CN115833242B - Self-adaptive switching control method and system for mobile array optical storage system - Google Patents
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Abstract
The application relates to a self-adaptive switching control method and a self-adaptive switching control system of a mobile array light storage system, wherein the light storage system comprises a photovoltaic cell, a power storage battery, mobile equipment and a load, and the method comprises the following steps: acquiring a first output power of a photovoltaic cell; obtaining a second output power of the photovoltaic cell; comparing the first output power and the second output power of the photovoltaic cell; and determining a movement parameter of the mobile device based on the comparison result. The maximum power point of the current photovoltaic cell is adjusted through the charge state and/or the environmental information of the power storage battery, so that the photovoltaic cell always operates at the maximum power point, the conversion efficiency of a power generation system is further improved, and the power generation efficiency is further improved.
Description
Technical Field
The application relates to the technical field of photovoltaics, in particular to a self-adaptive switching control method and system of a mobile array optical storage system.
Background
Solar energy is converted into electrical energy by photovoltaic cells. The movable photovoltaic cell can run in parallel with an external power grid or in isolation, and the applicability of the movable photovoltaic cell is wider and more convenient, so that more convenience is brought to the life and production of people.
Therefore, it is necessary to provide a method, a system, a device and a storage medium for adaptively switching and controlling a mobile array photovoltaic storage system, which can improve the power generation efficiency by enabling a mobile photovoltaic cell to be always at a maximum power point, and realizing automatic adjustment of photovoltaic power generation current.
Disclosure of Invention
The specification provides a high-power photovoltaic power generation tracking control method and system, which can adjust the maximum power point of a current photovoltaic cell through the charge state and/or environmental information of a power storage battery, so that the photovoltaic cell always operates at the maximum power point, further the conversion efficiency of a power generation system is improved, and the power generation efficiency is improved.
One of the embodiments of the present disclosure provides a tracking control method for high-power photovoltaic power generation, which includes: acquiring first environment information, first voltage information and first current information at a first moment; determining first power information at a first time based on the first voltage information and the first current information at the first time; acquiring second environmental information at a second moment, wherein the second moment is later than the first moment; the second power information at the second time is determined based on the first environment information at the first time, the first power information at the first time, and/or the second environment information at the second time.
One of the embodiments of the present disclosure provides an adaptive switching control system of a mobile arrayed light storage system, the light storage system including a photovoltaic cell, a power storage cell, a mobile device, and a load, the control system including: the first acquisition module is used for acquiring the first output power of the photovoltaic cell; the second acquisition module is used for acquiring the second output power of the photovoltaic cell; the comparison module is used for comparing the first output power and the second output power of the photovoltaic cell; and the determining module is used for determining the movement parameters of the mobile equipment based on the comparison result.
Drawings
Fig. 1 is a schematic diagram of an application scenario of an adaptive switching control system of a mobile arrayed optical storage system according to some embodiments of the present disclosure;
FIG. 2 is a block diagram of an adaptive switching control system for a mobile arrayed optical storage system according to some embodiments of the present disclosure;
FIG. 3 is a flow chart of an adaptive switching control method for a mobile arrayed optical storage system according to some embodiments of the present disclosure;
FIG. 4 is an exemplary flow chart for determining the number of stored energy cells according to some embodiments of the present description;
FIG. 5 is an exemplary flow chart for determining movement parameters according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic diagram of an application scenario of an adaptive switching control system of a mobile arrayed optical storage system according to some embodiments of the present disclosure.
Under certain environmental conditions, the photovoltaic cell has a maximum output power point, and when the environmental conditions and the electricity storage battery electrically connected with the photovoltaic cell are changed, the photovoltaic cell does not necessarily operate at the maximum power point. The MPPT method can be used for determining the maximum power value output by the photovoltaic cell group, so that the photovoltaic cell group can be in the output state of the maximum power value all the time under any environment, and the conversion efficiency of the photovoltaic power generation system is improved. In some embodiments, the adaptive switching control system of the mobile arrayed light storage system may keep the photovoltaic cells at a maximum power point at all times by adjusting the number of storage cells and the charging scheme by implementing the methods and/or processes disclosed in the present specification. In some embodiments, the preset algorithm provided in some embodiments of the present description may be used to determine the maximum power point of the current photovoltaic cell under the current environmental information, such that the photovoltaic cell is always operating at the maximum power point.
As shown in fig. 1, an application scenario 100 of an adaptive switching control system of a mobile arrayed optical storage system may include an optical storage system 110, a memory 120, a processor 130, a terminal 140, and a network 150. In some embodiments, components in the application scenario of the adaptive switching control system of the mobile arrayed light storage system may be connected and/or communicate with each other via a network 150 (e.g., a wireless connection, a wired connection, or a combination thereof). For example, processor 130 may be connected to memory 120 through network 150.
