CN116780583B - Intelligent energy storage method, system, equipment and medium for photovoltaic power generation - Google Patents
Intelligent energy storage method, system, equipment and medium for photovoltaic power generation Download PDFInfo
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Abstract
An intelligent energy storage method, system, equipment and medium for photovoltaic power generation relate to the field of photovoltaic power generation. In the method, the method comprises the following steps: acquiring weather information; predicting a weather scene of a future time node according to weather information; determining the starting probability of the energy storage battery at a future time node according to a prediction result of the meteorological scene; judging whether the enabling probability is larger than a first set threshold value or not; if yes, determining a charging strategy according to a preset charging strategy selection rule; and charging the energy storage battery according to a charging strategy. By adopting the technical scheme provided by the application, the energy storage battery can have sufficient electric quantity reserve and maintain a good working state when the basic power grid fails in a large probability, so as to carry out emergency power supply for users.
Description
Technical Field
The application relates to the field of photovoltaic power generation, in particular to an intelligent energy storage method, system, equipment and medium for photovoltaic power generation.
Background
The photovoltaic power generation system is an energy system capable of converting solar energy into electric energy, and has wide application in various fields due to its excellent economical efficiency and environmental protection.
For home users, especially home personal users in remote areas, the electric energy supply in the remote areas is unstable due to various reasons such as complex and changeable climate in the remote areas, frequent extreme climate, imperfect infrastructure and the like. When the power supply of a user is damaged due to the influence of climate factors on a basic power grid, an energy storage battery of the photovoltaic power generation system can be used as emergency energy source to supply electric energy to the user.
On the other hand, in remote areas, the traffic is inconvenient, and once the energy storage battery fails, the energy storage battery is difficult to maintain in time, so that the energy storage battery should be kept in a good working state as much as possible to prolong the service life of the battery. How to make the energy storage battery maintain a good working state and electric energy reserve when the infrastructure fails is a problem to be solved at present.
Disclosure of Invention
In order to keep a good working state and electric energy reserve when an infrastructure fails, the application provides an intelligent energy storage method, system, equipment and medium for photovoltaic power generation.
In a first aspect, the application provides an intelligent energy storage method for photovoltaic power generation, which comprises the following steps:
Acquiring weather information;
predicting a weather scene of a future time node according to the weather information;
Determining the starting probability of the energy storage battery at the future time node according to the prediction result of the meteorological scene;
judging whether the enabling probability is larger than a first set threshold value or not;
If yes, determining a charging strategy according to a preset charging strategy selection rule;
and charging the energy storage battery according to the charging strategy.
By adopting the technical scheme, the starting probability of the energy storage battery of the node in future time is determined through the prediction of the meteorological scene, and when the starting probability of the energy storage battery is larger than the set threshold value, the energy storage battery is charged according to the preset charging strategy, so that the energy storage battery can have sufficient electric quantity reserve when the basic power grid fails in a large probability, and emergency power supply is carried out for a user.
Meanwhile, the energy storage battery is charged only when the starting probability of the energy storage battery is larger than a set threshold value, instead of charging the energy storage battery at any time, the influence on the service life of the energy storage battery caused by frequent charging and discharging of the energy storage battery is avoided, and the energy storage battery can maintain a good working state when the basic power grid fails in a high probability.
Optionally, determining the probability of enabling the energy storage battery according to the prediction result of the meteorological scene specifically includes:
Inputting the meteorological scene into a preset power grid meteorological fault prediction model, and outputting the fault probability of a time-based power grid at the future time node;
And taking the fault probability of the basic power grid as the enabling probability of the energy storage battery.
By adopting the technical scheme, when the basic power grid fails, the energy storage battery is required to supply power for a user. And the prediction of the fault probability of the basic power grid is completed through a power grid weather fault prediction model, the starting probability of the energy storage battery is reflected by the fault probability of the basic power grid, and the prediction of the starting probability of the energy storage battery is realized.
Optionally, the meteorological scene includes a disaster meteorological scene and a normal meteorological scene, and the disaster meteorological scene includes a lightning scene, a strong wind scene, a flood scene, a storm scene, an icing scene, a haze scene and a forest fire scene.
By adopting the technical scheme, weather scenes possibly encountered by future time nodes are predicted, 6 types of disaster weather scenes and 1 type of normal weather scenes are arranged in total, and weather scenes of future time nodes are comprehensively considered, so that the prediction of the fault probability of the basic power grid is more accurate.
