Photovoltaic energy storage management system and method based on cloud computing

Abstract

The present invention relates to the technical field of photovoltaic power generation and energy storage management, and discloses a photovoltaic energy storage management system and method based on cloud computing. The system comprises an energy storage battery pack optimal charging voltage evaluation module, an energy storage battery pack optimal DC boost module parameter analysis module, and an energy storage battery pack charging control module. The system autonomously and efficiently performs energy storage battery pack charging simulation operations based on scientifically preset battery pack test charging voltage parameters through a voltage regulating device and a simulation power supply, thereby achieving scientific and safe execution of the energy storage battery pack charging simulation operations. Based on cloud computing, the optimal charging voltage parameters of the energy storage battery pack are scientifically and intelligently analyzed according to the energy storage battery pack test charging voltage parameters, the energy storage battery pack charging test real-time electric power parameters combined with an intelligent search algorithm, thereby improving the charging voltage matching accuracy of the energy storage battery pack in the charging state, and improving the charging efficiency and service life of the photovoltaic energy storage battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation and energy storage management, and in particular to a photovoltaic energy storage management system and method based on cloud computing. Background Art

[0002] Photovoltaic power generation is a technology that uses photosensitive semiconductors to directly convert sunlight into electricity. It primarily consists of solar panels, controllers and inverters, and energy storage batteries, with the primary components being electronic components. Solar cells are connected in series and then encapsulated and protected to form large-area solar modules. Combined with components such as power controllers, this creates a photovoltaic power generation device. During the photovoltaic power generation process, when the generated power exceeds the load power and the energy storage battery is not fully charged, photovoltaic charging and energy storage management are required. However, existing photovoltaic charging and energy storage management systems cannot accurately measure the energy storage battery's real-time optimal charging voltage, nor can they scientifically match the specifications of the DC boost module in the photovoltaic energy storage inverter based on the energy storage battery's real-time optimal charging voltage and the solar panel's real-time generated voltage, compromising the safety and reliability of photovoltaic energy storage management.

[0003] A Chinese invention patent application, publication number CN104092278A, discloses an energy management method for a photovoltaic energy storage system. The power sources in the photovoltaic energy storage system include photovoltaic modules, lithium batteries, and a power grid. The system implements operating conditions 1-4, islanding operation modes, based on four different photovoltaic power generation operating conditions, to achieve intelligent management of photovoltaic energy storage. However, in operating condition 1, islanding operation mode, the photovoltaic energy storage management system cannot accurately assess the real-time optimal charging voltage of the lithium battery, nor can it accurately match the specifications of the DC boost module in the energy storage inverter based on the real-time optimal charging voltage of the lithium battery and the real-time power generation voltage of the photovoltaic module, thereby reducing the charging efficiency and service life of the lithium battery. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In order to solve the problem that the above-mentioned existing photovoltaic charging energy storage management system cannot accurately measure the real-time optimal charging voltage of the energy storage battery group, and cannot scientifically match the specifications and types of the DC boost module in the energy storage inverter of the photovoltaic energy storage process based on the real-time optimal charging voltage of the energy storage battery group and the real-time power generation voltage of the solar panel, thereby reducing the safety and reliability of photovoltaic energy storage management, the above-mentioned scientific measurement of the real-time optimal charging voltage parameters of the energy storage battery group, intelligent analysis of the optimal specifications and types of the DC boost module of the energy storage inverter required for the charging process of the energy storage battery group, safe and efficient execution of the energy storage battery group charging operation, and improvement of the safety and quality of photovoltaic energy storage management are achieved.

[0006] (2) Technical solution

[0007] The present invention is implemented through the following technical solution: a photovoltaic energy storage management method, the method comprising the following steps:

[0008] S1. Execute a charging simulation operation of the energy storage battery pack according to the test charging voltage data of the energy storage battery pack;

[0009] S2. Collect real-time electric power data of energy storage battery pack charging test;

[0010] S3, based on the energy storage battery pack test charging voltage data and the energy storage battery pack charging test real-time electric power data, performing identification processing on the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery pack, and generating real-time optimal charging voltage data of the energy storage battery pack;

[0011] S4, collecting real-time power generation voltage data of solar panels;

[0012] S5, performing power generation voltage parameter noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate a standard solar panel real-time power generation voltage mean;

[0013] S6. Performing matching processing on the optimal energy storage inverter DC boost module specification type parameters required for the photovoltaic energy storage charging process of the energy storage battery pack based on the real-time optimal charging voltage data of the energy storage battery pack, the real-time average power generation voltage of the standard solar panel, and the specification type data of the energy storage inverter DC boost module, to generate the real-time optimal DC boost module specification type data of the energy storage battery pack;

[0014] S7: Construct energy storage inverter control data for energy storage battery pack charging operation and execute energy storage battery pack charging operation.