The light storage system 110 may include photovoltaic cells and their accessories that utilize photovoltaic to generate electrical energy. In some embodiments, the number of photovoltaic cells may be multiple, forming an array of photovoltaic cells. In some embodiments, accessories in the light storage system 110 may include a power storage battery, a mobile device of photovoltaic cells (not shown), a corresponding measurement device of each photovoltaic cell (not shown), and a load (not shown). The photovoltaic cell may be a photovoltaic power harvesting unit. For example, the photovoltaic cell may be a solar panel. In some embodiments, a mobile device may include a mobile wheel and a mobile stand. The moving wheel is used for the overall movement of the photovoltaic cell array. The movable support is used for adjusting the angle between each photovoltaic cell and the ground. In some embodiments, the photovoltaic cell is used to charge a power storage battery, which is used to power a load. In some embodiments, the measurement device of the light storage system 110 may be used to obtain environmental information of the light storage system 110. In some embodiments, the measurement device may include a locator, an illuminometer, a reflectance meter, a camera, or the like. In some embodiments, each photovoltaic cell may be equipped with a set of measurement devices.
In some implementations, the measurement device of the light storage system 110 may further include a voltmeter, an ammeter, and a voltmeter and an ammeter in the battery charging circuit and the photovoltaic cell output circuit, which are used to obtain the current and the voltage of the battery charging in the light storage system 110 in real time, and also to determine the current and the voltage output by the photovoltaic cell 110.
In some embodiments, the optical storage system 110 may be connected to other components in the application scenario 100 of the adaptive switching control system of the mobile arrayed optical storage system through the network 150, and send real-time environment information, current values, and voltage values to the other components, for example, may be sent to the processor 130.
The processor 130 may process data and/or information related to the adaptive switching control system of the mobile arrayed optical storage system. In some embodiments, processor 130 may connect to optical storage system 110 and/or terminal 140 over network 150 to access information and/or data. For example, the processor 130 may obtain real-time environmental information, current and voltage values, etc. from the optical storage system 110. In some embodiments, the processor 130 may process the acquired information and/or data. For example, the processor 130 may process the real-time environment information to determine movement parameters of the corresponding mobile device. In some embodiments, the processor 130 may also be coupled to a third party platform for obtaining predictions of ambient data.
In some embodiments, processor 130 may include one or more processing engines (e.g., a single chip processing engine or a multi-chip processing engine). For example only, the processor 130 may include a Central Processing Unit (CPU). Processor 130 may process data, information, and/or processing results obtained from other devices or system components and execute program instructions based on such data, information, and/or processing results to perform one or more functions described herein.
Terminal 140 may refer to one or more terminal devices or software used by a user. The user may refer to a person using the adaptive switching control system of the mobile arrayed light storage system. For example, the user may be a worker performing an operation. In some embodiments, the terminal 140 may be a mobile device, a tablet computer, a notebook computer, or the like, or any combination thereof. In some embodiments, the terminal 140 may interact with other components in the application scenario of the adaptive switching control system of the mobile arrayed light storage system through the network 150.
Network 150 may include any suitable network that provides information and/or data exchange capable of facilitating the application scenario of the adaptive switching control system of the mobile arrayed optical storage system. Information and/or data may be exchanged between one or more components (e.g., the optical storage system 110, the processor 130, the terminal 140, the memory 120) in an application scenario of an adaptive switching control system of a mobile arrayed optical storage system via the network 150. In some embodiments, network 150 may be any one or more of a wired network or a wireless network. In some embodiments, network 150 may include one or more network access points. For example, network 150 may include wired or wireless network access points. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies.
Memory 120 may be used to store data, instructions, and/or any other information. In some embodiments, memory 120 may store data and/or information obtained from, for example, optical storage system 110, processor 130, etc. For example, the memory 120 may store a power prediction model, a finalization model, environmental information, current and voltage values, and the like. In some embodiments, the memory 120 may be provided in the processor 130. In some embodiments, memory 120 may include mass memory, removable memory, or the like, or any combination thereof.
Fig. 2 is a block diagram of an adaptive switching control system of a mobile arrayed optical storage system according to some embodiments of the present disclosure. As shown in fig. 2, the adaptive switching control system 200 of the mobile arrayed optical storage system may include a first acquisition module 210, a second acquisition module 220, a comparison module 230, and a determination module 240.
In some embodiments, the first acquisition module 210 may be used to acquire a first output power of the photovoltaic cell.
In some embodiments, the second acquisition module 220 may be used to acquire a second output power of the photovoltaic cell.
In some embodiments, the comparison module 230 may be used to compare the first output power and the second output power of the photovoltaic cell.
In some embodiments, the determination module 240 may be configured to determine a movement parameter of the mobile device based on the comparison.
In some embodiments, the number of photovoltaic cells is at least two. In some embodiments, a mobile device includes a mobile wheel and a mobile stand. In some embodiments, the determination module 240 further includes a parameter determination module to: determining a movement parameter of each photovoltaic cell of the at least two photovoltaic cells through a preset algorithm according to environmental information, wherein the environmental information comprises one or more of longitude and latitude information, historical illumination information, current illumination information, ground reflection information and solar altitude information, and the movement parameter comprises a final angle and a final position; and controlling the movable support of the movable device to adjust the angle between the photovoltaic cell and the ground to a final angle, and controlling the movable wheel of the movable device to move to a final position.