Optionally, in determining the charging policy according to a preset charging policy selection rule, the method specifically includes:
Predicting the predicted power generation capacity C of the photovoltaic power generation group in an energy storage time period according to the meteorological information, wherein the energy storage time period is a time period from a current time node to the future time node;
Acquiring battery parameters of the energy storage battery, wherein the battery parameters at least comprise a maximum electric storage quantity Q max and a state of charge soc; calculating a charging strategy coefficient alpha through a charging strategy coefficient calculation formula, wherein the charging strategy coefficient calculation formula specifically comprises the following steps:
And determining a threshold interval in which the charging strategy coefficient is positioned, and determining the charging strategy according to the threshold interval in which the charging strategy coefficient is positioned.
By adopting the technical scheme, the relation between the to-be-charged electric quantity of the energy storage battery and the predicted generated energy of the photovoltaic power generation group in the energy storage time period is reflected by the charging strategy coefficient, so that the determined charging strategy can ensure that the energy storage battery is charged to a sufficient electric quantity in the energy storage time period through reliable basis for selecting the charging strategy, and the energy storage battery can provide emergency power supply for a user when a basic power grid is disabled.
Optionally, the charging policy includes a first charging policy, where the first charging policy is specifically that when the charging policy coefficient is in a first threshold interval:
and charging the energy storage battery by using the maximum charging power through the photovoltaic power generation group, wherein the first threshold interval is alpha=1.
Through adopting above-mentioned technical scheme, the prediction generated energy that first strategy of charging was applicable to photovoltaic power generation group just can accomplish the charge to energy storage battery in energy storage time quantum, in order to guarantee better economic benefits this moment, only charges energy storage battery through photovoltaic power generation group.
Optionally, the charging policy includes a second charging policy, where the second charging policy is specifically that when the charging policy coefficient is in a second threshold interval:
and charging the energy storage battery by adopting the maximum charging power through the photovoltaic power generation group and the basic power grid, wherein the second threshold interval is alpha epsilon (0, 1).
Through adopting above-mentioned technical scheme, the second strategy of charging is applicable to the prediction generated energy of photovoltaic power generation group and can't accomplish the charge to energy storage battery in energy storage time quantum, can keep sufficient electric quantity reserve in order to guarantee at the base electric wire netting when breaking down this moment, charges energy storage battery simultaneously through photovoltaic power generation group and base electric wire netting.
Optionally, the charging policy includes a third charging policy, where the third charging policy is specifically that when the charging policy coefficient is in a third threshold interval:
And charging the energy storage battery by adopting healthy charging power through the photovoltaic power generation group, wherein the third threshold interval is alpha epsilon (1, ++ infinity).
Through adopting above-mentioned technical scheme, the prediction generated energy that the third strategy of charging was applicable to photovoltaic power generation group is greater than the energy storage battery wait to charge electric quantity, in order to guarantee energy storage battery's working life this moment, adopts more mild charging mode, charges energy storage battery through healthy charging power to extend energy storage battery's life-span as far as possible.
In a second aspect of the application, there is provided a photovoltaic power generation intelligent energy storage system, the system comprising the following modules:
the weather information acquisition module is used for acquiring weather information;
the weather scene prediction module is used for predicting weather scenes of future time nodes according to the weather information;
the starting probability determining module is used for determining the starting probability of the energy storage battery at the future time node according to the prediction result of the meteorological scene;
The starting probability judging module is used for judging whether the starting probability is larger than a first set threshold value or not;
The charging strategy determining module is used for determining a charging strategy according to a preset charging strategy selection rule;
And the energy storage battery charging module is used for charging the energy storage battery according to the charging strategy.
In a third aspect of the application, an electronic device is provided;
In a fourth aspect of the application, a computer readable storage medium is provided;
in summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. The starting probability of the energy storage battery of the node at the future time is determined through the prediction of the meteorological scene, and when the starting probability of the energy storage battery is larger than a set threshold value, the energy storage battery is charged according to a preset charging strategy, so that the energy storage battery can have sufficient electric quantity reserve to carry out emergency power supply for a user when the basic power grid fails at high probability.
2. The energy storage battery is charged only when the starting probability of the energy storage battery is larger than a set threshold value, rather than being charged all the time, the influence on the service life of the energy storage battery caused by frequent charging and discharging of the energy storage battery is avoided, and the energy storage battery can keep a good working state when the basic power grid fails in a high probability.
