CN117175657B - Capacity configuration method, device, medium and equipment of photovoltaic energy storage system - Google Patents
Capacity configuration method, device, medium and equipment of photovoltaic energy storage system Download PDFInfo
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Abstract
The application provides a capacity configuration method, device, medium and equipment of a photovoltaic energy storage system, and relates to the technical field of photovoltaic energy storage. According to the method, different weather types are divided according to the daily historical solar irradiance, further photovoltaic power generation power corresponding to the different weather types is predicted, then the modified capacity configuration scheme corresponding to the different weather types is obtained through comparison with standard photovoltaic power generation power, and finally the final predicted capacity configuration scheme is determined through predicting the future weather types. Different configurations can be adopted for the capacity of the photovoltaic energy storage system according to different weather types, compared with fixed configurations, the capacity of the energy storage system required to be put into use in the same day can be effectively reduced, flexible configuration of the energy storage system is realized, and energy storage loss and operation cost are further reduced.
Description
Technical Field
The application relates to the technical field of photovoltaic energy storage, in particular to a capacity configuration method, a device, a medium and equipment of a photovoltaic energy storage system.
Background
Along with the development of the photovoltaic industry, in order to solve the fluctuation problem of the grid connection of the photovoltaic power generation, the photovoltaic energy storage system can timely respond to the dynamic change condition of energy, effectively absorb and release the energy, relieve the impact suffered by the power system, and the capacity configuration has important influence on the economy and the safety of the photovoltaic power station and the power system, so that the realization of the functions of the energy storage system is difficult to ensure due to the excessively small capacity, and high investment and operation and maintenance cost can be brought due to the excessively large capacity. Therefore, reasonable capacity allocation of the photovoltaic energy storage system is significant for improving the operation benefit of the photovoltaic energy storage system and maintaining the stability of the system.
In the related photovoltaic energy storage technology, solar irradiance and the scale of photovoltaic power generation are adopted for capacity configuration throughout the year, but the output power of actual photovoltaic power generation in different weather has obvious difference, and the output characteristic and the power prediction result have different characteristics. Therefore, the capacity configuration for the photovoltaic energy storage system in the related art is not flexible enough, resulting in high energy storage loss and high operation cost.
Disclosure of Invention
The application provides a capacity configuration method, device, medium and equipment of a photovoltaic energy storage system, which are used for realizing flexible configuration of the capacity of the photovoltaic energy storage system by judging weather types in several days in the future and correcting a standard capacity configuration scheme, so that energy storage loss and operation cost can be reduced.
In a first aspect, the present application provides a method for configuring a capacity of a photovoltaic energy storage system, the method comprising:
acquiring historical solar irradiance of an area where a photovoltaic module in a photovoltaic energy storage system is located;
clustering the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals;
based on the solar irradiance intervals, respectively predicting the photovoltaic power generation power respectively corresponding to the weather types;
obtaining standard photovoltaic power generation power and a corresponding standard capacity configuration scheme of the photovoltaic energy storage system;
comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power, and determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme;
acquiring a predicted weather type of a plurality of days after the current moment;
and determining a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the corrected capacity configuration scheme corresponding to the weather type.
By adopting the technical scheme, different weather types are divided according to the daily historical solar irradiance, further the photovoltaic power generation power corresponding to the different weather types is predicted, then the correction capacity allocation scheme corresponding to the different weather types is obtained by comparing the photovoltaic power generation power with the standard photovoltaic power generation power, and finally the final prediction capacity allocation scheme is determined by predicting the future weather types. Different configurations can be adopted for the capacity of the photovoltaic energy storage system according to different weather types, compared with fixed configurations, the capacity of the energy storage system required to be put into use in the same day can be effectively reduced, flexible configuration of the energy storage system is realized, and energy storage loss and operation cost are further reduced.