In some embodiments, the adaptive switching control system of the mobile arrayed light storage system may further include a quantity module 250. In some embodiments, the quantity module 250 may also be configured to obtain a third output power of the photovoltaic cell; determining the rated charge of the power storage battery; determining a distribution coefficient, wherein the distribution coefficient is between 0 and 1; and determining the number of the storage batteries according to the third output power, the rated charge and the distribution coefficient.
Fig. 3 is a flowchart of an adaptive switching control method of a mobile arrayed optical storage system according to some embodiments of the present disclosure. In some embodiments, the process 300 may be performed by a processor. The process 300 includes the steps of:
step 310, a first output power of the photovoltaic cell is obtained.
In some embodiments, the photovoltaic cell is used to power a power storage cell. The first output power may refer to the output power of the photovoltaic cell at the current moment. The first output power may be determined based on the voltage value and the current value of the output of the photovoltaic cell at the present time.
Step 320, a second output power of the photovoltaic cell is obtained.
The second output power may refer to a maximum power corresponding to a current time of the photovoltaic cell. In some embodiments, the second output power may be a maximum power estimated for the current time instant. In some embodiments, the second output power may be determined from historical data and at least one of the irradiance information.
The irradiation information may refer to environmental information related to photovoltaic cell power generation. In some embodiments, the irradiance information may refer to environmental data of the environment in which all photovoltaic cells are located. In some embodiments, the irradiance information may include at least one of current time, shading information, illumination information, temperature information. Shadow information may refer to the proportion of the current photovoltaic cell that is covered by the shadow, the reason why the covering occurred, and the duration. The ratio of the cover may be the ratio of the covered area to the area of the photovoltaic cell. The cause of the masking may include natural conditions (e.g., clouds) formed by higher object occlusions around the photovoltaic cells. The duration of the shadow may be the time from the beginning of the shadow to the shadow elimination, the length of the time period from the last shadow elimination to the current time. The shadow information can be identified by the picture taken by the camera in the environment where the photovoltaic cell is located. In some embodiments, the irradiance information may be represented by a vector, and each element of the irradiance information vector may represent a current time, shadow information, illumination information, temperature information, etc., respectively.
In some embodiments, the processor may preset a power database according to the historical data, where each element in the power database is a historical irradiation information vector composed of historical irradiation information and corresponding historical power information at the same historical moment. In some embodiments, the processor may determine a historical irradiance information vector having a minimum irradiance information vector distance from the current time through the power database, and take as the second output power historical power information corresponding to the historical irradiance information vector. In some embodiments, the processor may further determine a plurality of historical irradiation information vectors having a distance from the irradiation information vector at the current time less than a threshold value through the power database, and determine a plurality of historical power information corresponding to the plurality of historical irradiation information vectors, and use the maximum historical power information as the second output power. For example, the power information W at the current time 1 And irradiation information H 1 Determining irradiance information vector A 1 (W 1 ,H 1 ) Calculating each element and irradiation information vector A in a preset power database 1 Is based on the distance to determine the irradiation information vector A 1 A plurality of historical irradiation information vectors A with a distance smaller than a preset threshold value 2 、A 3 Wherein A is 2 、A 3 The power values respectively corresponding to the power values are P 2 、P 3 And P 2 >P 3 P2 is taken as the second output power.
According to the embodiments of the present disclosure, by considering the influence of the shadow area on the output power of the photovoltaic cell, the maximum power point of the mobile optical storage system can be predicted more accurately, so that the optical storage system can switch the working modes accurately, and can always operate in the MPPT state, thereby improving the electricity storage efficiency.
In some embodiments, the processor may predict the maximum power at the current time by a power prediction model based on the irradiance information. The power prediction model may be used to determine a second output power at the current time. In some embodiments, the input to the power prediction model may include irradiance information for the current time. The output of the power prediction model may include second power information. The power prediction model may be implemented by, for example, convolutional Neural networks (Convolutional Neural Network, CNN), neural Networks (NN), or the like.
In some embodiments, the power prediction model may be trained from an initial power prediction model. The power prediction model may be trained from training samples with multiple tags. The training samples may include historical exposure information. The label of the training samples may be the corresponding historical maximum power. The labels of the training samples may be obtained based on manual labeling. For example, a training sample is input to the initial power prediction model, a loss function is established based on the output results of the tag and the initial power prediction model in the training process, and parameters of the initial power prediction model are iteratively updated based on the loss function at the same time until a preset condition is met and training is completed. Parameters of the power prediction model may also be determined after training is completed.
Step 330 compares the first output power and the second output power of the photovoltaic cell.
In some embodiments, the processor may determine a comparison of the first output power and the second output power. The comparison result may refer to a similarity of the first output power and the second output power. The similarity of the first output power and the second output power may be determined by a difference or a ratio of the two. In some embodiments, a comparison threshold may be set for the difference or ratio of the first output power and the second output power to determine the similarity, the comparison threshold may be set manually. In some embodiments, the comparison result may include the first output power being greater than the second output power, the second output power minus the first output power, and/or the ratio of the second output power to the first output power being less than or equal to the comparison threshold, the second output power minus the first output power, and/or the ratio of the second output power to the first output power being greater than the comparison threshold phase difference. According to some embodiments of the present disclosure, the power output of the current photovoltaic cell may be obtained based on the comparison result, so as to further determine whether the current photovoltaic cell is in the state of the maximum output power point.
