CN116667443B - Photovoltaic equipment and photovoltaic equipment control system - Google Patents
Photovoltaic equipment and photovoltaic equipment control system Download PDFInfo
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- CN116667443B CN116667443B CN202310736817.4A CN202310736817A CN116667443B CN 116667443 B CN116667443 B CN 116667443B CN 202310736817 A CN202310736817 A CN 202310736817A CN 116667443 B CN116667443 B CN 116667443B
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- 238000010248 power generation Methods 0.000 claims abstract description 181
- 238000012544 monitoring process Methods 0.000 claims abstract description 76
- 238000007664 blowing Methods 0.000 claims abstract description 39
- 238000004146 energy storage Methods 0.000 claims abstract description 23
- 238000004891 communication Methods 0.000 claims abstract description 5
- 230000005611 electricity Effects 0.000 claims description 67
- 239000000428 dust Substances 0.000 claims description 45
- 238000004140 cleaning Methods 0.000 claims description 24
- 238000011156 evaluation Methods 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 16
- 238000010801 machine learning Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 8
- 230000003749 cleanliness Effects 0.000 description 30
- 238000012549 training Methods 0.000 description 12
- 238000004088 simulation Methods 0.000 description 9
- 239000013598 vector Substances 0.000 description 9
- 238000006243 chemical reaction Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000012986 modification Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000001276 controlling effect Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000002372 labelling Methods 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 3
- 238000007599 discharging Methods 0.000 description 2
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
Embodiments of the present disclosure provide a photovoltaic device and a control system, including: the system comprises a power generation device, a monitoring assembly, a blowing device and a processor; the power generation device at least comprises a photovoltaic power generation assembly and an energy storage assembly, and the air blowing device is used for blowing air to the power generation device; the monitoring component is used for acquiring first monitoring data and second monitoring data; the power generation device, the monitoring assembly and the blower device are in communication connection with a processor for: determining a power generation characteristic of the photovoltaic equipment, determining a power utilization characteristic of electric equipment connected to the photovoltaic equipment, determining a power supply strategy of the photovoltaic equipment, determining a control instruction, and controlling the working state of at least one of a power generation device and a monitoring component.
Description
Technical Field
The present disclosure relates to the field of energy efficiency management, and in particular, to a photovoltaic device and a photovoltaic device control system.
Background
A photovoltaic system is a power generation system that directly converts light energy into electrical energy using the photovoltaic effect. The set of photovoltaic system mainly comprises a solar cell array, a storage battery, a photovoltaic inverter and an alternating current circuit breaker. The photovoltaic and battery energy storage combination becomes a power supply system, so that the stability of photovoltaic power supply can be improved, the utilization rate of photovoltaic power generation can be improved, and the functional application of the system can be expanded. However, photovoltaic power generation is affected by natural factors such as day and night, seasons, illumination, temperature and the like, and has volatility, randomness and intermittence. It cannot independently stabilize the power supply and often needs to stabilize the instability in combination with the grid.
Aiming at the problem of making a power consumption plan according to electric quantity, CN104052150A provides an intelligent household energy efficiency management system of a household distributed photovoltaic power generation system, the application focuses on the energy efficiency management system to acquire the generated energy of the distributed photovoltaic power generation system, the electric quantity of a storage battery and the power consumption information of each electric appliance through a wireless communication system, an optimal power consumption plan is obtained through a traversing method, the electric appliances are controlled to work when the power consumption is lowest, the photovoltaic power generation allowance is on the internet when the photovoltaic power generation allowance is proper, and the household power consumption benefit is maximized. However, the actual demand data of electricity is not fully considered, and the problem of improper regulation may exist.
Therefore, it is desirable to provide a photovoltaic device and a control system capable of reasonably regulating and controlling a power supply strategy, so as to improve the degree of intelligence and reduce the waste of electric energy.
Disclosure of Invention
One or more embodiments of the present specification provide a photovoltaic device comprising: the system comprises a power generation device, a monitoring assembly, a blowing device and a processor; the power generation device at least comprises a photovoltaic power generation assembly and an energy storage assembly, and the air blowing device is used for blowing air to the power generation device; the monitoring component is used for acquiring first monitoring data and second monitoring data; the power generation device, the monitoring assembly and the blower device are in communication with the processor, the processor being configured to: determining a power generation characteristic of the photovoltaic device based on the first monitoring data; determining the electricity utilization characteristics of electric equipment connected to the photovoltaic equipment based on the second monitoring data; determining a power supply strategy of the photovoltaic device based on the power generation characteristic and the power utilization characteristic; and determining a control instruction based on the power supply strategy, and controlling the working state of at least one of the power generation device and the book making monitoring assembly based on the control instruction.
One or more embodiments of the present disclosure provide a photovoltaic device control system, including a first feature determination module, a second feature determination module, a policy determination module, a control module; the first characteristic determining module is used for determining the power generation characteristic of the photovoltaic equipment based on the first monitoring data; the second characteristic determining module is used for determining the electricity utilization characteristic of electric equipment connected to the photovoltaic equipment based on the second monitoring data; the strategy determination module is used for determining a power supply strategy of the photovoltaic equipment based on the power generation characteristics and the power utilization characteristics; the control module is used for determining a control instruction based on the power supply strategy and controlling the working state of at least one of the power generation device and the monitoring component based on the control instruction.