Optionally, the clustering processing is performed on the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals, including:
clustering the historical solar irradiance by using a clustering formula to obtain weather types corresponding to a plurality of solar irradiance intervals;
the clustering formula is as follows:
wherein F is an objective function, n is a total number of days in the historical solar irradiance, i is a sequence number of the number of days in the historical solar irradiance, and x i For the historical solar irradiance corresponding to the ith date, k is the number of the weather types, j is the serial number of the weather types, P j Is the cluster center of the jth weather type.
By adopting the technical scheme, different solar irradiance intervals can be accurately divided by using a clustering algorithm, and further different weather types can be determined according to the different solar irradiance intervals.
Optionally, the predicting the photovoltaic power generation power respectively corresponding to each weather type based on the plurality of solar irradiance intervals includes:
taking the historical solar irradiance as an input sample, taking the actually measured photovoltaic power generation power corresponding to the date of the historical solar irradiance as an output sample, and training to obtain a power prediction neural network model;
and inputting the historical solar irradiance of the plurality of solar irradiance intervals into the power prediction neural network model to obtain photovoltaic power generation power respectively corresponding to various weather types.
By adopting the technical scheme, the historical actually measured photovoltaic power generation power is used as an output sample to train the power prediction neural network model, so that the accuracy of the photovoltaic power generation power corresponding to the weather type can be improved, and the accuracy of the correction capacity configuration scheme can be improved when the photovoltaic power generation power is compared with the standard photovoltaic power generation power in the follow-up process.
Optionally, after the historical solar irradiance of the plurality of solar irradiance intervals is input into the power prediction neural network model, the method further includes:
calculating the average absolute error of the power prediction neural network model;
and optimizing the power prediction neural network model based on the average absolute error.
By adopting the technical scheme, the average absolute error of the power prediction neural network model is calculated, and the accuracy of the predicted value corresponding to the weather type corresponding to the solar irradiance interval can be judged.
Optionally, the comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power, and determining a modified capacity configuration scheme corresponding to the weather type based on the standard capacity configuration scheme includes:
calculating a power difference value between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power;
and if the power difference value is larger than the set threshold value, determining the configuration capacity of the super capacitor based on the power difference value to obtain a correction capacity configuration scheme corresponding to the weather type.
By adopting the technical scheme, when the difference between the photovoltaic power generation power and the standard photovoltaic power generation power is large, the super capacitor is introduced to effectively adjust the photovoltaic power generation power with large fluctuation range, so that the photovoltaic energy storage system can respond in time.
Optionally, after determining the predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the modified capacity configuration scheme corresponding to the weather type, the method further includes:
judging whether the predicted capacity allocation scheme can meet the actual capacity allocation requirement of the photovoltaic energy storage system;
and if the predicted capacity configuration scheme cannot meet the actual capacity configuration requirement of the photovoltaic energy storage system, adjusting the corrected capacity configuration scheme.
By adopting the technical scheme, the predicted capacity allocation scheme is judged to meet the actual capacity allocation requirement, so that the predicted capacity allocation scheme is correspondingly and dynamically adjusted, and the flexibility of capacity allocation of the photovoltaic energy storage system is improved.
In a second aspect, the present application provides a capacity configuration device of a photovoltaic energy storage system, the device comprising:
the historical data acquisition module is used for acquiring historical solar irradiance of an area where the photovoltaic module in the photovoltaic energy storage system is located;
the weather type dividing module is used for carrying out clustering processing on the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals;
the power generation power prediction module is used for respectively predicting the photovoltaic power generation power respectively corresponding to the weather types based on the plurality of solar irradiance intervals;
the standard capacity acquisition module is used for acquiring the standard photovoltaic power generation power of the photovoltaic energy storage system and a corresponding standard capacity configuration scheme;
the configuration scheme correction module is used for comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power and determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme;
the weather type prediction module is used for obtaining the predicted weather types of a plurality of days after the current moment;
and the configuration scheme prediction module is used for determining a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the corrected capacity configuration scheme corresponding to the weather type.
In a third aspect, the present application provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform any of the methods described above.
In a fourth aspect, the present application provides an electronic device comprising a processor, a memory for storing instructions, and a transceiver for communicating with other devices, the processor for executing the instructions stored in the memory to cause the electronic device to perform a method as in any one of the above.