Step 340, determining movement parameters of the mobile device based on the comparison result.
A mobile device may refer to a device that moves the photovoltaic cell. The movement parameter may refer to a parameter that changes the current photovoltaic cell state. In some embodiments, the movement parameters of the mobile device may include movement of the position of the photovoltaic cell and/or change in the angle of the photovoltaic cell to the ground.
In some embodiments, the photovoltaic cell may continue to maintain the current position and angle to the ground, i.e., the current first output power, when the first output power is higher than the second output power.
In some embodiments, the difference of the second output power minus the first output power and/or the ratio of the second output power to the first output power is less than or equal to a comparison threshold, indicating that the current photovoltaic cell is not operating at the maximum power point, the position of the photovoltaic cell or the photovoltaic cell to ground angle may be fine-tuned to cause the photovoltaic cell to operate at the maximum power point.
In some embodiments, the difference of the second output power minus the first output power and/or the ratio of the second output power to the first output power is greater than a comparison threshold, which indicates that the current photovoltaic cell is more different from the maximum power point state, and the photovoltaic cell can be controlled to displace or/and change the angle between the photovoltaic cell and the ground to quickly operate the photovoltaic cell at the maximum power point.
In some embodiments, the number of photovoltaic cells is at least two. In some embodiments, a mobile device includes a mobile wheel and a mobile stand. In some embodiments, determining movement parameters of the mobile device based on the comparison results includes: determining a movement parameter of each of the at least two photovoltaic cells according to environmental information, wherein the environmental information comprises one or more of longitude and latitude information, historical illumination information, current illumination information, ground reflection information and solar altitude information, and the movement parameter comprises a final angle and a final position; and controlling the movable support of the movable device to adjust the angle between the photovoltaic cell and the ground to a final angle, and controlling the movable wheel of the movable device to move to a final position. For detailed information on the movement parameters, see the description of the other parts of the present specification, for example, fig. 5.
In some implementations of the present disclosure, the current operating state of the photovoltaic cell is determined by comparing the current first output power and the predicted maximum power, and the movement parameter of the mobile device is adjusted based on the operating state, specifically, when the first output power and the maximum power are not greatly different, one of the movement parameters can be adjusted by fine adjustment, that is, the output power can reach the maximum power, so that instability caused by adjustment of the movement parameter is reduced; when the first output power and the maximum power have larger phase difference, the output power can be quickly adjusted to the maximum power by adjusting the two moving parameters, the adjustment time of the maximum output power is reduced, and the energy conversion efficiency is improved.
In some embodiments, when charging the electrical storage cells with the optical storage system, it is desirable to determine the number of electrical storage cells and to determine the charging scheme of each electrical storage cell. FIG. 4 is an exemplary flow chart for determining the number of stored energy cells according to some embodiments of the present description. The process 400 may be performed by a processor. In some embodiments, the process 400 may include the steps of:
step 410, a third output power of the photovoltaic cell is obtained.
The third output power of the photovoltaic cell may be an average output power of the photovoltaic cell over a preset period of time. In some embodiments, the preset time period may be a time period in which the accumulated output power is highest. For example, the preset time period may be one day. In some embodiments, the processor may select the time points at which the maximum output power exists within the preset time period, and determine the third output power of the photovoltaic cell by calculating an average value of the output powers corresponding to the time points. The third output power may be obtained from historical data. For example, the output power of the photovoltaic cell on day 8 within 9 months is highest in 9 months, 12 on day 9 months 8: 10. 12: 30. 13: 50. 15:00 reaches the respective maximum power of 1KVA, 1.1KVA, 0.9KVA, 1KVA, respectively, and the third output power is (1+1.1+0.9+1)/4=1 KVA.
Step 420, determining a rated charge of the electric storage battery.
The electricity storage cell may refer to a cell for storing electricity connected to a photovoltaic cell. The number of the storage cells may be plural. The rated charge amount may refer to a rated power corresponding to each of the power storage batteries. In some embodiments, the rated charge of each of the plurality of power storage cells may be the same. The rated charge of each storage battery can be obtained from its identification.
Step 430, determining an allocation coefficient, the allocation coefficient being between 0 and 1;
in some embodiments, the distribution coefficient may be determined by empirical values. In some embodiments, the partition coefficient is between 0.1-0.9. In some embodiments, the partition coefficient is between 0.2-0.8. In some embodiments, the partition coefficient is between 0.3-0.7. In some embodiments, the partition coefficient is between 0.4-0.6.
Step 440, determining the number of the storage batteries according to the third output power, the rated charge amount and the distribution coefficient.
Illustratively, the number of the storage batteries may be determined by equation (1).
Wherein N represents the number of the storage batteries, P 1 Represents the third output power, P 2 Represents the rated charge of each storage battery, θ represents the distribution coefficient, [ the ]Representation pairThe results obtained were rounded.