One of the embodiments of the present disclosure provides a computer-readable storage medium storing computer instructions, where when the computer reads the computer instructions in the storage medium, the computer performs the functions corresponding to the aforementioned photovoltaic device control system.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic block diagram of a photovoltaic device according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a photovoltaic device control method according to some embodiments of the present description;
FIG. 3 is a schematic illustration of determining a power generation characteristic, shown in accordance with some embodiments of the present description;
FIG. 4 is a schematic illustration of determining a power usage characteristic shown in accordance with some embodiments of the present description;
fig. 5 is a block diagram of a photovoltaic device control system 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 block schematic diagram of a photovoltaic device according to some embodiments of the present description.
In some embodiments, as shown in fig. 1, the photovoltaic apparatus 100 includes a power generation device 110, a monitoring assembly 120, a blowing device 130, and a processor 140.
The power generation device 110 may be a device that provides power to an external device or powered device. In some embodiments, the power generation device 110 may include a photovoltaic power generation component 111, an energy storage component 112, and a voltage conversion component (not shown), among others, that may provide power to the monitoring component 120, the blower device 130, the processor 140, other external devices, or powered devices.
In some embodiments, the photovoltaic power generation component 111 may be a component that converts light energy into electrical energy, such as a solar panel or the like.
In some embodiments, the energy storage assembly 112 may be used to store electrical energy generated by the photovoltaic power generation assembly 111 and provide electrical energy to other devices. In some embodiments, the energy storage assembly 112 may also include an interface for connection of a mobile charging device. Correspondingly, the energy storage component 112 can provide electric energy for a mobile charging device (such as a charger, a battery, etc.) through the interface.
In some embodiments, the photovoltaic power generation assembly 111 may be connected to the energy storage assembly 112 and store the converted electrical energy within the energy storage assembly 112. In some embodiments, the energy storage assembly 112 may be coupled to a voltage conversion assembly such that the voltage conversion assembly outputs electrical energy at a desired voltage.
In some embodiments, the monitoring component 120 may be used to monitor the operating state of the photovoltaic device 100 or other device, such as the first monitoring data and the second monitoring data. In some embodiments, the monitoring component 120 can be used to obtain external environmental parameters, such as weather data, geographic features, and the like. In some embodiments, monitoring component 120 may also be configured to obtain an operating state of the powered device, such as historical power usage data of the powered device, a parameter of the powered device, and the like. Correspondingly, in some embodiments, the monitoring component 120 may include an electricity meter, a device detector, a hygrothermograph, a locator, and the like. For more details on the first and second monitoring data, reference may be made to fig. 2 and the related description below.
In some embodiments, the monitoring component 120 can include an image acquisition device and a dust sensor.
Wherein the image acquisition device may be used to acquire an image of the photovoltaic apparatus 100, which may reflect the degree of cleanliness of the photovoltaic apparatus 100. The dust sensor may be used to detect a dust condition on the power generation device 110 that may reflect the cleanliness of the photovoltaic apparatus 100.
In some embodiments, the blower 130 may be configured to blow air to the power generator 110 to clean dust on the power generator 110 and increase the power generation efficiency of the power generator 110. For example, the blower 130 may include an electric fan, a blower, or the like.
Processor 140 may refer to the operational and control core of photovoltaic device 100, which is the final execution unit for information processing, program execution. Such as a central processing unit, a graphics processor, a field programmable gate array, etc.
In some embodiments, the processor 140 may be connected to the power generation device 110, the monitoring assembly 120, and the blower device 130, respectively, via a network to control the operating states of the respective devices. For example, processor 140 may control an operating state of at least one of power generation device 110 and monitoring assembly 120 to power the powered device based on the control instructions. As another example, the processor 140 may drive the blower 130 to operate according to the blower parameters.
The powered device may be a device that consumes electrical energy. In some embodiments, the powered device may include a grid load (e.g., a consumer unit that consumes electricity, etc.), a mobile charging device (e.g., a charger, a battery, etc.), and so forth.
In some embodiments, the photovoltaic device 100 may also include a solar charging stake, which may be used to charge new energy vehicles, drones, and the like.
It should be noted that the above description of the photovoltaic device and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the monitoring component 120 and the processor 140 disclosed in fig. 1 may be different modules in a system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
Fig. 2 is an exemplary flow chart of a photovoltaic device control method according to some embodiments of the present description. In some embodiments, the process 200 may be performed by the aforementioned processor 140.
As shown in fig. 2, the process 200 includes the steps of:
step 210, determining a power generation characteristic of the photovoltaic device based on the first monitoring data.
The first monitoring data may be data reflecting an operating environment of the photovoltaic device. In some embodiments, the first monitoring data may include at least one of weather data, geographic feature data. The cleaning degree of the photovoltaic equipment can be influenced by weather data, and the cleaning degree of the photovoltaic equipment is good after raining. The geographic characteristic data may be data reflecting an area in which the photovoltaic device is located, which may affect a cleaning state of the photovoltaic device, such as a cleaning degree of the photovoltaic device in a desert area being worse than a cleaning degree of the photovoltaic device in a grassland area.