In summary, the beneficial effects brought by the technical scheme of the application include:
by adopting the technical scheme, different weather types are divided according to the daily historical solar irradiance, further the photovoltaic power generation power corresponding to the different weather types is predicted, then the correction capacity allocation scheme corresponding to the different weather types is obtained by comparing the photovoltaic power generation power with the standard photovoltaic power generation power, and finally the final prediction capacity allocation scheme is determined by predicting the future weather types. Different configurations can be adopted for the capacity of the photovoltaic energy storage system according to different weather types, compared with fixed configurations, the capacity of the energy storage system required to be put into use in the same day can be effectively reduced, flexible configuration of the energy storage system is realized, and energy storage loss and operation cost are further reduced.
Drawings
Fig. 1 is a schematic flow chart of a capacity configuration method of a photovoltaic energy storage system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a capacity allocation device of a photovoltaic energy storage system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 201. a historical data acquisition module; 202. a weather type dividing module; 203. a power generation power prediction module; 204. a standard capacity acquisition module; 205. a configuration scheme correction module; 206. a weather type prediction module; 207. a configuration scheme prediction module; 300. an electronic device; 301. a processor; 302. a communication bus; 303. a user interface; 304. a network interface; 305. a memory.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.
In the description of embodiments of the present application, words such as "exemplary," "such as" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "illustrative," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "illustratively," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The execution main body of the method and the device is a server in the control photovoltaic energy storage system, can acquire various operation data in the photovoltaic energy storage system, and can control switching of the energy storage battery so as to realize different capacity configuration schemes.
Referring to fig. 1, a flow chart of a capacity configuration method of a photovoltaic energy storage system according to an embodiment of the present application is provided, and the method may be implemented by a computer program, may be implemented by a single-chip microcomputer, or may be run on a capacity configuration device of a photovoltaic energy storage system based on von neumann system. The computer program may be integrated in the application or may run as a stand-alone tool class application. Specific steps of the capacity configuration method of the photovoltaic energy storage system are described in detail below.
S101, acquiring historical solar irradiance of an area where a photovoltaic module in the photovoltaic energy storage system is located.
The historical solar irradiance refers to historical data of solar irradiance of an area where the photovoltaic module is located, the solar irradiance refers to radiant energy of solar radiation reaching the solid earth surface in unit area per unit time after the solar radiation is absorbed, scattered, reflected and the like by an atmosphere layer, and the unit time of the historical solar irradiance is in a unit of day. The historical solar irradiance can be obtained from historical data of the photovoltaic power station, or from a meteorological database such as NASA, meteonorm, and will not be described in detail herein.
Specifically, the obtained historical solar irradiance may be subjected to processing steps such as data cleaning or outlier removal for subsequent analysis and use.
S102, clustering is carried out on the historical solar irradiance, and weather types corresponding to a plurality of solar irradiance intervals are obtained.
Because the historical solar irradiance is daily standardized data, the historical solar irradiance can be clustered, the historical solar irradiance data is divided into a plurality of sections, and each section is allocated with a corresponding weather type. The purpose of this clustering process is to classify the data according to similarity of solar irradiance in order to better understand solar irradiance changes under different weather conditions. Through cluster analysis, several different solar irradiance intervals can be determined, each interval corresponding to a particular weather type.
Specifically, clustering is carried out on the historical solar irradiance by using a clustering formula, so as to obtain weather types corresponding to a plurality of solar irradiance intervals;
the clustering formula is:
wherein F is an objective function, n is a total number of days in the historical solar irradiance, i is a sequence number of the number of days in the historical solar irradiance, and x i For the historical solar irradiance corresponding to the ith date, k is the number of weather types, j is the serial number of the number of weather types, P j Is the cluster center of the jth weather type.