Since the state of charge of each of the storage batteries is different, the charging power of each of the storage batteries is not the same. When the battery is near full charge, its charging power is reduced, for example, the rated charge of each battery is 1KVA, the charging power is 1KVA when charging is started, and when the battery is charged quickly, its charging power is less than 1KVA, possibly 0.5KVA. In this way, the output power of the photovoltaic cell, which outputs electrical energy to the storage battery, is also reduced, so that the photovoltaic cell does not operate at the maximum power point, thereby making the energy utilization low. Through the embodiment provided in the specification, a proper amount of electricity storage battery is added, so that the output power of the photovoltaic battery can be effectively and reasonably distributed, the electric energy output by the photovoltaic battery is fully utilized, and more electric quantity is stored.
In some embodiments, the processor may determine that the first portion of the battery is charged first, and when the battery in the first portion of the battery is near full charge, the output power of the photovoltaic cell may be reduced, and the reduced output power may be provided to the second portion of the battery for charging.
The first portion of the battery may refer to a battery that stores electricity preferentially. The number of the first partial storage cells may be determined by formula (2).
Wherein M represents the number of the first partial storage cells, P 1 Represents the third output power, P 2 Representing the rated charge of each storage battery []Representation pairThe results obtained were rounded.
The second portion of the storage cells are storage cells that are divided from the first portion of the storage cells. The number of second portion of the storage cells may be determined by (N-M).
For example, the third output power of the photovoltaic cell is 10KVA, the rated charge of the battery is 2KVA, and typically 5 batteries are set, but according to the embodiment of the present disclosure, the distribution coefficient may be set to 0.6, then 8 batteries may be set according to formula (1), the 5 batteries of the first portion may be charged first, when one of the batteries is quickly charged after full charge, the charging power may be reduced, and the output power of the photovoltaic cell to the quickly charged battery may be reduced, and the excessive output power may be distributed to the 3 batteries of the second portion for charging.
According to some embodiments of the application, the photovoltaic cell can be kept to run at the maximum power point by adjusting the charging scheme of the power storage battery, so that the storage capacity and the utilization rate of energy can be improved, and the waste of energy can be reduced.
FIG. 5 is an exemplary flow chart for determining movement parameters according to some embodiments of the present description. The process 500 may be performed by a processor. In some embodiments, the process 500 may include the steps of:
step 510, determining a movement parameter of each of the at least two photovoltaic cells through a preset algorithm according to the environmental information.
The environmental information may refer to information about the environment in which each photovoltaic cell is located at the current time. In some embodiments, the environmental information includes one or more of latitude and longitude information, historical illumination information, current illumination information, ground reflection information, solar altitude information, and the movement parameters include a final angle and a final position. Longitude and latitude information may be determined by a position locator, such as GPS. The illumination information may be the illumination intensity around the photovoltaic cell. For example, the light intensity information may be 100Klux. The illumination information may be measured by an illuminometer. Ground reflection information may refer to ground reflectivity, which may be expressed by the ratio of the energy reflected by the ground to the light energy impinging on the ground. The ground reflection information may be determined by a reflectometer. The solar altitude information may be obtained through a third party database, such as a weather database.
Each photovoltaic cell movement parameter of the at least two photovoltaic cells may refer to a parameter of displacement of the photovoltaic cell or/and a parameter of change of the angle of the photovoltaic cell to the ground. In some embodiments, each photovoltaic cell movement parameter of the at least two photovoltaic cells may include a final angle and a final position. The final angle may refer to the angle of the photovoltaic cell to the ground corresponding to the maximum power point of the photovoltaic cell. The final position may refer to the latitude and longitude of the photovoltaic cell at the maximum power point. In some embodiments, the number of photovoltaic cells is at least two. Since the position of each photovoltaic cell is different, the movement parameters of each photovoltaic cell may be different, i.e. the final angle and the final position of each photovoltaic cell may be different.
In some embodiments, a preset algorithm may be used to determine movement parameters for each of the at least two photovoltaic cells based on the environmental information. In some embodiments, the preset algorithm may include: generating a plurality of candidate combinations 511 for each of the plurality of photovoltaic cells, wherein the candidate combinations 511 may include candidate angles and candidate positions for each photovoltaic cell; multiple iterations of updating are performed on the plurality of candidate combinations 511 to determine the final angle and final position of each of the at least two photovoltaic cells. At least one of the multiple iterations includes: for at least one candidate combination 511, updating the corresponding angle and position change amplitude based on the relation with the historical optimal combination, and updating the candidate combination 511 based on the angle and position change amplitude, wherein the historical optimal combination is determined based on an evaluation algorithm.
Candidate combinations may refer to combinations for iteratively updating the determined movement parameters. Candidate combinations 511 may be represented by vectors. Candidate combinations 511 may include candidate angles and candidate positions for each candidate combination, i.eX i Represents the i-th candidate combination, X i1 Represents the candidate angle, X, in the ith candidate combination i2 Representing candidate positions in the i-th candidate combination, and k represents the number of iterative updates. In the first iteration, k is 1.