In some embodiments, the processor may obtain the first monitoring data through the monitoring component. For example, the processor may establish communication with a base station where the photovoltaic device is located through a network, and acquire geographic feature data of the photovoltaic device, weather data within a preset time period, and the like. For more details of the monitoring assembly, reference may be made to FIG. 1 and its associated description.
The power generation characteristics may be used to reflect the current or future power generation status of the photovoltaic device. In some embodiments, the power generation characteristic may include a sequence of power generation for a preset period of time. The power generation sequence may include at least one time in the future and the power generation or power generation of the corresponding predicted photovoltaic device.
In some embodiments, the processor may determine the power generation characteristics in a variety of ways. Such as lookup tables, vector library matches, etc. For example only, the processor may query a preset, previously established power generation table for power generation characteristics of the photovoltaic device corresponding thereto based on the first monitoring data.
In some embodiments, the processor may also determine the power generation characteristics through a power generation predictive model, see FIG. 3 and its associated description for further details.
Step 220, determining the electricity utilization characteristic of the electric equipment connected to the photovoltaic equipment based on the second monitoring data.
The second monitoring data may reflect a historical power usage status of the powered device. In some embodiments, the second monitoring data may include at least one of a historical power usage characteristic and a current power usage characteristic of the powered device. The historical electricity utilization characteristic and the current electricity utilization characteristic can comprise electricity utilization sequences of the electric equipment in preset time. The electricity consumption sequence may include electricity consumption or electricity consumption power (i.e., electricity consumption power) of the photovoltaic device and an electricity consumption time corresponding thereto.
In some embodiments, the processor may obtain the second monitoring data through the monitoring component. For example, the processor may obtain historical electricity usage of the consumer over a plurality of time periods via the electricity meter to determine historical electricity usage characteristics of the consumer.
The electricity utilization characteristics of the electric equipment connected with the photovoltaic equipment can reflect the electricity utilization state of the electric equipment in a certain time period. In some embodiments, the power usage characteristics may include a predicted power usage sequence of the powered device over a current time period or a future preset time period. The electricity consumption sequence may include predicted electricity consumption or electricity consumption power of the electric equipment and the corresponding electricity consumption time. For more details on powered devices, reference may be made to fig. 1 and the associated description.
In some embodiments, the processor may determine the power usage characteristics in a variety of ways. Such as lookup tables, vector library matches, etc. For example only, the processor may match similar vectors in a preset electric device vector library established in advance based on the vectors corresponding to the second monitoring data, thereby determining electric characteristics corresponding to the similar vectors, and using the electric characteristics as electric devices connected to the photovoltaic device.
In some embodiments, the processor may determine the power usage characteristics by a power usage prediction model, see FIG. 4 and its associated description for further details.
Step 230, determining a power supply strategy of the photovoltaic device based on the power generation characteristic and the power usage characteristic.
The power supply strategy may be a strategy that directs the distribution of electrical energy over a certain period of time in the future. In some embodiments, the power supply strategy may include power distribution characteristics of the photovoltaic device during the energy-surplus phase, such as the power supplied to the powered device, and the stored power stored to the energy storage assembly; and the electricity buying characteristics of the photovoltaic equipment in the energy deficiency stage, such as whether electricity buying is needed or not, and electricity buying power, wherein the electricity buying power refers to the electric quantity required to be bought from a power grid in unit time. The energy-surplus phase can be a phase that the generated energy of the photovoltaic equipment is larger than the power consumption of the electric equipment, and otherwise, the energy-shortage phase is the energy-shortage phase.
Correspondingly, in some embodiments, the photovoltaic device is in an energy-surplus phase, and the energy storage component can be charged; and if the energy storage component is full in electric quantity, redundant power generation electric energy of the photovoltaic equipment can be input to a power grid to obtain income. In some embodiments, the photovoltaic device is in the energy-deficient stage, and the electric device can be powered by the electric quantity stored by the energy storage component; if the energy storage component is exhausted, electric energy can be purchased from the power grid to meet the requirement that the electric equipment can consume electricity at the moment, and expenditure is generated. For more details of the energy storage assembly, reference may be made to fig. 1 and the associated description.
In some embodiments, the processor may determine the target power supply policy in a variety of ways. For example by consulting a reference power policy table. Wherein the reference power policy table may be pre-established by the processor based on historical power data. The reference power supply strategy table comprises scene characteristics and corresponding reference power supply strategies. Wherein, scene characteristics may include: the power generation characteristics of the photovoltaic equipment at the current moment or the future moment, the power utilization characteristics of the electric equipment and the electricity price. The processor may determine the target power supply policy based on matching scene features in the reference power supply policy table.
In some embodiments, the processor may obtain at least one candidate power supply strategy, determine an evaluation value of the at least one candidate power supply strategy based on the power usage characteristics and the power generation characteristics, and determine a target power supply strategy from the at least one candidate power supply strategy based on the evaluation value.