K objects (namely the number of weather types) are selected from the historical solar irradiance as initial clustering centers, euclidean distances between the k clustering centers and the residual historical solar irradiance are calculated, each residual historical solar irradiance is distributed to the clustering center with the shortest Euclidean distance to form k initial clustering clusters, then new clustering centers are respectively calculated for the objects in each initial clustering cluster, new clustering clusters are redistributed, and the above processes are repeated continuously to obtain optimal clustering results. Each historical solar irradiance in the final clustering result is distributed into final clusters to form k clusters, and each cluster corresponds to one solar irradiance interval and weather type.
In one implementation, the weather types include a first weather type, a second weather type, and a third weather type, the first weather type being a generalized sunny weather type, the second weather type being a generalized cloudy and cloudy weather type, the third weather type being a generalized rain, snow, fog, dust, and haze weather type.
Specifically, the number k of weather types represented by the cluster center may be set to 3, and thus three weather types, that is, a first weather type, a second weather type, and a third weather type, respectively, may be obtained. Likewise, the number of weather types may be thinned to other numbers to have distinguishing features of solar irradiance.
The first weather type is a generalized sunny type, namely, in a solar irradiance interval corresponding to the weather type, solar irradiance is at a higher level, and photovoltaic power generation power is also higher. The second weather type is a generalized overcast weather and cloudy weather type, which means that in a solar irradiance interval corresponding to the weather type, solar irradiance is lower than the solar irradiance level of the first weather type. The third weather type is a generalized weather type of rain, snow, fog, dust and haze, which means that the solar irradiance is very low or even close to zero in a solar irradiance interval corresponding to the weather type. By clustering the historical solar irradiance and corresponding to the weather types, the photovoltaic power generation power under different weather conditions can be predicted more accurately.
S103, respectively predicting the photovoltaic power generation power respectively corresponding to various weather types based on the plurality of solar irradiance intervals.
Photovoltaic power generation power refers to the rate at which a photovoltaic module converts solar energy into electrical energy. In a photovoltaic energy storage system, solar irradiance is one of the factors that mainly affects photovoltaic power generation. Therefore, when the relation between solar irradiance and photovoltaic power generation power in the photovoltaic energy storage system is analyzed, the historical photovoltaic power generation power data can be respectively predicted to solar irradiance intervals corresponding to various weather types according to the clustering result of the historical solar irradiance.
In an implementation manner, taking the historical solar irradiance as an input sample, taking the actual measured photovoltaic power generation power corresponding to the date of the historical solar irradiance as an output sample, and training to obtain a power prediction neural network model; and inputting the historical solar irradiance of the plurality of solar irradiance intervals into a power prediction neural network model to obtain photovoltaic power generation power respectively corresponding to various weather types.
Each sample contains the historical solar irradiance of a day and the measured photovoltaic power generation power of the day. Each sample of model training is divided into a training set and a testing set, and the samples are selected as the testing set at fixed intervals. By establishing a prediction model, we can predict the photovoltaic power generation under different weather types (according to the clustering result) and corresponding solar irradiance intervals. The power prediction neural network model may consider historical photovoltaic power generation power data and corresponding solar irradiance data to predict by learning a relationship between them. According to the solar irradiance interval corresponding to the weather type, the historical photovoltaic power generation power data can be mapped to the corresponding solar irradiance interval, and the photovoltaic power generation power corresponding to the interval can be obtained by using a power prediction neural network model.
In one implementation, the mean absolute error of the power prediction neural network model is calculated; and optimizing the power prediction neural network model based on the average absolute error.
Specifically, the formula of the average absolute error is:
wherein MAE is the average absolute error, n is the total number of days in the historical solar irradiance, i is the number of days in the historical solar irradiance, and P f,i For predicted photovoltaic power generation on day i, P m,i The measured photovoltaic power generation power on the i th day.
Comparing the photovoltaic power generation power predicted by the power prediction neural network model with the actually measured photovoltaic power generation power, and calculating the absolute error of each sample, namely the absolute value of the difference between the actual value and the predicted value. The absolute errors of all samples are averaged, the absolute error values of all samples are added, and then divided by the number of samples to obtain the average absolute error. Meanwhile, the model optimization can be realized by adjusting the super parameters of the power prediction neural network model based on the average absolute error.