[0015] Preferably, the operation steps of performing the energy storage battery pack charging simulation operation based on the energy storage battery pack test charging voltage data are as follows:

[0016] S11, establish the energy storage battery group test charging voltage data set U=(u1,…,u p ,…,u η ), p=1,2,3,…,eta; where u p represents the pth type of energy storage battery pack test charging voltage data, η represents the maximum number of energy storage battery pack test charging voltage types, u p The unit is volt, and the energy storage battery pack test charging voltage data represents the standard charging test voltage data set for the optimal charging voltage under the charging condition of the energy storage battery pack in the photovoltaic power generation process;

[0017] S12, when the photovoltaic energy storage management platform issues a charging instruction to the energy storage battery group, at this time, the voltage regulating device is combined with the analog power supply according to the energy storage battery group test charging voltage type number in order according to the energy storage battery group test charging voltage data set U p Different charging voltages are applied to the charging end of the energy storage battery pack to perform a charging simulation operation on the energy storage battery pack. The voltage regulating device includes any one of an adjustable voltage and current charger and a voltage regulator.

[0018] Preferably, the steps for collecting real-time electric power data of the energy storage battery pack charging test are as follows:

[0019] S21, during the energy storage battery pack charging simulation operation, synchronously collect the energy storage battery pack charging end by applying different energy storage battery pack test charging voltage data u p The corresponding charging power data is generated, and the real-time power data set of the energy storage battery charging test is generated A=(a1,…,a p ,…,a η ), where a p Indicates the test charging voltage data u of the energy storage battery pack p The corresponding real-time electric power data of the energy storage battery pack charging test, a p The unit is watt.

[0020] Preferably, the optimal charging voltage parameter identification process required for the photovoltaic energy storage charging process of the energy storage battery group is performed based on the energy storage battery group test charging voltage data and the energy storage battery group charging test real-time electric power data, and the operating steps for generating the real-time optimal charging voltage data of the energy storage battery group are as follows:

[0021] S31, the real-time electric power data a of the energy storage battery pack charging test in the real-time electric power data set A of the energy storage battery pack charging test p Perform electric power numerical analysis and search for the real-time electric power data of the energy storage battery pack charging test with the largest electric power value. p , and generate the real-time electric power extreme value a′ of the energy storage battery pack charging test through data identification p , where a′ p The unit is Watt;

[0022] S32, using the BERT language model algorithm to calculate the real-time electric power extreme value a′ of the energy storage battery pack charging test p The energy storage battery pack test charging voltage data u in the energy storage battery pack test charging voltage data set U p Perform energy storage battery pack test charging voltage type character matching to identify the energy storage battery pack charging test real-time electric power extreme value a' pThe corresponding energy storage battery pack test charging voltage data u p , and constructed as the real-time optimal charging voltage data u′ of the energy storage battery pack, where the unit of u′ is volt.

[0023] Preferably, the steps for collecting real-time power generation voltage data of solar panels are as follows:

[0024] S41 , collecting output voltage parameters of the output end of the solar panel online through a voltage sensor and generating a real-time power generation voltage data set B of the solar panel.

[0025] Preferably, the steps of performing power generation voltage parameter noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate the real-time power generation voltage mean of the standard solar panel are as follows:

[0026] S51, using an adaptive filtering method to perform voltage data noise reduction preprocessing on the real-time power generation voltage data of the solar panel in the real-time power generation voltage data set B, to generate a standard solar panel real-time power generation voltage data set B'=(b'1, ..., b' o ,…,b' ι ), o=1,2,3,…,ι; where b' o represents the real-time power generation voltage data of the oth standard solar panel, ι represents the maximum value of the real-time power generation voltage of the standard solar panel, b' o The unit of is volt;

[0027] S52, using the mean formula to measure the standard solar panel real-time power generation voltage data b' in the standard solar panel real-time power generation voltage data set B'. o The average value of the real-time power generation voltage of the standard solar panel is generated in The unit is volts,

[0028] Preferably, the steps of performing matching processing on the optimal energy storage inverter DC boost module specification type parameters required for the photovoltaic energy storage charging process of the energy storage battery group according to the real-time optimal charging voltage data of the energy storage battery group, the real-time average power generation voltage of the standard solar panel, and the specification type data of the energy storage inverter DC boost module are as follows:

[0029] S61, establish the energy storage inverter DC boost module specification type data set C = (c1, ..., c k ,…,c λ ), k=1,2,3,…,λ; where c kIndicates the kth type of energy storage inverter DC boost module specification data corresponding to the real-time charging voltage parameters of different types of energy storage battery packs and the power generation voltage parameters of solar panels, where λ represents the maximum number of energy storage inverter DC boost module specification types; the energy storage inverter DC boost module specification type represents the DC / DC circuit voltage regulation specification type used for solar panel DC voltage regulation in the energy storage inverter;

[0030] S62, the real-time optimal charging voltage data u′ of the energy storage battery pack and the real-time average power generation voltage of the standard solar panel The energy storage inverter DC boost module specification type data c in the energy storage inverter DC boost module specification type data set C k According to the character matching of the real-time charging voltage parameter of the energy storage battery group and the power generation voltage parameter of the solar panel, the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel are searched. The corresponding energy storage inverter DC boost module specification type data c k , and construct the real-time optimal DC boost module specification type data c" for energy storage battery pack k , execute to generate the real-time optimal DC boost module specification type data c" of the energy storage battery pack k The specific steps are as follows:

[0031] S621, population initialization, setting the maximum update iteration number T, DC boost module specification type identification wild dog population randomly updates the spatial position in the energy storage inverter DC boost module specification type data set C search space, the spatial position update formula is as follows: Among them, J i It represents the search boundary position of the i-th DC boost module specification type identification wild dog individual in the search space of the energy storage inverter DC boost module specification type data set C, and They respectively represent the upper and lower boundaries of the DC boost module specification type identification of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C, and rand represents a random number between values ​​​​[0,1];

[0032] S622, in the group attack phase, the DC boost module specification type identification wild dog searches for the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel in the search space of the energy storage inverter DC boost module specification type data set C. The matching energy storage inverter DC boost module specification type data c k The prey location and surrounding, search prey simulation behavior formula is as follows: Among them Ji (t+1) represents the new position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the t+1th iteration in the group attack phase, ρ represents a random integer generated in the reverse order of [2, N / 2], where N is the size of the wild dog population; Θ y (t) is the type of DC boost module specification to be attacked to identify the wild dog population subset, y represents the cumulative number of calculations, J i (t) represents the position of the DC boost module specification type identification wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the tth iteration; J * (t) represents the best position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the tth iteration, that is, the best charging voltage data u′ of the energy storage battery group and the average real-time power generation voltage of the standard solar panel are searched in the search space of the energy storage inverter DC boost module specification type data set C. The most matching energy storage inverter DC boost module specification type data c k location; Indicates a random number of the proportional factor used to change the size of the DC boost module specification type to identify the size of the wild dog track and has a value within [-2, 2];

[0033] S623, persecution attack phase, the DC boost module specification type identification wild dog in the energy storage inverter DC boost module specification type data set C search space until it hunts the real-time optimal charging voltage data u' of the energy storage battery group and the real-time power generation voltage average of the standard solar panel The matching energy storage inverter DC boost module specification type data c k The hunting simulation behavior formula is as follows: Among them J' i (t+1) represents the new position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the t+1th iteration in the persecution attack phase; Represents a random number of the scaling factor within the range [-2,2], Represents a random number in the range [-1,1], Represents a random number The exponential parameter, r represents a random number in the interval [1, N], J r(t) represents the position of the wild dog individual r in the search space of the energy storage inverter DC boost module specification type data set C after the t-th iteration by randomly selecting the DC boost module specification type, where i≠r;

[0034] S624, cleaning phase, the cleaning behavior is defined as when the DC boost module specification type identification wild dog walks randomly in the search space of the energy storage inverter DC boost module specification type data set C and finds the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel The matching energy storage inverter DC boost module specification type data c k The formula for the carrion-eating behavior and the cleaning simulation behavior is as follows: where J″ i (t+1) represents the new position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the t+1th iteration in the cleaning phase, Π represents a binary number randomly generated by the algorithm, Π∈{0,1};

[0035] S625: When the algorithm meets the maximum number of iterations T, output the real-time optimal charging voltage data u′ of the energy storage battery pack and the real-time power generation voltage average of the standard solar panel. The most matching energy storage inverter DC boost module specification type data c k ;

[0036] S626, according to the energy storage inverter DC boost module specification type data c in step S625. k Constructing real-time optimal DC boost module specification type data for energy storage battery packs c″ k .

[0037] Preferably, the steps of constructing the energy storage inverter control data for the energy storage battery pack charging operation and executing the energy storage battery pack charging operation are as follows:

[0038] S71, the energy storage battery pack real-time optimal DC boost module specification type data c" k Perform data identification to generate energy storage battery pack charging operation energy storage inverter control data f;

[0039] S72. The photovoltaic energy storage management platform controls the energy storage inverter to perform the energy storage battery group charging operation according to the energy storage inverter control data f for the energy storage battery group charging operation.

[0040] A photovoltaic energy storage management system based on cloud computing, used to implement the photovoltaic energy storage management method, the system includes an energy storage battery pack optimal charging voltage evaluation module, an energy storage battery pack optimal DC boost module parameter analysis module, and an energy storage battery pack charging control module;

[0041] The energy storage battery pack optimal charging voltage evaluation module includes an energy storage battery pack test charging voltage parameter storage unit, an energy storage battery pack charging simulation execution unit, an energy storage battery pack test charging power acquisition unit, and an energy storage battery pack real-time optimal charging voltage identification unit;

[0042] The energy storage battery group test charging voltage parameter storage unit is used to store the energy storage battery group test charging voltage data; the energy storage battery group charging simulation execution unit performs the energy storage battery group charging simulation operation according to the energy storage battery group test charging voltage data through the voltage regulation device combined with the simulated power supply; the energy storage battery group test charging power acquisition unit acquires the real-time electric power data of the energy storage battery group charging test through the power meter; the energy storage battery group real-time optimal charging voltage identification unit identifies the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery group based on cloud computing, and generates the real-time optimal charging voltage data of the energy storage battery group;