In some embodiments, the processor may generate the at least one candidate power supply policy in a variety of ways. For example, randomly generated based on scene features, or generated based on historical data, etc.
In some embodiments, the evaluation value of the candidate power supply strategy may be affected by the purchase cost and the amount of power supplied after the power supply strategy is implemented. For example, the lower the purchase cost after the power supply strategy is implemented, the higher the evaluation value, and for example, the larger the power supply amount after the power supply strategy is implemented, the higher the evaluation value.
In some alternative embodiments, the processor may determine the evaluation value based on a purchase cost, which may be inversely proportional to the evaluation value. In some alternative embodiments, the processor may take as an evaluation a ratio of the amount of power supplied and the cost of purchasing power. In some alternative embodiments, the processor may weight the purchase cost and the power supply amount to obtain the evaluation value of the candidate power supply policy, where the power supply amount weight is positive, the purchase cost weight is negative, and the specific value of the weight may be preset or determined by acquiring a manual input.
In some embodiments, the processor may determine the evaluation value of each candidate power supply strategy by simulating the purchase cost and the amount of power supplied by executing the candidate power supply strategy for a preset period of time (e.g., within 24 hours in the future) based on the candidate power supply strategy. The preset time period may be set manually.
For example, at each time during the simulation, the processor may determine the power supplied and stored, and whether to purchase and purchase power, and continue the simulation based on the power characteristics, power generation characteristics, and candidate power supply strategies at that time or at a future time. When the simulation is completed, the processor can count the buying power in the simulation process based on the monitoring data in the simulation process, and then determine the buying cost based on the electricity price; and determining the power supply quantity of the electric equipment based on the monitoring data in the simulation process, and determining the evaluation value of the candidate power supply strategy based on the electricity buying cost and the power supply quantity.
The processor can simulate generation of a photovoltaic device in the simulation system, and different generation powers are given to the photovoltaic device at different moments according to the predicted generation characteristics, so that the generation characteristics of the photovoltaic device at future moments are simulated. The processor can also generate a charging event in the simulation system (for example, simulate an electric vehicle to charge), and ensure that the generation of the charging event is matched with the predicted electricity utilization characteristic, so as to simulate the electricity utilization characteristic of the electric equipment at the future moment.
For more details regarding predicted power generation characteristics and predicted power usage characteristics, see figures 3-4 and their associated description herein.
In some embodiments, the processor may select a candidate power supply policy having an optimal evaluation value from among a plurality of candidate power supply policies as the target power supply policy based on the evaluation value. That is, the processor may select the candidate power supply strategy with the lowest purchase cost (or the lowest ratio of purchase cost to power supply amount) as the target power supply strategy.
In the embodiment of the specification, the candidate power supply strategy with the optimal evaluation value is selected through simulation as the target power supply strategy, so that the electricity buying cost and the power supply quantity can be measured, the energy efficiency distribution management of the photovoltaic equipment is more balanced, and the stability of a power grid is ensured.
Step 240, determining a control command based on the power supply strategy, and controlling an operating state of at least one of the power generation device and the monitoring component based on the control command.
The control instructions may be instructions that the processor transmits to other components in the photovoltaic device so that the photovoltaic device may perform power distribution based on the power supply strategy. In some embodiments, the control instructions may include generated power, stored power, etc. of the power generation device, operating time of the monitoring component, etc.
In some embodiments, the operating state of the power generation device may include an operating state of the energy storage component (e.g., energy storage or discharging), an operating state of the photovoltaic power generation component (e.g., electrical energy output to the powered device or the energy storage component), and so on. In some embodiments, the operating state of the monitoring component may include on/off of the monitoring component, as well as a monitored object, a monitored time, etc. when the monitoring component is on. For more details of the power generation device and monitoring assembly, reference is made to FIG. 1 and its associated description.
For example, the processor may adjust whether the energy storage assembly is storing energy or discharging energy based on the control instruction, and control the photovoltaic power generation assembly to output electrical energy to the electrical device or the energy storage assembly, so that the photovoltaic device may generate power according to the power supply strategy. Or the processor can control the monitoring component to monitor the power generation device so as to ensure the normal operation of the power generation device.
In some embodiments, the processor may determine the blowing parameters of the blowing device from dust data of the photovoltaic power generation assembly.
The dust data may reflect the dust level at the surface of the photovoltaic power module, which may affect the cleanliness of the photovoltaic power module and the energy conversion. In some embodiments, the dust data may include dust location, dust level (or dust thickness), dust type, and the like. For example, the higher the dust level, the lower the cleanliness of the photovoltaic power generation module, the worse the energy conversion efficiency of the photovoltaic power generation module, and the lower the power generation amount of the power generation device.
In some embodiments, the processor may acquire an image of the photovoltaic power generation component using the image acquisition device, identify the image using feature extraction, and determine dust data for the surface of the photovoltaic power generation component; dust data on the surface of the photovoltaic power generation assembly can be collected by using a dust sensor. Correspondingly, the processor can determine the dirt degree of the photovoltaic power generation assembly according to the dust data so as to clean the power generation assembly in time.