S104, obtaining standard photovoltaic power generation power of the photovoltaic energy storage system and a corresponding standard capacity configuration scheme.
The standard photovoltaic power generation power of the photovoltaic energy storage system is the daily power generation power calculated by comprehensively considering the factors such as temperature, illumination condition, system efficiency and the like according to solar irradiance throughout the year. In response, the standard capacity configuration scheme needs to be determined by considering factors such as the electric quantity requirement of the power supply load, the amount of electric energy to be stored, the use frequency, the conversion efficiency of the photovoltaic power generation assembly, the charge and discharge efficiency of the energy storage system and the like.
S105, comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power, and determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme.
Comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power to obtain the difference between the photovoltaic power generation power and the standard photovoltaic power generation power, wherein the larger the difference is, the more the adjustment amount is. And then determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme to adjust the capacity of the photovoltaic power generation system according to the actual weather condition.
In an alternative embodiment, calculating a power difference between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power; if the power difference value is larger than the set threshold value, determining the configuration capacity of the super capacitor based on the power difference value, and obtaining a correction capacity configuration scheme corresponding to the weather type.
The power difference is the absolute value of the difference between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power. By setting the threshold value, whether the power difference value to be adjusted is too large or not is judged, the storage battery is used for carrying out capacity adjustment by too large power fluctuation, the service life of the storage battery is shortened, and the stability of the photovoltaic energy storage system is affected. Therefore, when the power difference is larger than the set threshold, the capacity of the super capacitor is adjusted to obtain a modified capacity configuration scheme comprising the super capacitor corresponding to the weather type, so that the capacity configuration of the photovoltaic energy storage system is completed.
S106, obtaining the predicted weather types several days after the current moment.
Since the scheme in the application predicts the subsequent weather types by day units and adjusts the corresponding capacity allocation scheme according to the weather types, the predicted weather types of several days are obtained by day units when the predicted weather types are obtained. The predicted weather type may be obtained by acquiring weather data, and will not be described in detail herein.
S107, determining a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the corrected capacity configuration scheme corresponding to the weather type.
When the predicted weather type is obtained for a plurality of days after the current moment, the predicted capacity configuration scheme can be planned for the photovoltaic energy storage system for the next plurality of days according to the corrected capacity configuration scheme corresponding to the obtained weather type. For example, if the first day is a rainy day, the second day is cloudy, and the third day is sunny in the predicted weather types, the corrected capacity configuration scheme corresponding to the third weather type is adopted in the first day, the corrected capacity configuration scheme corresponding to the second weather type is adopted in the second day, the corrected capacity configuration scheme corresponding to the first weather type is adopted in the third day, and the capacity configuration schemes of the three days are summarized to obtain the predicted capacity configuration scheme of the photovoltaic energy storage system in the next three days.
In one implementation, determining whether the predicted capacity allocation scheme can meet the actual capacity allocation requirements of the photovoltaic energy storage system; and if the predicted capacity allocation scheme cannot meet the actual capacity allocation requirement of the photovoltaic energy storage system, adjusting the predicted capacity allocation scheme.
In actual use, whether the actual photovoltaic power generation power deviates from the photovoltaic power generation power corresponding to the weather type can be monitored, so that whether the predicted capacity configuration scheme can meet the actual capacity configuration requirement of the photovoltaic energy storage system is judged, if the difference is large, the predicted capacity configuration scheme may not meet the actual capacity configuration requirement, and therefore adjustment of the predicted capacity configuration scheme is required.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Referring to fig. 2, a schematic structural diagram of a capacity configuration device of a photovoltaic energy storage device according to an exemplary embodiment of the present application is shown. The apparatus may be implemented as all or part of an apparatus by software, hardware, or a combination of both. The device comprises a historical data acquisition module 201, a weather type division module 202, a power generation power prediction module 203, a standard capacity acquisition module 204, a configuration scheme correction module 205, a weather type prediction module 206 and a configuration scheme prediction module 207.