3. Setting a plurality of charging strategies, wherein the first charging strategy ensures the economical efficiency of charging the energy storage battery and ensures sufficient electric quantity of the energy storage battery when the energy storage battery is started; the second charging strategy ensures that the electric quantity of the energy storage battery is sufficient when the energy storage battery is started; the third charging strategy further ensures battery health when the energy storage battery is charged.
Drawings
Fig. 1 is a schematic flow chart of an intelligent energy storage method for photovoltaic power generation according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a photovoltaic power generation intelligent energy storage system according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to the disclosure.
Reference numerals illustrate: 201. a weather information acquisition module; 202. a weather scene prediction module; 203. enabling a probability determination module; 204. enabling a probability judging module; 205. a charging strategy determination module; 206. an energy storage battery charging 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 that those skilled in the art will better understand the technical solutions in the present specification, 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 apparent that the described embodiments are only some embodiments of the present application, not all embodiments.
In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of embodiments of the 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.
Referring to fig. 1, the application provides an intelligent energy storage method for photovoltaic power generation, which specifically comprises the following steps:
S1: acquiring weather information;
Specifically, the required weather information is obtained through the arranged weather sensor or from a weather center. The weather information is used as a data base of the base power grid fault probability prediction and the photovoltaic power generation group prediction power generation amount prediction, the acquired weather information type is determined according to the demand data type of the base power grid fault probability prediction and the photovoltaic power generation group prediction power generation amount prediction, and in one feasible embodiment of the application, the weather information specifically comprises accumulated precipitation amount, snowfall amount, lightning current amplitude, number of echoes, wind speed, wind direction, relative humidity, short wave radiation, air temperature, visibility, PM10, PM2.5, evaporation amount, cloud amount, SO 2, cloud height, long wave radiation, NO 2、CO、O3, ground air pressure, sea level air pressure, specific humidity and solar irradiance.
S2: predicting a weather scene of a future time node according to weather information;
Specifically, after the relevant weather information is obtained, the obtained weather information is inferred, calculated and analyzed, so that the weather scene of the future time node is predicted.
After the weather information of the current time node is obtained, the weather information of the future time node is predicted according to the weather information of the current time node, and the weather information of the predicted future time node is classified to determine the weather scene of the future time node. The future time node is set by personnel at the discretion, and the setting of the future time node should be adjusted according to the actual scene by considering factors such as the accuracy of weather prediction, the battery parameters of the energy storage battery, the electric energy consumption of the user load and the like. In one possible embodiment of the present application, the weather information of the future time node may be predicted by means of machine learning, and in another possible embodiment of the present application, the weather information of the future time node may be obtained directly by means of receiving weather forecast information.
And processing the acquired weather information of the future time node through a weather scene classifier to determine the weather scene of the future time node. The meteorological scene classifier is a classification model based on a neural network, in particular, the meteorological scene classifier may be Softmax. After inputting the weather information of the future time node into the weather scene classifier, the weather scene classifier identifies the weather information of a group of future time nodes as a determined weather scene according to the preconfigured weather scene labels, and in one possible embodiment of the application, 7 weather scenes are set, wherein the weather scenes comprise 6 disaster weather scenes respectively: lightning scene, strong wind scene, flood scene, storm scene, icing scene, haze scene, mountain fire scene, and normal meteorological scene.
S3: determining the starting probability of the energy storage battery at a future time node according to a prediction result of the meteorological scene;
Specifically, the energy storage battery is used as an emergency power supply, and emergency power supply is provided for a user when the user cannot obtain power supply from the base power grid due to the fact that the base power grid fails, so that the starting probability of the energy storage battery at a future time node is the failure probability of the base power grid at the future time node. And predicting the fault probability of the node base power grid at the future time according to the prediction result of the meteorological scene, and taking the fault probability of the node base power grid at the future time as the starting probability of the energy storage battery.
In one possible embodiment of the application, the calculation of the underlying grid fault probability is achieved by a pre-set grid weather fault prediction model. When the weather scene of the future time node is determined as the disaster weather scene, the weather scene of the future time node and weather information of the future time node are input into a preset grid weather fault prediction model, and the damage probability of the basic grid is output.
The grid weather fault prediction model may be an SAE (sparse self-coded network) that has been trained prior to the calculation of the underlying grid damage probability. The power grid weather fault model is trained through the historical damage information and the historical weather information when damage occurs, so that the power grid weather model can roughly judge the damage probability of the basic power grid under the weather information and the weather scene when receiving the weather information and the weather scene label, and the calculation of the damage probability of the basic power grid is completed.