In some embodiments, the processor may also periodically acquire dust data and timely clean dust of the photovoltaic power generation assembly, thereby ensuring energy conversion efficiency of the photovoltaic power generation assembly. More details of the image acquisition device and dust sensor are seen in fig. 1.
In some embodiments, the blowing parameters may include a combination of one or more parameters of a blowing time, a blowing power, a blowing direction, etc. of the blowing device.
In some embodiments, the processor may determine the blowing parameters of the blowing device from the dust data based on each photovoltaic power module by various means such as look-up table, vector library matching, and the like. For example, when the dust level of a certain photovoltaic power generation component reaches the dust threshold, the processor may determine the blowing direction of the blowing device according to the dust position, and then query the blowing time and the blowing wind level of the corresponding blowing device based on the dust level and the dust type by using a look-up table, so that the processor may drive the blowing device to perform blowing based on the foregoing blowing parameters (such as the blowing time, the blowing wind level, the blowing direction, etc.).
In the embodiment of the specification, the dust data of the photovoltaic power generation assembly is monitored, and the air blowing device is driven to blow air at regular time, so that dust on the photovoltaic power generation assembly is blown away in time, and the energy conversion efficiency of the photovoltaic power generation assembly is ensured.
FIG. 3 is a schematic illustration of determining a power generation characteristic, shown in accordance with some embodiments of the present description.
In some embodiments, as shown in fig. 3, the processor may obtain the power generation characteristics 350 from the power generation prediction model 340 based on the weather data 310, the geographic feature data 320, and the photovoltaic device parameters 330, the power generation prediction model 340 being a machine learning model.
The photovoltaic device parameters may reflect the capability range of the photovoltaic device under normal operating conditions. In some embodiments, photovoltaic device parameters 330 may include one or more parameters of the number of photovoltaic devices, peak power, peak voltage, operating temperature, and the like.
In some embodiments, the processor may obtain the photovoltaic device parameters 330 by obtaining manual inputs, networking obtains, or the like. For more details on weather data 310 and geographic feature data 320, reference is made to FIG. 2 and its associated description.
In some embodiments, the power generation prediction model 340 may be a machine learning model, such as a neural network model (Neural Networks, NN). In some embodiments, the power generation predictive model may also be other models capable of performing the same function.
In some embodiments, the processor may output the power generation characteristics 350 of the photovoltaic device using the weather data 310, the geographic feature data 320, and the photovoltaic device parameters 330 as inputs to the power generation prediction model 340. The weather data 310 may include wind power characteristics, temperature characteristic data, humidity characteristic data, and the like at a plurality of historical moments, the geographic characteristic data 320 may include air dust concentrations, and the like at a plurality of historical moments, and the photovoltaic device parameters 330 may include air blast parameters, and the like at a plurality of historical moments.
In some embodiments, the weather data 310, the geographic feature data 320, and the photovoltaic device parameters 330 for the plurality of historical time instants may be in a sequence, respectively. Taking the weather data 310 as an example, the weather data 310 may include a sequence of temperature characteristic data and humidity characteristic data each hour of 3 hours before the current time, such as weather dataWherein the first row, the second row and the third row respectively represent temperature characteristic data X and humidity characteristic data Y of different historical moments t.
In some embodiments, as shown in fig. 3, the input to the power generation prediction model 340 may also include the degree of cleanliness 360 of the photovoltaic power generation module.
In some embodiments, the degree of cleanliness 360 of the photovoltaic power generation module may be used to reflect the power generation efficiency of the photovoltaic power generation module, e.g., the higher the degree of cleanliness 360, the higher the power generation efficiency of the photovoltaic power generation module. In some embodiments, the cleanliness level 360 of the photovoltaic power module may also be affected by dust data of the photovoltaic power module, e.g., the greater the dust level, the weaker the cleanliness level 360 of the photovoltaic power module.
In some embodiments, the processor determines the degree of cleanliness 360 of the photovoltaic power module based on the dust degree in inverse relationship to the degree of cleanliness 360 of the photovoltaic power module from the collected dust data. In some alternative embodiments, the processor may also acquire an image of the photovoltaic power module using the image acquisition device and identify the image using feature extraction to determine the cleanliness 360 of the photovoltaic power module. For more details of the image acquisition apparatus, reference is made to fig. 1 and the associated description.
In some embodiments of the present disclosure, considering the influence of the cleanliness 360 of the photovoltaic power generation module on the power generation efficiency, by adding the cleanliness 360 of the photovoltaic power generation module to the model input, the accuracy of the predicted power generation characteristics 350 of the photovoltaic power generation module can be improved, so that the power distribution can be performed more accurately.
In some embodiments, the power generation predictive model 340 may be trained based on a large number of labeled training samples. In some embodiments, the training samples may include a plurality of weather data samples, geographic feature data samples, and photovoltaic device parameter samples at a first historical time, and may further include a plurality of cleanliness samples of the photovoltaic power generation module at the first historical time, the training samples may be obtained by retrieving the weather data, the geographic feature data, the photovoltaic device parameter, and the cleanliness of the photovoltaic power generation module at the first historical time in the database. In some embodiments, the tag may be a power generation feature of the photovoltaic device at the second historical time, the tag may be obtained by calling a historical power generation feature corresponding to a plurality of second historical times, or may be obtained by manually labeling the photovoltaic device. Wherein the second historical time is later than the first historical time.