In some embodiments, the processor may generate the plurality of candidate combinations 511 in the first round of iterative updating in a variety of ways. Candidate combinations 511 may be generated by random methods or acquired based on historical data.
In some embodiments, at least one of the multiple rounds of iterative updating includes: and updating the candidate combinations based on the change amplitude of the angle and the position corresponding to the relation update of the historical optimal combination for at least one candidate combination, wherein the historical optimal combination is determined based on an evaluation algorithm. Details of the historical optimum combination and evaluation algorithm may be found in the following. In some embodiments, step 510 may include steps 520-540.
Step 520, update the magnitude of the change in angle and position.
The change amplitude of the angle and the position refers to the update amplitude of the candidate angle and the candidate position in the candidate combination 511. In some embodiments, the magnitude of the change in angle and position may include multiple sets. For example, the number of the variation magnitudes of the angle and the position is the same as the number of the candidate combinations 511. Each of the plurality of sets of the magnitude of the change in angle and position corresponds one-to-one with each of the candidate combinations 511.
In some embodiments, the magnitudes of change for each set of angles and positions may include a plurality of sub-magnitudes of change. Each sub-amplitude of change represents an updated amplitude of the candidate angle or candidate position in the candidate combination corresponding to the amplitude of change of the angle and position. For example, the magnitude of the change in angle and position corresponding to the ith candidate combination may be expressed asWherein V is i1 、V i2 And respectively representing the sub-variation amplitude corresponding to the candidate angle and the candidate position in the ith candidate combination, wherein k represents the iteration times. In some embodiments, the initial values of the magnitudes of the changes in angle and position corresponding to the plurality of candidate combinations may be the same or different. Wherein initial values of the magnitudes of the changes in angle and position may be generated on a random basis.
In some embodiments, a history optimal combination may be used to determine the magnitude of the change in angle and position. The processor may update the magnitude of the change in angle and position based on the relationship of the candidate combination to the historical optimal combination. For example, if the difference between the candidate combination and the historical optimal combination is small, the corresponding angle and the candidate value change amplitude are small; and vice versa, larger.
In some embodiments, the historical optimal combination may include an independent optimal combination corresponding to the candidate combination, and an associated optimal combination that collectively corresponds to the plurality of candidate combinations.
In some embodiments, for each of the plurality of candidate combinations, the historical optimal combination includes an independent optimal combination corresponding to each candidate combination, and an associated optimal combination that corresponds in common with the plurality of candidate combinations. Wherein, the associated optimal combinations corresponding to the candidate combinations are the same, and the independent optimal combinations are different. In some embodiments, the historical optimal combination may be determined based on an evaluation algorithm. For a detailed description of the evaluation algorithm, reference is made to the following relevant text.
The historical optimal combination may refer to a candidate combination with the maximum photovoltaic cell output power prediction value in the historical iterative update process. For more description of battery output power predictions, see the relevant description elsewhere.
The independent optimal combination corresponding to the ith candidate combination may refer to the update candidate combination with the largest photovoltaic cell output power predicted value corresponding to the plurality of update candidate combinations corresponding to the ith candidate combination up to the current iteration update round. For example, in the k-th iteration, the independent optimal combination corresponding to the i-th candidate combination may be the updated candidate combination with the largest battery output power predicted value corresponding to all updated i-th candidate combinations in the previous k-1 iteration.
The associated optimal combination corresponding to the ith candidate combination is the update candidate combination with the largest photovoltaic cell output power predicted value corresponding to all the update candidate combinations corresponding to the plurality of candidate combinations up to the current iteration round. For example, in the kth iteration, the associated optimal combination may be the updated candidate combination with the highest predicted battery output power value during the previous k-1 iterations.
In some embodiments, by combining the independent optimal combination and the associated optimal combination, the local exploration and global situation can be better combined in the process of exploring the final angle and the final position, and the final angle and the final position can be obtained quickly.
In some embodiments, the historical optimal combination may be determined based on an evaluation algorithm. In some embodiments, the evaluation algorithm may include: determining, based on the plurality of candidate combinations 511, a battery output power prediction value corresponding to each of the plurality of candidate combinations 511 by a final determination model; based on the battery output power predictions, a historical optimal combination is determined.
In some embodiments, the processor may predict the power corresponding to the current environmental information by finalizing the model based on the environmental information. The finalization model may be used to determine a corresponding power based on the current environmental information. In some embodiments, the input to the finalized model may include environmental information for the current time. The output of the finalized model may include second power information. The finalized model may be implemented by, for example, a convolutional Neural Network (Convolutional Neural Network, CNN), a Neural Network (NN), or the like.
In some embodiments, the finalized model may be trained by an initial finalized model. The finalized model may be trained from a training sample with multiple tags. The training samples may include historical environmental information. The labels of the training samples may be the corresponding power. The labels of the training samples may be obtained based on manual labeling. For example, a training sample is input to the initial final determination model, a loss function is established based on the label and the output result of the initial final determination model, and parameters of the initial final determination model are iteratively updated based on the loss function at the same time during training until a preset condition is satisfied. Parameters of the final determined model after training is completed can also be determined.