[0043] The energy storage battery pack optimal DC boost module parameter analysis module includes a solar panel real-time power generation voltage acquisition unit, a solar panel real-time power generation voltage parameter preprocessing unit, an energy storage inverter DC boost module specification type storage unit, and an energy storage battery pack real-time optimal DC boost module type matching unit;

[0044] The solar panel real-time power generation voltage acquisition unit collects the real-time power generation voltage data of the solar panel through a voltage sensor; the solar panel real-time power generation voltage parameter preprocessing unit performs power generation voltage parameter noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate the standard solar panel real-time power generation voltage mean; the energy storage inverter DC boost module specification type storage unit is used to store the energy storage inverter DC boost module specification type data; the energy storage battery group real-time optimal DC boost module type matching unit performs optimal energy storage inverter DC boost module specification type parameter matching processing required for the photovoltaic energy storage charging process of the energy storage battery group according to the real-time optimal charging voltage data of the energy storage battery group, the standard solar panel real-time power generation voltage mean and the energy storage inverter DC boost module specification type data to generate the real-time optimal DC boost module specification type data of the energy storage battery group;

[0045] The energy storage battery pack charging control module includes an energy storage battery pack charging operation control parameter acquisition unit and an energy storage battery pack charging operation execution unit;

[0046] The energy storage battery group charging operation control parameter acquisition unit is used to construct the energy storage inverter control data for the energy storage battery group charging operation; the energy storage battery group charging operation execution unit, the photovoltaic energy storage management platform controls the energy storage inverter to execute the energy storage battery group charging operation according to the energy storage inverter control data for the energy storage battery group charging operation.

[0047] (3) Beneficial effects

[0048] The present invention provides a photovoltaic energy storage management system and method based on cloud computing. It has the following beneficial effects:

[0049] 1. Through voltage regulation equipment and simulated power supply, the energy storage battery pack charging simulation operation is autonomously and efficiently performed based on scientifically preset battery pack test charging voltage parameters, so as to realize scientific and safe execution of energy storage battery pack charging simulation operation; the power meter is used to dynamically and accurately collect the real-time electric power parameters of the energy storage battery pack charging test, and provide real data for intelligent evaluation of the optimal charging voltage of the energy storage battery pack under the charging state; based on cloud computing, the optimal charging voltage parameters of the energy storage battery pack are scientifically and intelligently analyzed according to the energy storage battery pack test charging voltage parameters, the real-time electric power parameters of the energy storage battery pack charging test and the intelligent search algorithm, which improves the charging voltage matching accuracy of the energy storage battery pack under the charging state, and improves the charging efficiency and service life of the photovoltaic energy storage battery pack.

[0050] 2. The real-time power generation voltage parameters of solar panels are obtained online through voltage sensors and combined with numerical analysis and data noise reduction algorithms to scientifically generate the real-time power generation voltage average of standard solar panels, thereby realizing accurate collection of the real-time power generation voltage of solar panels and improving the quality of photovoltaic energy storage management. Based on cloud computing, the real-time optimal charging voltage parameters of energy storage battery packs, the real-time power generation voltage average of standard solar panels, combined with intelligent recognition algorithms and the specification type parameters of energy storage inverter DC boost module are used to accurately and efficiently match the optimal charging parameters of energy storage battery packs with the specification type parameters of energy storage inverter DC boost module, thereby improving the matching accuracy of the specification type of photovoltaic energy storage battery pack charging DC boost module and improving the intelligence and scientific nature of photovoltaic energy storage management.

[0051] 3. By scientifically constructing the energy storage inverter control parameters for the energy storage battery pack charging operation based on data processing, efficient timing collection of the photovoltaic energy storage battery pack charging operation control parameters is achieved, thereby improving the data collection efficiency of photovoltaic energy storage management; the photovoltaic energy storage management platform is used to autonomously and efficiently execute the energy storage battery pack charging operation based on the energy storage inverter control parameters for the energy storage battery pack charging operation, thereby improving the response speed and reliability of photovoltaic energy storage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A schematic diagram of a photovoltaic energy storage management system based on cloud computing provided by the present invention;

[0053] Figure 2 This is a flow chart of a photovoltaic energy storage management method provided by the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] The embodiments of the photovoltaic energy storage management system and method based on cloud computing are as follows:

[0056] Example 1:

[0057] See also Figure 1 - Figure 2 , a photovoltaic energy storage management method, the method comprising the following steps:

[0058] S1. Execute a charging simulation operation of the energy storage battery pack according to the test charging voltage data of the energy storage battery pack;

[0059] S2. Collect real-time electric power data of energy storage battery pack charging test;

[0060] S3, based on the energy storage battery pack test charging voltage data and the energy storage battery pack charging test real-time electric power data, perform identification and processing of the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery pack, and generate real-time optimal charging voltage data of the energy storage battery pack;

[0061] S4, collecting real-time power generation voltage data of solar panels;

[0062] S5. Performing noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate a standard solar panel real-time power generation voltage mean;

[0063] S6. Performing matching processing on the optimal energy storage inverter DC boost module specification type parameters required for the photovoltaic energy storage charging process of the energy storage battery pack based on the real-time optimal charging voltage data of the energy storage battery pack, the real-time average power generation voltage of the standard solar panel, and the specification type data of the energy storage inverter DC boost module, thereby generating the real-time optimal DC boost module specification type data of the energy storage battery pack;

[0064] S7: Construct energy storage inverter control data for energy storage battery pack charging operation and execute energy storage battery pack charging operation.