In the embodiment of the present disclosure, the power generation characteristics 350 of the photovoltaic device can be obtained quickly by predicting the power generation prediction model 340 based on the parameters of the photovoltaic device and the working environment (such as the weather data 310 and the geographic feature data 320) thereof, so as to improve the efficiency of the subsequent power distribution management.
In some embodiments, the processor may obtain a predicted cleanliness level of the photovoltaic power generation module at a future time, and obtain a predicted value of the power generation characteristic at the future time based on the predicted cleanliness level.
In some embodiments, the processor may obtain the predicted cleanliness of the photovoltaic power generation component at the future time of the power generation feature by looking up a table, matching a vector library, and other manners based on parameters such as dust data, weather data samples, and the like of the photovoltaic power generation component at a plurality of historical times. Wherein the table and vector library may be determined for the processor based on the cleanliness levels of the photovoltaic power generation module at a plurality of historical moments.
In some embodiments, the power generation prediction model 340 may include a clean prediction layer and a power generation prediction layer. Correspondingly, the processor can acquire the predicted cleaning degree of the photovoltaic power generation component at the future moment through the cleaning prediction layer 341 based on the cleaning degree, the air dust concentration, the wind power characteristic, the temperature characteristic data and the air blast parameter of the photovoltaic power generation component at a plurality of historical moments; and acquiring power generation characteristics through a power generation prediction layer based on the weather data, the geographic characteristic data, the photovoltaic equipment parameters and the predicted cleanliness.
The clean prediction layer and the power generation prediction layer may be machine learning models, such as neural network models (Neural Networks, NN), among others. In some embodiments, the power generation predictive model may also be other models capable of performing the same function.
In some embodiments, the processor may take as input to the cleaning prediction layer 341 the cleaning degree of the photovoltaic power generation module, the air dust concentration, the wind characteristics, the temperature characteristic data, and the blowing parameters for a plurality of historical time instants to obtain a predicted cleaning degree of the photovoltaic power generation module at a future time instant.
In some embodiments, the air dust concentration, wind characteristics, and blowing parameters at the historical time may affect the cleanliness of the photovoltaic power module at future times. For example, the greater the concentration of airborne dust, the more likely the dust will fall onto the surface of the photovoltaic power module, and the less clean the predicted future time will be. As another example, the greater the wind characteristics and/or the greater the blowing parameters, the more easily dust on the surface of the photovoltaic power module is blown off, and the greater the degree of cleanliness predicted for future times. For more details on the degree of cleanliness of the photovoltaic power module, see the relevant description above.
In some embodiments, the processor may obtain the cleaning degree, the air dust concentration, the wind power characteristic, the temperature characteristic data and the air blast parameter of the photovoltaic power generation component at a plurality of historical moments through a network, or through a plurality of modes such as manual input.
In some embodiments, the clean prediction layer may be trained based on a large number of labeled training samples. In some embodiments, the training samples may include a plurality of samples of cleanliness of the photovoltaic power generation module at the first historical time, an air dust concentration sample, a wind characteristic sample, a temperature characteristic data sample, and a blast parameter sample, and the training samples may be obtained by retrieving the plurality of samples of cleanliness, air dust concentration, wind characteristic, and temperature characteristic data at the first historical time in the database. In some embodiments, the label may be a sample of the cleanliness of the photovoltaic power module at the second historical time, and the label may be obtained by calling historical cleanliness corresponding to a plurality of second historical times, or may be obtained by manually labeling the photovoltaic power module. Wherein the second historical time is later than the first historical time.
In some embodiments, the processor may take weather data, geographic feature data, photovoltaic device parameters, and predicted cleanliness as inputs to the power generation prediction layer 342 to obtain predicted values of power generation characteristics at future times. Wherein the processor may input a predicted cleanliness level of the clean prediction layer output as part of the power generation prediction layer 342 to obtain a predicted value of the power generation characteristic at a future time.
In some embodiments, the power generation prediction layer may be trained based on a number of labeled training samples. The training of the power generation prediction layer is similar to the training of the power generation prediction model described above, and for further details reference is made to the power generation prediction model described above and its associated description.
In the embodiment of the specification, the trained clean prediction layer is arranged, so that the predicted clean degree can be quickly obtained to serve as the input of the power generation prediction layer, the trained power generation prediction layer is used for prediction, the predicted value of the power generation characteristic of the photovoltaic equipment can be quickly and accurately obtained, and the efficiency of subsequent power distribution management is further improved.
FIG. 4 is a schematic diagram illustrating determining electrical characteristics according to some embodiments of the present description.
In some embodiments, as shown in fig. 4, the processor may determine the power usage characteristics 450 based on the historical power usage characteristics 410, the current power usage characteristics 420, and the powered device data 430 by a power usage prediction model 440, the power usage prediction model 440 being a machine learning model.