The historical data acquisition module 201 is used for acquiring historical solar irradiance of an area where a photovoltaic module in the photovoltaic energy storage system is located;
the weather type dividing module 202 is configured to perform clustering processing on the historical solar irradiance, so as to obtain weather types corresponding to a plurality of solar irradiance intervals;
the power generation power prediction module 203 is configured to predict photovoltaic power generation powers respectively corresponding to various weather types based on a plurality of solar irradiance intervals;
the standard capacity acquisition module 204 is configured to acquire standard photovoltaic power generation power of the photovoltaic energy storage system and a corresponding standard capacity configuration scheme;
the configuration scheme correction module 205 is configured to compare the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power, and determine a correction capacity configuration scheme corresponding to the weather type based on the standard capacity configuration scheme;
a weather type prediction module 206, configured to obtain a predicted weather type several days after the current time;
the configuration scheme prediction module 207 is configured to determine a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the modified capacity configuration scheme corresponding to the weather type.
Optionally, the weather type classification module 202 further includes a clustering processing unit.
The clustering processing unit is used for clustering the historical solar irradiance by using a clustering formula to obtain weather types corresponding to a plurality of solar irradiance intervals;
the clustering formula is:
wherein F is an objective function, n is a total number of days in the historical solar irradiance, i is a sequence number of the number of days in the historical solar irradiance, and x i For the historical solar irradiance corresponding to the ith date, k is the number of weather types, j is the serial number of the number of weather types, P j Is the cluster center of the jth weather type.
Optionally, the generated power prediction module 203 further includes a model prediction unit and a model optimization unit.
The model prediction unit is used for taking the historical solar irradiance as an input sample, taking the actually measured photovoltaic power generation power corresponding to the date of the historical solar irradiance as an output sample, and training to obtain a power prediction neural network model; and inputting the historical solar irradiance of the plurality of solar irradiance intervals into a power prediction neural network model to obtain photovoltaic power generation power respectively corresponding to various weather types.
The model optimization unit is used for calculating the average absolute error of the power prediction neural network model; and optimizing the power prediction neural network model based on the average absolute error.
Optionally, the configuration scheme modification module 205 further includes a difference processing unit.
The difference processing unit is used for calculating the power difference between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power; if the power difference value is larger than the set threshold value, determining the configuration capacity of the super capacitor based on the power difference value, and obtaining a correction capacity configuration scheme corresponding to the weather type.
Optionally, the configuration scheme prediction module 207 further comprises a configuration scheme adjustment unit.
The configuration scheme adjusting unit is used for judging whether the predicted capacity configuration scheme can meet the actual capacity configuration requirement of the photovoltaic energy storage system; and if the predicted capacity allocation scheme cannot meet the actual capacity allocation requirement of the photovoltaic energy storage system, adjusting the predicted capacity allocation scheme.
The embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are adapted to be loaded by a processor and execute the method for configuring a capacity of a photovoltaic energy storage device according to the embodiment shown in fig. 1, and a specific execution process may be referred to in the specific description of the embodiment shown in fig. 1, which is not repeated herein.
Referring to fig. 3, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 3, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein the communication bus 302 is used to enable connected communication between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.
The Memory 305 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. As shown in fig. 3, an operating system, a network communication module, a user interface module, and an application program of a capacity configuration method of a photovoltaic energy storage system may be included in the memory 305 as a computer storage medium.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 301 may be used to invoke an application program in memory 305 that stores a method of capacity configuration of a photovoltaic energy storage system, which when executed by one or more processors, causes an electronic device to perform the method as in one or more of the embodiments described above.
An electronic device readable storage medium storing instructions. The method of one or more of the above embodiments is performed by one or more processors, which when executed by an electronic device.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (10)
1. A method for capacity configuration of a photovoltaic energy storage system, the method comprising:
acquiring historical solar irradiance of an area where a photovoltaic module in a photovoltaic energy storage system is located;
clustering the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals;
based on the solar irradiance intervals, respectively predicting the photovoltaic power generation power respectively corresponding to the weather types;
obtaining standard photovoltaic power generation power and a corresponding standard capacity configuration scheme of the photovoltaic energy storage system;
comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power, and determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme;
acquiring a predicted weather type of a plurality of days after the current moment;
and determining a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the corrected capacity configuration scheme corresponding to the weather type.