In another possible embodiment of the application, when the power grid weather fault prediction model is trained, a power grid damage degree index is introduced into the training set, wherein the power grid damage degree index is an index for evaluating the damage degree of a basic power grid when the basic power grid specified by personnel according to experience or related standards fails. In this embodiment, when the weather scene and weather information of the future time node are input into the weather fault prediction model of the power grid, the damage probability and the predicted damage degree of the power grid at the future time node are output at the same time, so that better data support is provided for the subsequent steps.
S4: judging whether the enabling probability is larger than a first set threshold value or not;
Specifically, the enabling probability of the energy storage battery reflects the possibility that the energy storage battery is used as a power supply at a future time node, and when the enabling probability is larger than a first set threshold value, charging of the energy storage battery is started to finish charging of the energy storage battery in an energy storage time period from the current time node to the future time node, so that the energy storage battery has sufficient electric quantity at the future time node; and when the enabling probability is smaller than or equal to the first set threshold value, continuously predicting the enabling probability of the energy storage battery, and meanwhile, not charging the energy storage battery. In one embodiment of the present application, the first set threshold is set to 50%.
S5: if the enabling probability is larger than a first set threshold, determining a charging strategy according to a preset charging strategy selection rule; specifically, when the enabling probability of the energy storage battery is greater than a set first set threshold value, the base power grid is indicated to fail at a node in the future, the node in the future needs to be started to provide emergency power for a user at the high probability, a reasonable charging strategy is selected for the energy storage battery at the moment, and the energy storage battery can keep sufficient electric quantity as the highest target in the node in the future, so that the energy storage battery is charged.
The charging strategy selection rule is the basis of charging strategy selection, and the charging strategy selection rule prescribes the charging strategy of the energy storage battery corresponding to each charging strategy coefficient on the premise of determining the threshold value interval where the charging strategy coefficient is located.
The charging strategy coefficient is used for reflecting the relation between the predicted generated energy of the photovoltaic power generation group in the energy storage time period and the to-be-charged amount of the energy storage battery, and the energy storage time period is a time period from the current time node to a future time node with the enabling probability of the energy storage battery being larger than a first set threshold value.
The method comprises the steps of obtaining predicted generated energy C of a photovoltaic power generation set in an energy storage time period and battery parameters of an energy storage battery, wherein the predicted generated energy of the photovoltaic power generation set is obtained according to weather information prediction, and the battery parameters of the energy storage battery specifically comprise the maximum electric energy storage Q max of the energy storage battery and the state of charge soc of the energy storage battery at a current time node. Calculating a charging strategy coefficient alpha through a charging strategy coefficient calculation formula, wherein the charging strategy coefficient calculation formula specifically comprises:
after the calculation of the charging strategy coefficient is completed, determining a threshold interval in which the charging strategy coefficient is located.
When the charging strategy coefficient is in a first threshold value interval, the fact that the predicted generated energy of the photovoltaic power generation group is equal to the to-be-charged amount of the energy storage battery is explained, the photovoltaic power generation group can just complete charging of the energy storage battery in an energy storage time period, the energy storage battery is enabled to be in the maximum stored energy amount at a future time node, at the moment, the first charging strategy is adopted to charge the energy storage battery, and the first threshold value interval is specifically alpha=1; when the charging strategy coefficient is in a second threshold value interval, the predicted generating capacity of the photovoltaic power generation group is smaller than the to-be-charged capacity of the energy storage battery, and the photovoltaic power generation group cannot complete the charging of the energy storage battery in the energy storage time period, so that the lacking electric quantity is required to be supplemented from a basic power grid, the energy storage battery is in the maximum electric quantity at a future time node, and the energy storage battery is charged by adopting a second charging strategy at the moment, wherein the second threshold value interval is specifically alpha epsilon (0, 1); when the charging strategy coefficient is in a third threshold interval, the fact that the predicted power generation amount of the photovoltaic power generation group is larger than the to-be-charged amount of the energy storage battery is explained, the photovoltaic power generation group can charge the energy storage battery in the energy storage time period, the third charging strategy is adopted to charge the energy storage battery, and the third threshold interval is specifically alpha epsilon (1, ++ infinity).