In some embodiments, historical power usage characteristics 410 may be power usage characteristics of the powered device over a past time period or a past time, and current power usage characteristics 420 may be power usage characteristics of the powered device at the current time. The powered device data may reflect a range of capabilities of the powered device in a normal operating state. In some embodiments, powered device data 430 may include one or more parameters of a number of powered devices currently connected to the photovoltaic device, peak power, peak voltage, device usage power, operating temperature, and the like. The power usage characteristics 450 may be power usage characteristics of the powered device at a future time period or future time.
In some embodiments, the processor may obtain historical electricity usage characteristics 410, current electricity usage characteristics 420, and electricity usage data 430 in a variety of ways, such as by manual input, a network, and the like. For more details on the historical electricity usage characteristics 410, the current electricity usage characteristics 420, reference may be made to FIG. 2 and the associated description.
In some embodiments, the power usage prediction model 440 may be a machine learning model, such as a neural network model (Neural Networks, NN). In some embodiments, the power generation predictive model may also be other models capable of performing the same function.
In some embodiments, the processor may output the power usage characteristics 450 of the powered device using the historical power usage characteristics 410, the current power usage characteristics 420, and the powered device data 430 as inputs to the power usage prediction model 440. The power utilization feature 450 of the powered device may include a power utilization sequence at a future point in time, and for more details of the sequence, reference is made to fig. 3 and the description thereof.
In some embodiments, when the photovoltaic device includes a solar charging stake, the input to the electrical signature prediction model 440 also includes traffic flow data.
The traffic flow data may be used to reflect the number of vehicles that access the photovoltaic device in a unit time period, which may affect the powered characteristics 450 of the powered device. Illustratively, the larger the traffic data, the greater the power usage of the powered device. In some embodiments, the traffic data may include traffic data at a plurality of historical times and/or traffic data at a current time.
In some embodiments, the processor may obtain the traffic data via a network, or by human input, or the like. In some alternative embodiments, the processor may also monitor the vehicle flow data of the electric vehicle via a sensor disposed at the interface.
In some embodiments, the power usage prediction model 440 may be trained based on a number of labeled training samples. In some embodiments, the training samples may include historical electricity usage feature samples, current electricity usage feature samples, and electricity usage device data samples, and may also include a plurality of first historical time-of-day traffic flow data. The training samples may be obtained by retrieving power usage status of the powered device for a plurality of first historical time periods in the database. In some embodiments, the tag may be a sample of the electrical characteristics of the photovoltaic device at the second historical time, and the tag may be obtained by calling the electrical characteristics corresponding to the second historical times, or may be obtained by manually labeling the photovoltaic device. Wherein the second historical time is later than the first historical time.
In the embodiment of the present disclosure, based on the historical electricity utilization characteristics, the current electricity utilization characteristics and the electricity utilization device data, the trained electricity utilization prediction model 440 is utilized to predict, so that the electricity utilization characteristics 450 of the electricity utilization device can be obtained quickly, and the efficiency of the subsequent electric energy distribution management is improved.
Fig. 5 is a block diagram of a photovoltaic device control system according to some embodiments of the present description.
As shown in fig. 5, the photovoltaic device control system 500 may include a first feature determination module 510, a second feature determination module 520, a policy determination module 530, and a control module 540.
In some embodiments, the first characteristic determination module 510 may be configured to determine a power generation characteristic of the photovoltaic device based on the first monitoring data.
In some embodiments, the first monitoring data includes at least one of weather data, geographic feature data.
In some embodiments, the first feature determination module 510 is further to: based on weather data, geographic feature data and photovoltaic equipment parameters, the power generation features are obtained through a power generation prediction model, and the power generation prediction model is a machine learning model.
In some embodiments, the second characteristic determination module 520 may be configured to determine an electrical characteristic of the powered device accessing the photovoltaic device based on the second monitoring data.
In some embodiments, the second monitoring data includes at least one of a historical power usage characteristic and a current power usage characteristic of the powered device.
In some embodiments, the second feature determination module 520 is further to: based on the historical electricity utilization characteristics, the current electricity utilization characteristics and the electric equipment data, determining the electricity utilization characteristics through an electricity utilization prediction model, wherein the electricity utilization prediction model is a machine learning model.
In some embodiments, the policy determination module 530 may be configured to determine a power supply policy for the photovoltaic device based on the power generation characteristics and the power usage characteristics.
In some embodiments, the policy determination module 530 is further to: acquiring at least one candidate power supply strategy; determining an evaluation value of at least one candidate power supply strategy based on the power utilization characteristic and the power generation characteristic; a target power supply strategy is determined from the at least one candidate power supply strategy based on the evaluation value.
In some embodiments, the control module 540 may be configured to determine control instructions based on a power supply strategy and control an operating state of at least one of the power generation device and the monitoring component based on the control instructions.
For further details on the first feature determination module 510, the second feature determination module 520, the policy determination module 530, the control module 540, and their functions, see fig. 1-4 and their associated descriptions of this specification.