[0065] For further information, see Figure 1 - Figure 2 The steps for performing energy storage battery pack charging simulation operations based on the energy storage battery pack test charging voltage data are as follows:

[0066] S11, establish the energy storage battery group test charging voltage data set U=(u1,…,u p ,…,u η ), p=1,2,3,…,eta; where u p represents the pth type of energy storage battery pack test charging voltage data, η represents the maximum number of energy storage battery pack test charging voltage types, u p The unit is volts. The energy storage battery pack test charging voltage data represents the standard charging test voltage data set for the optimal charging voltage under the charging conditions of the energy storage battery pack during the photovoltaic power generation process.

[0067] S12, when the photovoltaic energy storage management platform issues a charging instruction to the energy storage battery group, at this time, the voltage regulating device is combined with the analog power supply according to the energy storage battery group test charging voltage type number in order according to the energy storage battery group test charging voltage data set U. p Different charging voltages are applied to the charging end of the energy storage battery pack to perform a charging simulation operation on the energy storage battery pack. The voltage regulating device includes any one of an adjustable voltage and current charger and a voltage regulator.

[0068] The steps for collecting real-time power data for energy storage battery pack charging test are as follows:

[0069] S21, during the energy storage battery pack charging simulation operation, synchronously collect the energy storage battery pack charging end by applying different energy storage battery pack test charging voltage data u p The corresponding charging power data is generated, and the real-time power data set of the energy storage battery charging test is generated A=(a1,…,a p ,…,a η ), where a p Indicates the energy storage battery pack test charging voltage data u p The corresponding real-time electric power data of the energy storage battery pack charging test, a p The unit is watt.

[0070] Based on the energy storage battery pack test charging voltage data and the energy storage battery pack charging test real-time electric power data, the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery pack are identified and processed. The operation steps for generating the real-time optimal charging voltage data of the energy storage battery pack are as follows:

[0071] S31, the real-time electric power data a of the energy storage battery pack charging test in the real-time electric power data set A of the energy storage battery pack charging testp Perform electric power numerical analysis and search for the real-time electric power data of the energy storage battery pack charging test with the largest electric power value. p , and generate the real-time electric power extreme value a′ of the energy storage battery pack charging test through data identification p , where a′ p The unit is Watt;

[0072] S32, using the BERT language model algorithm to calculate the real-time electric power extreme value a′ of the energy storage battery pack charging test p The energy storage battery pack test charging voltage data u in the energy storage battery pack test charging voltage data set U p Perform energy storage battery pack test charging voltage type character matching to identify the energy storage battery pack charging test real-time electric power extreme value a′ p Corresponding energy storage battery pack test charging voltage data u p , and constructed as the real-time optimal charging voltage data u′ of the energy storage battery pack, where the unit of u′ is volt.

[0073] Through the cooperation of the energy storage battery group test charging voltage parameter storage unit and the energy storage battery group charging simulation execution unit, the voltage regulation equipment and the simulation power supply are used to autonomously and efficiently execute the energy storage battery group charging simulation operation based on the scientific preset battery group test charging voltage parameters, so as to realize the scientific and safe execution of the energy storage battery group charging simulation operation; the energy storage battery group test charging power acquisition unit uses a power meter to dynamically and accurately collect the real-time electric power parameters of the energy storage battery group charging test, and provide real data for the intelligent evaluation of the optimal charging voltage of the energy storage battery group under the charging state; the energy storage battery group real-time optimal charging voltage identification unit uses cloud computing to perform scientific and intelligent analysis of the optimal charging voltage parameters of the energy storage battery group according to the energy storage battery group test charging voltage parameters, the energy storage battery group charging test real-time electric power parameters combined with the intelligent search algorithm, thereby improving the charging voltage matching accuracy of the energy storage battery group under the charging state, and improving the charging efficiency and service life of the photovoltaic energy storage battery group.

[0074] For further information, see Figure 1 - Figure 2 ,The steps for collecting real-time power generation voltage data of solar panels are as follows:

[0075] S41 , collecting output voltage parameters of the output end of the solar panel online through a voltage sensor and generating a real-time power generation voltage data set B of the solar panel.