In some embodiments, the evaluation algorithm may refer to an algorithm that evaluates candidate combinations based on a machine learning model, preset rules, or the like. In some embodiments, the evaluation algorithm may include iterating the photovoltaic cell output power predictions for each candidate combination through a finalized model prediction based on the candidate combinations, and determining a historical optimal combination based on the cell output power predictions. Specifically, environmental data of each candidate combination is input into a final determination model to obtain a corresponding photovoltaic cell output power predicted value, the photovoltaic cell output power predicted value in multiple iterations in each candidate combination is the independent optimal combination, and the photovoltaic cell output power predicted value in multiple iterations in all candidate combinations is the associated optimal combination.
By some embodiments of the present description, a historical optimal combination may be more accurately determined based on a battery output power prediction value, and further improve iteration efficiency.
In some embodiments, the processor updating the angles and the magnitudes of the changes in the positions corresponding to the plurality of candidate combinations may refer to determining, for each position and the sub-magnitudes of the changes in the angles, based on the following formula: updated sub-variance = weight 1 original candidate combination sub-variance + weight 2 first difference + weight 3 second difference. The updated sub-variation amplitude may be the sub-variation amplitude corresponding to the candidate angle or the candidate position in the candidate combination corresponding to the next round of updating iteration. The sub-variation amplitude of the original candidate combination can be the sub-variation amplitude corresponding to the candidate angle or the candidate position in the candidate combination corresponding to the current round of updating iteration. The first difference value corresponds to the difference value between the candidate combination and the independent optimal combination; the second difference value corresponds to a difference value of the candidate combination and the associated optimal combination. The weights 1, 2 and 3 may be preset, or may be determined by other manners, for example, determined based on an algorithm such as regression analysis.
In step 530, the candidate combinations are updated.
At iteration 1, the plurality of candidate combinations 511 includes a plurality of initial combinations. As shown in fig. 5, the plurality of initial combinations includes initial combination 1, initial combination 2, initial combination i, and the like.
The iterative updating of the candidate combinations by the processor includes iteratively updating each candidate combination based on the sub-variation magnitudes corresponding to each candidate combination. For example, the sub-variation amplitude may be added to the original candidate combination to obtain an updated candidate combination, i.e., the updated candidate combination may be expressed as Wherein,,can be expressed as +.>
By the method, the search can be performed in different directions in the solution space based on a plurality of groups of different candidate combinations. The direction of exploration and the magnitude of the variation amplitude can be dynamically adjusted according to the comparison with the historical optimal combination, so that exploration is more targeted and the final position and the final angle are approached more quickly.
In step 540, whether the iteration satisfies the termination condition or not, the movement parameter 512 is determined.
In some embodiments, when the iteration meets a preset termination condition when the iteration candidate combination is performed, the iteration update is ended. The termination condition may be that the iteration number reaches an iteration number threshold, the predicted value of the battery output power corresponding to the candidate combination reaches a preset value, and the like, and the iteration is stopped. For example, the iteration number threshold is set to 100 times, and when the iteration number reaches 100 times, the iteration is stopped. And setting a preset value, and stopping iteration when the predicted value of the battery output power reaches the preset value.
In some embodiments, after each iteration update is completed, at least one of the sets of candidate combinations is determined whether it meets an iteration termination condition. If the iteration satisfies the iteration termination condition, the iteration is ended and the historical optimal combination is determined as the movement parameter 512. And if the iteration does not meet the termination condition, continuing the next iteration until the iteration termination condition is met.
Some embodiments of the present description may update the plurality of candidate combinations in an iterative manner, and optimize the candidate combinations. Therefore, the final angle and the final position of the maximum output power of the photovoltaic cell are determined, and the utilization rate of the photovoltaic cell is further improved while energy is saved.
And 550, controlling a movable support of the mobile device to adjust the angle between the photovoltaic cell and the ground to a final angle, and controlling a movable wheel of the mobile device to move to a final position.
In some embodiments, a mobile device includes a mobile wheel and a mobile stand. Each photovoltaic cell is provided with a corresponding mobile support. The movable wheels are arranged below the movable photovoltaic cells and are used for moving the photovoltaic cell array group. The processor may send the final position to a movement wheel, which receives the command to move the photovoltaic cell array to the final position. The movable support is used for adjusting the angle between the photovoltaic cell and the ground. One for each photovoltaic cell. The processor may send the final angle of each photovoltaic cell to the mobile carriage, which receives the command, will adjust the angle of each photovoltaic cell to the final angle with the ground.
Because the longitude and latitude of the photovoltaic cells are different, the environmental information of each photovoltaic cell is different, and the moving parameter of each photovoltaic cell can be accurately determined by refining the environmental information of each photovoltaic cell, so that the photovoltaic cell operates at the maximum power point, and the light energy utilization rate is improved. The iterative algorithm provided by some embodiments of the present disclosure determines the final position and the final angle, and adjusts the position and the angle of the photovoltaic cell based on the final position and the final angle, thereby realizing automatic adjustment of the photovoltaic cell, reducing energy loss, and improving the power generation efficiency.