2. The method of claim 1, wherein the clustering the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals comprises:
clustering the historical solar irradiance by using a clustering formula to obtain weather types corresponding to a plurality of solar irradiance intervals;
the clustering formula is as follows:
wherein F is an objective function and n is the historyThe total number of days in the solar irradiance, i is the number of days in the historical solar irradiance, x i For the historical solar irradiance corresponding to the ith date, k is the number of the weather types, j is the serial number of the weather types, P j Is the cluster center of the jth weather type.
3. The method according to claim 1, wherein predicting photovoltaic power generated by each weather type based on the plurality of solar irradiance intervals, respectively, comprises:
taking the historical solar irradiance as an input sample, taking the actually measured photovoltaic power generation power corresponding to the date of the historical solar irradiance as an output sample, and training to obtain a power prediction neural network model;
and inputting the historical solar irradiance of the plurality of solar irradiance intervals into the power prediction neural network model to obtain photovoltaic power generation power respectively corresponding to various weather types.
4. The method of claim 3, wherein after said inputting the historical solar irradiance of the number of solar irradiance intervals into the power predicting neural network model, further comprising:
calculating the average absolute error of the power prediction neural network model;
and optimizing the power prediction neural network model based on the average absolute error.
5. The method of claim 1, wherein the comparing the photovoltaic power generation corresponding to the weather type with the standard photovoltaic power generation, and determining a modified capacity configuration corresponding to the weather type based on the standard capacity configuration comprises:
calculating a power difference value between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power;
and if the power difference value is larger than the set threshold value, determining the configuration capacity of the super capacitor based on the power difference value to obtain a correction capacity configuration scheme corresponding to the weather type.
6. The method of claim 1, wherein after determining the predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the revised capacity configuration scheme corresponding to the weather type, further comprising:
judging whether the predicted capacity allocation scheme can meet the actual capacity allocation requirement of the photovoltaic energy storage system;
and if the predicted capacity configuration scheme cannot meet the actual capacity configuration requirement of the photovoltaic energy storage system, adjusting the predicted capacity configuration scheme.
7. The method of claim 1, wherein the weather types include a first weather type, a second weather type, and a third weather type, the first weather type being a weather type of a generalized sunny day, the second weather type being a weather type of a generalized cloudy day and cloudy day, the third weather type being a weather type of a generalized rain, snow, fog, dust, haze.
8. A capacity allocation device of a photovoltaic energy storage system, the device comprising:
the historical data acquisition module is used for acquiring historical solar irradiance of an area where the photovoltaic module in the photovoltaic energy storage system is located;
the weather type dividing module is used for carrying out clustering processing on the historical solar irradiance to obtain weather types corresponding to a plurality of solar irradiance intervals;
the power generation power prediction module is used for respectively predicting the photovoltaic power generation power respectively corresponding to the weather types based on the plurality of solar irradiance intervals;
the standard capacity acquisition module is used for acquiring the standard photovoltaic power generation power of the photovoltaic energy storage system and a corresponding standard capacity configuration scheme;
the configuration scheme correction module is used for comparing the photovoltaic power generation power corresponding to the weather type with the standard photovoltaic power generation power and determining a correction capacity configuration scheme corresponding to the weather type on the basis of the standard capacity configuration scheme;
the weather type prediction module is used for obtaining the predicted weather types of a plurality of days after the current moment;
and the configuration scheme prediction module is used for determining a predicted capacity configuration scheme of the photovoltaic energy storage system based on the predicted weather type and the corrected capacity configuration scheme corresponding to the weather type.
9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any one of claims 1 to 7.
10. An electronic device comprising a processor, a memory for storing instructions, and a transceiver for communicating with other devices, the processor for executing instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-7.
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