[0076] The steps for performing noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate the standard solar panel real-time power generation voltage mean are as follows:

[0077] S51, using the adaptive filtering method to perform voltage data noise reduction preprocessing on the real-time power generation voltage data of the solar panel in the real-time power generation voltage data set B, generating a standard solar panel real-time power generation voltage data set B'=(b'1, ..., b' o ,…,b' ι ), o=1,2,3,…,ι; where b' o represents the real-time power generation voltage data of the oth standard solar panel, ι represents the maximum value of the real-time power generation voltage of the standard solar panel, b' o The unit of is volt;

[0078] S52, using the mean formula to measure the standard solar panel real-time power generation voltage data b' in the standard solar panel real-time power generation voltage data set B'. o The average value of the real-time power generation voltage of the standard solar panel is generated in The unit is volts,

[0079] Based on the real-time optimal charging voltage data of the energy storage battery pack, the real-time average power generation voltage of the standard solar panel, and the specification type data of the energy storage inverter DC boost module, the optimal energy storage inverter DC boost module specification type parameters required for the photovoltaic energy storage charging process of the energy storage battery pack are matched. The operation steps for generating the real-time optimal DC boost module specification type data of the energy storage battery pack are as follows:

[0080] S61, establish the energy storage inverter DC boost module specification type data set C = (c1, ..., c k ,…,c λ ), k=1,2,3,…,λ; where c k Represents the kth energy storage inverter DC boost module specification type data corresponding to the real-time charging voltage parameters of different types of energy storage battery packs and the power generation voltage parameters of solar panels. λ represents the maximum number of energy storage inverter DC boost module specification types. The energy storage inverter DC boost module specification type represents the DC / DC circuit voltage regulation specification type used for solar panel DC voltage regulation in the energy storage inverter.

[0081] S62, the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time average power generation voltage of the standard solar panel Energy storage inverter DC boost module specification type data set C energy storage inverter DC boost module specification type data c k According to the character matching of the real-time charging voltage parameters of the energy storage battery group and the power generation voltage parameters of the solar panel, the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel are searched. Corresponding energy storage inverter DC boost module specification type data c k , and construct the real-time optimal DC boost module specification type data c" for energy storage battery pack k , execute to generate the real-time optimal DC boost module specification type data c″ of the energy storage battery pack k The specific steps are as follows:

[0082] S621. Initialize the population, set the maximum update iteration number T, and identify the DC boost module specification type. The wild dog population randomly updates its spatial position in the search space of the energy storage inverter DC boost module specification type data set C. The spatial position update formula is as follows: Among them, J i It represents the search boundary position of the i-th DC boost module specification type identification wild dog individual in the energy storage inverter DC boost module specification type data set C search space, and They represent the upper and lower boundaries of the DC boost module specification type identification of wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C, and rand represents a random number between [0,1];

[0083] S622, in the group attack phase, the DC boost module specification type identification wild dog searches for the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel in the energy storage inverter DC boost module specification type data set C search space. Matching energy storage inverter DC boost module specification type data c k The prey location and surrounding, search prey simulation behavior formula is as follows: Among them J i (t+1) represents the new position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the t+1th iteration in the group attack phase, ρ represents a random integer generated in the reverse order of [2, N / 2], where N is the size of the wild dog population; Θ y (t) is the type of DC boost module specification to be attacked to identify the wild dog population subset, y represents the cumulative number of calculations, J i (t) represents the position of the wild dog individual i in the search space of the DC boost module specification type data set C of the energy storage inverter after the tth iteration; J *(t) represents the best position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the tth iteration, that is, the best charging voltage data u′ of the energy storage battery group and the average real-time power generation voltage of the standard solar panel are searched in the search space of the energy storage inverter DC boost module specification type data set C. The most suitable energy storage inverter DC boost module specification type data c k location; Indicates a random number of the proportional factor used to change the size of the DC boost module specification type to identify the size of the wild dog track and has a value within [-2, 2];

[0084] S623, persecution attack phase, DC boost module specification type identification wild dog in the energy storage inverter DC boost module specification type data set C search space until hunting with the energy storage battery group real-time optimal charging voltage data u ′, standard solar panel real-time power generation voltage average Matching energy storage inverter DC boost module specification type data c k The hunting simulation behavior formula is as follows: Among them J' i (t+1) represents the new position of the wild dog individual i in the search space of the energy storage inverter DC boost module specification type data set C after the t+1th iteration in the persecution attack phase; Represents a random number of the scaling factor within the range [-2,2], Represents a random number in the range [-1,1], Represents a random number The exponential parameter, r represents a random number in the interval [1, N], J r (t) represents the position of the wild dog individual r in the search space of the energy storage inverter DC boost module specification type data set C after the t-th iteration by randomly selecting the DC boost module specification type, where i≠r;

[0085] S624, cleaning phase, the cleaning behavior is defined as when the DC boost module specification type identification wild dog randomly walks in the energy storage inverter DC boost module specification type data set C search space to find the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel Matching energy storage inverter DC boost module specification type data c k The formula for the carrion-eating behavior and the cleaning simulation behavior is as follows: where J″ i(t+1) represents the new position of the wild dog individual i in the search space of the DC boost module specification type data set C of the energy storage inverter after the t+1th iteration in the cleaning phase, Π represents the binary number randomly generated by the algorithm, Π∈{0,1};

[0086] S625, when the algorithm meets the maximum number of iterations T, output the real-time optimal charging voltage data u′ of the energy storage battery group and the real-time power generation voltage average of the standard solar panel The most suitable energy storage inverter DC boost module specification type data c k ;

[0087] S626, according to the energy storage inverter DC boost module specification type data c in step S625. k Constructing real-time optimal DC boost module specification type data for energy storage battery packs c″ k .