The prediction of the predicted power generation amount of the photovoltaic power generation group in the energy storage time period can also be performed through a neural network, short-time prediction of the power generation power of the energy storage time Duan Guangfu power generation group is realized according to the weather information of the energy storage time period, so that the predicted power generation amount is calculated according to the power generation power of the photovoltaic power generation group in the predicted energy storage time period, and it is required to calculate the predicted power generation amount in the energy storage time period in an integral mode because the power generation power of the photovoltaic power generation group is an amount which dynamically changes along with time. The prediction method of the power generation power of the photovoltaic power generation group comprises short-time photovoltaic power generation power prediction based on GA-BP, photovoltaic power generation power prediction based on DBN, photovoltaic power generation power prediction based on BA-WNN and the like, which are the prior art and are not described in detail herein.
S6: and charging the energy storage battery according to a charging strategy.
Specifically, the charging strategies comprise a first charging strategy, a second charging strategy and a third charging strategy, wherein the first charging strategy specifically uses a photovoltaic power generation group as a charging source to charge the energy storage battery, and the first charging strategy charges the energy storage battery through the maximum charging power of the energy storage battery; the second charging strategy specifically uses the photovoltaic power generation group and the basic power grid as charging sources to charge the energy storage battery, and the energy storage battery obtains energy from the basic power grid while obtaining energy from the photovoltaic power generation group, and the second charging strategy charges the energy storage battery through the maximum charging power of the energy storage battery; the third charging strategy specifically uses the photovoltaic power generation group as a charging source to charge the energy storage battery, when the energy storage battery is suitable for the third charging strategy, the predicted power generation amount of the photovoltaic power generation group is far greater than the to-be-charged amount of the energy storage battery, and in order to ensure that the energy storage battery is more healthy to charge, the healthy charging voltage is adopted to charge the energy storage battery. It should be noted that the above-mentioned maximum charging voltage and healthy charging voltage are both charging voltages within the safe range of the charging voltage of the energy storage battery, and both are set by those skilled in the art according to experience.
In another possible embodiment of the present application, the healthy charging voltage may be dynamically adjusted according to the weather information of the current time node, specifically, the charging state of the energy storage battery is determined according to the acquired weather information, and the healthy charging voltage is determined according to the charging state of the energy storage battery in a manner of classifying through a comparison table or a neural network.
Referring to fig. 2, the application further provides a photovoltaic power generation intelligent energy storage system, which specifically comprises the following modules:
A weather information acquisition module 201 for acquiring weather information;
A weather scene prediction module 202, configured to predict a weather scene of a future time node according to weather information;
the enabling probability determining module 203 is configured to determine, according to a prediction result of the meteorological scenario, an enabling probability of the energy storage battery at a future time node;
An enabling probability judging module 204, configured to judge whether the enabling probability is greater than a first set threshold;
The charging policy determining module 205 is configured to determine a charging policy according to a preset charging policy selection rule;
the energy storage battery charging module 206 is configured to charge the energy storage battery according to a charging policy.
It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
The application also discloses the electronic equipment 300. Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present disclosure. 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 at least one hardware form 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. Referring to fig. 3, an operating system, a network communication module, a user interface module, and an application program of a photovoltaic power generation intelligent energy storage method 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 configured to invoke an application program in memory 305 that stores a photovoltaic power generation intelligent energy storage method that, when executed by one or more processors 301, causes electronic device 300 to perform the method as described in one or more of the embodiments above. It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for 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 by the present application, 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 the embodiments 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 305. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory 305, and includes 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 method of the embodiments of the present application. And the aforementioned memory 305 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 foregoing is merely exemplary embodiments of the present disclosure and is 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 variations, 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 (9)
1. An intelligent energy storage method for photovoltaic power generation is characterized by comprising the following steps:
Acquiring weather information;
predicting a weather scene of a future time node according to the weather information;
Inputting the meteorological scene into a preset power grid meteorological fault prediction model, and outputting the fault probability of a time-based power grid at the future time node;
taking the fault probability of the basic power grid as the starting probability of an energy storage battery;
judging whether the enabling probability is larger than a first set threshold value or not;
If so, determining a charging strategy according to a preset charging strategy selection rule, wherein the charging strategy selection rule is an energy storage battery charging strategy corresponding to each charging strategy coefficient on the premise that a threshold value interval where the charging strategy coefficient is located is determined, and the charging strategy coefficient is used for reflecting the relation between the predicted power generation amount of the photovoltaic power generation group in an energy storage time period and the to-be-charged amount of the energy storage battery, and the energy storage time period is a time period from a current time node to a future time node where the enabling probability of the energy storage battery is larger than a first set threshold value;
and charging the energy storage battery according to the charging strategy.
