
From the above description, the computer-readable storage medium provided by the embodiment of the invention can be used for quantitatively and finely analyzing errors of each key link of power prediction, so as to accurately locate a weak prediction link of a station with a poor prediction level, and correspondingly develop an optimization measure to efficiently improve the power prediction level of a photovoltaic power station.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (14)

1. A photovoltaic power prediction optimization method for a photovoltaic power station is characterized by comprising the following steps:
acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of a photovoltaic power station;
acquiring a total photovoltaic power prediction error according to the prediction data and the measured data;
obtaining equivalent prediction data according to the photoelectric conversion model, the measured data and the operation data;
obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and optimizing each key link of photovoltaic power prediction according to errors caused by each key link.
2. The method of claim 1, wherein the predictive data comprises: station predicted power; the measured data includes: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;
the obtaining equivalent prediction data according to the optical-to-electrical conversion model, the measured data and the operating data includes:
calculating equivalent predicted power under the condition of accurate starting capacity according to the station predicted power, the planned starting capacity and the actual starting capacity;
and obtaining equivalent prediction power under an accurate optical resource condition according to the actually measured meteorological data of the station, the optical-electrical conversion model, the planned starting-up capacity and the rated installed capacity.
3. The method of claim 2, wherein the calculating the equivalent predicted power at an accurate boot capacity based on the station predicted power, the planned boot capacity, and the actual boot capacity comprises:
and multiplying the quotient of dividing the actual starting capacity by the planned starting capacity by the station predicted power to obtain the equivalent predicted power under the condition of accurate starting capacity.
4. The method of claim 2, wherein the obtaining the equivalent predicted power under accurate light resource conditions from the site measured meteorological data, the photovoltaic-to-electrical conversion model, the planned startup capacity, and the rated installed capacity comprises:
inputting the actually measured meteorological data of the station into the photoelectric conversion model to obtain predicted generated energy;
and multiplying the quotient of dividing the planned starting capacity by the rated installed capacity by the predicted power generation amount to obtain the equivalent predicted power under the accurate optical resource condition.
5. The method of claim 2, wherein the key elements comprise: a numerical weather forecasting link, a photoelectric conversion model link and a correction link;
the obtaining of the error caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data comprises:
obtaining errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
obtaining errors caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;
and subtracting the error caused by the numerical weather forecast link and the error caused by the correction link from the total photovoltaic power prediction error to obtain the error caused by the optical-electrical conversion model link.
6. The method of claim 1, wherein the predictive data comprises: the station predicts the power, and the measured data comprises: actual power of the station;
the obtaining of the total photovoltaic power prediction error according to the prediction data and the measured data includes:
and subtracting the actual power of the station from the predicted power of the station to obtain the total predicted error of the photovoltaic power.
7. A photovoltaic power prediction optimization apparatus for a photovoltaic power plant, comprising:
the data acquisition module is used for acquiring a photovoltaic-electric conversion model, prediction data, actual measurement data and operation data of the photovoltaic power station;
the photovoltaic power prediction total error acquisition module is used for acquiring a photovoltaic power prediction total error according to the prediction data and the measured data;
the equivalent prediction data acquisition module is used for acquiring equivalent prediction data according to the photoelectric conversion model, the measured data and the operating data;
the error decoupling evaluation module is used for obtaining errors caused by each key link of photovoltaic power prediction according to the total photovoltaic power prediction error, the prediction data and the equivalent prediction data;
and the prediction optimization module optimizes each key link of photovoltaic power prediction according to errors caused by each key link.
8. The photovoltaic power prediction optimization apparatus for photovoltaic power plants of claim 7, wherein the prediction data comprises: station predicted power; the measured data includes: the station measured weather data, the operation data includes: planned startup capacity, actual startup capacity, and rated installed capacity;
the equivalent prediction data acquisition module comprises:
the first equivalent prediction power obtaining unit is used for calculating equivalent prediction power under the condition of accurate starting capacity according to the station prediction power, the planned starting capacity and the actual starting capacity;
and the second equivalent prediction power acquisition unit is used for acquiring equivalent prediction power under an accurate optical resource condition according to the actually measured meteorological data of the station, the optical-electrical conversion model, the planned startup capacity and the rated installed capacity.
9. The photovoltaic power prediction optimization apparatus for a photovoltaic power plant of claim 8, wherein the first equivalent prediction power obtaining unit comprises:
and the first calculation subunit multiplies the station predicted power by the quotient of dividing the actual startup capacity by the planned startup capacity to obtain the equivalent predicted power under the condition of accurate startup capacity.
10. The photovoltaic power prediction optimization apparatus for a photovoltaic power plant of claim 8, wherein the second equivalent prediction power obtaining unit comprises:
the predicted power generation amount obtaining subunit inputs the actually measured meteorological data of the station into the optical-electric conversion model to obtain predicted power generation amount;
and the second calculating subunit multiplies the predicted power generation amount by the quotient of dividing the planned starting capacity by the rated installed capacity to obtain equivalent predicted power under the accurate light resource condition.
11. The photovoltaic power prediction optimization apparatus for photovoltaic power plants of claim 8, wherein the key links comprise: a numerical weather forecasting link, a photoelectric conversion model link and a correction link;
the error decoupling evaluation module comprises:
the numerical weather forecast link error evaluation unit is used for obtaining errors caused by the numerical weather forecast link according to the station predicted power and the equivalent predicted power under the accurate light resource condition;
the correction link error evaluation unit is used for obtaining errors caused by the correction link according to the station predicted power and the equivalent predicted power under the accurate starting capacity condition;
and the photoelectric conversion model link error evaluation unit subtracts the error caused by the numerical weather forecast link and the error caused by the correction link according to the total photovoltaic power prediction error to obtain the error caused by the photoelectric conversion model link.
12. The photovoltaic power prediction optimization apparatus for photovoltaic power plants of claim 7, wherein the prediction data comprises: the station predicts the power, and the measured data comprises: actual power of the station;
the photovoltaic power prediction total error obtaining module comprises:
and the photovoltaic power prediction total error obtaining unit is used for obtaining the photovoltaic power prediction total error by subtracting the actual power of the station from the station prediction power.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the method for predictive optimization of photovoltaic power for photovoltaic power plants according to any one of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for predictive optimization of photovoltaic power for a photovoltaic power plant according to any one of claims 1 to 6.
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CN102914969A (en) * 2012-09-28 2013-02-06 南方电网科学研究院有限责任公司 Comprehensive error correction method of short-period wind power prediction system
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