[0088] Through the cooperation of the solar panel real-time power generation voltage acquisition unit and the solar panel real-time power generation voltage parameter preprocessing unit, the voltage sensor is used to obtain the real-time power generation voltage parameters of the solar panel online, and the real-time power generation voltage average of the standard solar panel is scientifically generated in combination with numerical analysis and data noise reduction algorithm, so as to realize the accurate collection of the real-time power generation voltage of the solar panel and improve the quality of photovoltaic energy storage management; the energy storage inverter DC boost module specification type storage unit and the energy storage battery group real-time optimal DC boost module type matching unit cooperate with each other, based on cloud computing, according to the real-time optimal charging voltage parameters of the energy storage battery group, the real-time power generation voltage average of the standard solar panel, combined with the intelligent recognition algorithm and the energy storage inverter DC boost module specification type parameters, the energy storage battery group charging optimal energy storage inverter DC boost module specification type parameters are accurately and efficiently matched, thereby improving the matching accuracy of the photovoltaic energy storage battery group charging DC boost module specification type, and improving the intelligence and scientific nature of photovoltaic energy storage management.

[0089] For further information, see Figure 1 - Figure 2 The steps for constructing the energy storage inverter control data for the energy storage battery pack charging operation and executing the energy storage battery pack charging operation are as follows:

[0090] S71, the energy storage battery pack real-time optimal DC boost module specification type data c" k Perform data identification to generate energy storage battery pack charging operation energy storage inverter control data f;

[0091] S72. The photovoltaic energy storage management platform controls the energy storage inverter to perform the energy storage battery group charging operation based on the energy storage inverter control data f of the energy storage battery group charging operation.

[0092] Through the energy storage battery group charging operation control parameter acquisition unit, the energy storage inverter control parameters for the energy storage battery group charging operation are scientifically constructed based on data processing, realizing efficient timing collection of photovoltaic energy storage battery group charging operation control parameters, and improving the data collection efficiency of photovoltaic energy storage management; the energy storage battery group charging operation execution unit adopts the photovoltaic energy storage management platform to autonomously and efficiently execute the energy storage battery group charging operation according to the energy storage inverter control parameters for the energy storage battery group charging operation, thereby improving the response speed and reliability of photovoltaic energy storage management.

[0093] Example 2:

[0094] See also Figure 1 - Figure 2 A photovoltaic energy storage management system based on cloud computing is used to implement a photovoltaic energy storage management method. The system includes an energy storage battery pack optimal charging voltage evaluation module, an energy storage battery pack optimal DC boost module parameter analysis module, and an energy storage battery pack charging control module;

[0095] The energy storage battery pack optimal charging voltage evaluation module includes an energy storage battery pack test charging voltage parameter storage unit, an energy storage battery pack charging simulation execution unit, an energy storage battery pack test charging power acquisition unit, and an energy storage battery pack real-time optimal charging voltage identification unit;

[0096] The energy storage battery group test charging voltage parameter storage unit is used to store the energy storage battery group test charging voltage data; the energy storage battery group charging simulation execution unit performs the energy storage battery group charging simulation operation according to the energy storage battery group test charging voltage data through the voltage regulation device combined with the simulated power supply; the energy storage battery group test charging power acquisition unit collects the real-time electric power data of the energy storage battery group charging test through the power meter; the energy storage battery group real-time optimal charging voltage identification unit identifies and processes the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery group based on the energy storage battery group test charging voltage data and the real-time electric power data of the energy storage battery group charging test based on cloud computing, and generates the real-time optimal charging voltage data of the energy storage battery group;

[0097] The energy storage battery pack optimal DC boost module parameter analysis module includes a solar panel real-time power generation voltage acquisition unit, a solar panel real-time power generation voltage parameter preprocessing unit, an energy storage inverter DC boost module specification type storage unit, and an energy storage battery pack real-time optimal DC boost module type matching unit;

[0098] A solar panel real-time power generation voltage acquisition unit collects real-time power generation voltage data of solar panels through voltage sensors; a solar panel real-time power generation voltage parameter preprocessing unit performs power generation voltage parameter noise reduction and mean processing on the real-time power generation voltage data of solar panels to generate a standard solar panel real-time power generation voltage mean; an energy storage inverter DC boost module specification type storage unit is used to store the energy storage inverter DC boost module specification type data; an energy storage battery pack real-time optimal DC boost module type matching unit performs optimal energy storage inverter DC boost module specification type parameter matching processing required for the photovoltaic energy storage charging process of the energy storage battery pack based on the real-time optimal charging voltage data of the energy storage battery pack, the standard solar panel real-time power generation voltage mean and the energy storage inverter DC boost module specification type data to generate the real-time optimal DC boost module specification type data of the energy storage battery pack;

[0099] The energy storage battery pack charging control module includes an energy storage battery pack charging operation control parameter acquisition unit and an energy storage battery pack charging operation execution unit;

[0100] The energy storage battery group charging operation control parameter acquisition unit is used to construct the energy storage inverter control data for the energy storage battery group charging operation; the energy storage battery group charging operation execution unit, the photovoltaic energy storage management platform controls the energy storage inverter to execute the energy storage battery group charging operation based on the energy storage inverter control data for the energy storage battery group charging operation.