Some embodiments of the present disclosure further provide a computer readable storage medium storing computer instructions, where when the computer reads the computer instructions in the storage medium, the computer performs the functions corresponding to the photovoltaic device control system.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, 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 (8)
1. A photovoltaic device, characterized in that the photovoltaic device comprises a power generation device, a monitoring assembly, a blowing device and a processor;
The power generation device at least comprises a photovoltaic power generation assembly and an energy storage assembly, and the air blowing device is used for blowing air to the power generation device;
The monitoring component is used for acquiring first monitoring data and second monitoring data;
The first monitoring data comprises at least one of weather data and geographic feature data;
the power generation device, the monitoring assembly and the blower device are in communication with the processor, the processor being configured to:
Determining a power generation characteristic of the photovoltaic device based on the first monitoring data; the method comprises the following steps:
Determining a power generation characteristic of the photovoltaic equipment through a power generation prediction model, wherein the power generation prediction model is a machine learning model, the power generation prediction model comprises a cleaning prediction layer and a power generation prediction layer, the input of the cleaning prediction layer comprises cleaning degrees, air dust concentrations, wind power characteristics, temperature characteristic data and blowing parameters of the photovoltaic power generation component at a plurality of historical moments, the output of the cleaning prediction layer comprises the predicted cleaning degrees of the photovoltaic power generation component at future moments, the input of the power generation prediction layer comprises the weather data, the geographic characteristic data, the photovoltaic equipment parameters and the predicted cleaning degrees, and the output of the power generation prediction layer comprises the power generation characteristics;
Determining the electricity utilization characteristics of electric equipment connected to the photovoltaic equipment based on the second monitoring data;
Determining a power supply strategy of the photovoltaic device based on the power generation characteristic and the power utilization characteristic;
Based on the power supply strategy, a control command is determined, and based on the control command, an operating state of at least one of the power generation device and the monitoring component is controlled.
2. The photovoltaic device of claim 1, further comprising a solar charging peg for charging a new energy vehicle.
3. The photovoltaic device of claim 1, wherein the second monitoring data includes at least one of historical power usage characteristics and current power usage characteristics of the powered device;
The processor is further configured to:
And determining the electricity utilization characteristics through an electricity utilization prediction model based on the historical electricity utilization characteristics, the current electricity utilization characteristics and the electricity utilization equipment data, wherein the electricity utilization prediction model is a machine learning model.
4. The photovoltaic device of claim 1, wherein the processor is further configured to:
acquiring at least one candidate power supply strategy;
determining an evaluation value of the at least one candidate power supply strategy based on the power utilization characteristic and the power generation characteristic;
A target power supply strategy is determined from the at least one candidate power supply strategy based on the evaluation value.
5. The photovoltaic equipment control system is characterized by comprising a first characteristic determining module, a second characteristic determining module, a strategy determining module and a control module;
The photovoltaic equipment comprises a power generation device, a blowing device and a monitoring assembly;
The power generation device at least comprises a photovoltaic power generation assembly and an energy storage assembly, and the air blowing device is used for blowing air to the power generation device; the monitoring component is used for acquiring first monitoring data and second monitoring data; the first characteristic determining module is used for determining the power generation characteristic of the photovoltaic equipment based on the first monitoring data, and the first monitoring data comprises at least one of weather data and geographic characteristic data; the first feature determination module is further to:
Determining a power generation characteristic of the photovoltaic equipment through a power generation prediction model, wherein the power generation prediction model is a machine learning model, the power generation prediction model comprises a cleaning prediction layer and a power generation prediction layer, the input of the cleaning prediction layer comprises cleaning degrees, air dust concentrations, wind power characteristics, temperature characteristic data and blowing parameters of the photovoltaic power generation component at a plurality of historical moments, the output of the cleaning prediction layer comprises the predicted cleaning degrees of the photovoltaic power generation component at future moments, the input of the power generation prediction layer comprises the weather data, the geographic characteristic data, the photovoltaic equipment parameters and the predicted cleaning degrees, and the output of the power generation prediction layer comprises the power generation characteristics;
The second characteristic determining module is used for determining the electricity utilization characteristic of electric equipment connected to the photovoltaic equipment based on the second monitoring data;
The strategy determining module is used for determining a power supply strategy of the photovoltaic equipment based on the power generation characteristics and the power utilization characteristics;
The control module is used for determining a control instruction based on the power supply strategy and controlling the working state of at least one of the power generation device and the monitoring component based on the control instruction.
6. The photovoltaic device control system of claim 5, wherein the second monitoring data includes at least one of a historical power usage characteristic and a current power usage characteristic of the powered device;
The second feature determination module is further to:
And determining the electricity utilization characteristics through an electricity utilization prediction model based on the historical electricity utilization characteristics, the current electricity utilization characteristics and the electricity utilization equipment data, wherein the electricity utilization prediction model is a machine learning model.
7. The photovoltaic device control system of claim 5, wherein the policy determination module is further to:
acquiring at least one candidate power supply strategy;
determining an evaluation value of the at least one candidate power supply strategy based on the power utilization characteristic and the power generation characteristic;
A target power supply strategy is determined from the at least one candidate power supply strategy based on the evaluation value.
8. A computer readable storage medium storing computer instructions, which when read by a computer in the storage medium, the computer performs the functions corresponding to the photovoltaic device control system of any one of claims 5 to 7.
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