It should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (4)
1. An adaptive switching control method of a mobile arrayed light storage system, wherein the light storage system comprises a photovoltaic cell, a power storage battery, a mobile device and a load, and the method is characterized by comprising the following steps:
Acquiring first output power of the photovoltaic cell, wherein the first output power refers to output power of the photovoltaic cell at the current moment;
obtaining second output power of the photovoltaic cell, wherein the second output power is estimated maximum power corresponding to the current moment;
comparing the first output power and the second output power of the photovoltaic cell;
determining a movement parameter of the mobile device based on the comparison result;
the number of the electric storage batteries is at least two, the at least two electric storage batteries are used for supplying power to the load, and the method for determining the number of the electric storage batteries comprises the following steps:
obtaining third output power of the photovoltaic cell, wherein the third output power is average output power of the photovoltaic cell in a preset time period;
determining the rated charge of the power storage battery;
determining a distribution coefficient, the distribution coefficient being between 0 and 1;
determining the number of the electricity storage batteries according to the third output power, the rated charge and the distribution coefficient, wherein the number of the electricity storage batteries is as follows:
wherein N represents the number of the electric storage batteries, P1 represents the third output power, P2 represents the rated charge of each electric storage battery, θ represents the distribution coefficient, and [ (] represents the rounding of the obtained result);
When charging, the quantity of the first part of electricity storage batteries is determined, the first part of electricity storage batteries are charged preferentially, the first part of electricity storage batteries refer to batteries which store electricity preferentially, and the quantity of the first part of electricity storage batteries is as follows:
wherein M represents the number of the first part of the power storage batteries, P1 represents the third output power, P2 represents the rated charge of each power storage battery, and [ (] represents the rounding of the obtained result;
and determining a second part of the electric storage battery, and charging the second part of the electric storage battery, wherein the second part of the electric storage battery is an electric storage battery except the first part of the electric storage battery.
2. The method of claim 1, wherein the number of photovoltaic cells is at least two, the mobile device comprises a mobile wheel and a mobile stand, and the determining the mobile parameter of the mobile device based on the comparison comprises:
determining a movement parameter of each photovoltaic cell of the at least two photovoltaic cells through a preset algorithm according to environment information, wherein the environment information comprises one or more of longitude and latitude information, historical illumination information, current illumination information, ground reflection information and solar altitude information, and the movement parameter comprises a final angle and a final position;
And controlling the movable support of the mobile device to adjust the angle between the photovoltaic cell and the ground to a final angle, and controlling the movable wheel of the mobile device to move to the final position.
3. An adaptive switching control system of a mobile arrayed light storage system, the light storage system comprising a photovoltaic cell, a power storage cell, a mobile device and a load, the control system comprising:
the first acquisition module is used for acquiring first output power of the photovoltaic cell, wherein the first output power refers to output power of the photovoltaic cell at the current moment;
the second acquisition module is used for acquiring second output power of the photovoltaic cell, wherein the second output power is estimated maximum power corresponding to the current moment;
a comparison module for comparing the first output power and the second output power of the photovoltaic cell;
a determining module, configured to determine a movement parameter of the mobile device based on a comparison result; the number of the electricity storage batteries is at least two, the at least two electricity storage batteries are used for supplying power to the load, and the control system further comprises a number module, wherein the number module is used for:
Obtaining third output power of the photovoltaic cell, wherein the third output power is average output power of the photovoltaic cell in a preset time period;
determining the rated charge of the power storage battery;
determining a distribution coefficient, the distribution coefficient being between 0 and 1;
determining the number of the electricity storage batteries according to the third output power, the rated charge and the distribution coefficient, wherein the number of the electricity storage batteries is as follows:
wherein N represents the number of the electric storage batteries, P1 represents the third output power, P2 represents the rated charge of each electric storage battery, θ represents the distribution coefficient, and [ (] represents the rounding of the obtained result);
when charging, the quantity of the first part of electricity storage batteries is determined, the first part of electricity storage batteries are charged preferentially, the first part of electricity storage batteries refer to batteries which store electricity preferentially, and the quantity of the first part of electricity storage batteries is as follows:
wherein M represents the number of the first part of the power storage batteries, P1 represents the third output power, P2 represents the rated charge of each power storage battery, and [ (] represents the rounding of the obtained result;
and determining a second part of the electric storage battery, and charging the second part of the electric storage battery, wherein the second part of the electric storage battery is an electric storage battery except the first part of the electric storage battery.
4. The system of claim 3, wherein the number of photovoltaic cells is at least two, the mobile device comprises a mobile wheel and a mobile stand, and the determining module further comprises:
a parameter determining module for:
determining a movement parameter of each photovoltaic cell of the at least two photovoltaic cells through a preset algorithm according to environment information, wherein the environment information comprises one or more of longitude and latitude information, historical illumination information, current illumination information, ground reflection information and solar altitude information, and the movement parameter comprises a final angle and a final position;
and controlling the movable support of the mobile device to adjust the angle between the photovoltaic cell and the ground to a final angle, and controlling the movable wheel of the mobile device to move to the final position.
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