2. The intelligent energy storage method of photovoltaic power generation according to claim 1, wherein the meteorological scenes comprise disaster meteorological scenes and normal meteorological scenes, and the disaster meteorological scenes comprise lightning scenes, strong wind scenes, flood scenes, storm scenes, icing scenes, haze scenes and mountain fire scenes.
3. The intelligent energy storage method for photovoltaic power generation according to claim 1, wherein in determining the charging strategy according to a preset charging strategy selection rule, the method specifically comprises:
Predicting the predicted power generation capacity C of a power generation group of the energy storage time Duan Guangfu according to the meteorological information, wherein the energy storage time period is a time period from a current time node to the future time node;
Acquiring battery parameters of the energy storage battery, wherein the battery parameters at least comprise the maximum electric storage capacity Qmax and the state of charge soc; calculating a charging strategy coefficient alpha through a charging strategy coefficient calculation formula, wherein the charging strategy coefficient calculation formula specifically comprises the following steps:
And determining a threshold interval in which the charging strategy coefficient is positioned, and determining the charging strategy according to the threshold interval in which the charging strategy coefficient is positioned.
4. The intelligent energy storage method of photovoltaic power generation according to claim 3, wherein the charging strategy comprises a first charging strategy, in particular when the charging strategy coefficient is within a first threshold interval:
and charging the energy storage battery by using the maximum charging power through the photovoltaic power generation group, wherein the first threshold interval is alpha=1.
5. A photovoltaic power generation intelligent energy storage method according to claim 3, characterized in that the charging strategy comprises a second charging strategy, in particular when the charging strategy coefficient is in a second threshold interval: and charging the energy storage battery by using the maximum charging power through the photovoltaic power generation group and the basic power grid, wherein the second threshold interval is alpha epsilon (0, 1).
6. A photovoltaic power generation intelligent energy storage method according to claim 3, characterized in that the charging strategy comprises a third charging strategy, in particular when the charging strategy coefficient is in a third threshold interval:
And charging the energy storage battery by adopting healthy charging power through the photovoltaic power generation group, wherein the third threshold interval is alpha epsilon (1, ++ infinity).
7. A photovoltaic power generation intelligent energy storage system, the system comprising:
a weather information acquisition module (201) for acquiring weather information;
A weather scene prediction module (202) for predicting a weather scene of a future time node from the weather information;
An enabling probability determination module (203) for determining an enabling probability of the energy storage battery at the future time node according to a prediction result of the meteorological scene; inputting the meteorological scene into a preset power grid meteorological fault prediction model, and outputting the fault probability of a time-based power grid at the future time node; taking the fault probability of the basic power grid as the enabling probability of the energy storage battery;
an enabling probability judging module (204) for judging whether the enabling probability is greater than a first set threshold;
the charging strategy determining module (205) is used for determining a charging strategy according to a preset charging strategy selection rule;
The energy storage battery charging module (206) is used for charging the energy storage battery according to the charging strategy, the charging strategy selection rule is an energy storage battery charging strategy corresponding to each charging strategy coefficient on the premise that the threshold interval where the charging strategy coefficient is located is determined, the charging strategy coefficient is used for reflecting the relation between the predicted generated energy of the photovoltaic power generation set in the energy storage time period and the to-be-charged amount of the energy storage battery, and the energy storage time period is a time period from the current time node to a future time node where the enabling probability of the energy storage battery is larger than a first set threshold.
8. An electronic device comprising a processor (301), a memory (305), a user interface (303) and a network interface (304), the memory (305) being adapted to store instructions, the user interface (303) and the network interface (304) being adapted to communicate to other devices, the processor (301) being adapted to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method according to any of claims 1-6.
9. A computer readable storage medium storing instructions which, when executed, perform the method steps of any of claims 1-6.
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| CN112366708A (en) * | 2020-11-30 | 2021-02-12 | 深圳市汇拓新邦科技有限公司 | Charging management method, device, equipment and storage medium |
| CN113516280A (en) * | 2021-04-28 | 2021-10-19 | 贵州电网有限责任公司 | Optimization method for power grid equipment fault probability prediction based on big data |
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| CN112366708A (en) * | 2020-11-30 | 2021-02-12 | 深圳市汇拓新邦科技有限公司 | Charging management method, device, equipment and storage medium |
| CN113516280A (en) * | 2021-04-28 | 2021-10-19 | 贵州电网有限责任公司 | Optimization method for power grid equipment fault probability prediction based on big data |
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