[0101] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic energy storage management method, characterized in that: The method comprises the following steps: S1. Execute a charging simulation operation of the energy storage battery pack according to the test charging voltage data of the energy storage battery pack; S2. Collect real-time electric power data of energy storage battery pack charging test; S3, based on the energy storage battery pack test charging voltage data and the energy storage battery pack charging test real-time electric power data, performing identification processing on the optimal charging voltage parameters required for the photovoltaic energy storage charging process of the energy storage battery pack, and generating real-time optimal charging voltage data of the energy storage battery pack; S4, collecting real-time power generation voltage data of solar panels; S5, performing power generation voltage parameter noise reduction and mean processing on the real-time power generation voltage data of the solar panel to generate a standard solar panel real-time power generation voltage mean; S6. Performing matching processing on the optimal energy storage inverter DC boost module specification type parameters required for the photovoltaic energy storage charging process of the energy storage battery pack based on the real-time optimal charging voltage data of the energy storage battery pack, the real-time average power generation voltage of the standard solar panel, and the specification type data of the energy storage inverter DC boost module, to generate the real-time optimal DC boost module specification type data of the energy storage battery pack; S7: Construct energy storage inverter control data for energy storage battery pack charging operation and execute energy storage battery pack charging operation.

2. A photovoltaic energy storage management method according to claim 1, characterized in that: Said S1 comprises the following steps: S11, establish the energy storage battery group test charging voltage data set U=(u1,…,u p ,…,u η ), p=1,2,3,…,eta; where u p represents the pth type of energy storage battery pack test charging voltage data, η represents the maximum number of energy storage battery pack test charging voltage types, u p The unit of is volt; S12, when the photovoltaic energy storage management platform issues a charging instruction to the energy storage battery group, at this time, the voltage regulating device is combined with the analog power supply according to the energy storage battery group test charging voltage type number in order according to the U p Apply different charging voltages to the charging terminal of the energy storage battery pack to perform a charging simulation operation on the energy storage battery pack.

3. A photovoltaic energy storage management method according to claim 2, characterized in that: The S2 comprises the following steps: S21, in the process of executing the energy storage battery pack charging simulation operation, synchronously collects the energy storage battery pack charging end by the power meter online and applies different p The corresponding charging power data is generated, and the real-time power data set of the energy storage battery charging test is generated A=(a1,…,a p ,…,a η ), where a p Indicates the u p The corresponding real-time electric power data of the energy storage battery pack charging test, a p The unit is watt.

4. A photovoltaic energy storage management method according to claim 3, characterized in that: The S3 includes the following steps: S31, the a in the A p Perform electric power numerical analysis and search for the a with the largest electric power value. p , and generate the real-time electric power extreme value a′ of the energy storage battery pack charging test through data identification p , where a′ p The unit is Watt; S32, using the BERT language model algorithm to convert the a′ p With the U in the u p Perform energy storage battery test charging voltage type character matching to identify the a' p The corresponding u p , and constructed as the real-time optimal charging voltage data u′ of the energy storage battery pack, where the unit of u′ is volt.

5. A photovoltaic energy storage management method according to claim 4, characterized in that: The S4 comprises the following steps: S41 , collecting output voltage parameters of the output end of the solar panel online through a voltage sensor and generating a real-time power generation voltage data set B of the solar panel.

6. A photovoltaic energy storage management method according to claim 5, characterized in that: The S5 comprises the following steps: S51, using the adaptive filtering method to perform voltage data noise reduction preprocessing on the real-time power generation voltage data of the solar panel in B, generating a standard solar panel real-time power generation voltage data set B'=(b'1, ..., b' o ,…,b' ι ), o=1,2,3,…,ι; where b' o represents the real-time power generation voltage data of the oth standard solar panel, ι represents the maximum value of the real-time power generation voltage of the standard solar panel, b' o The unit of is volt; S52, using the mean formula to measure the b' in B' o The average value of the real-time power generation voltage of the standard solar panel is generated in The unit is volts.

7. A photovoltaic energy storage management method according to claim 6, characterized in that: The S6 comprises the following steps: S61, establish the energy storage inverter DC boost module specification type data set C = (c1, ..., c k ,…,c λ ), k=1,2,3,…,λ; where c k Indicates the kth type of energy storage inverter DC boost module specification data corresponding to the real-time charging voltage parameters of different types of energy storage battery packs and the power generation voltage parameters of solar panels, λ represents the maximum number of energy storage inverter DC boost module specification types; the energy storage inverter DC boost module specification type represents the DC / DC circuit voltage regulation specification type used for solar panel DC voltage regulation in the energy storage inverter; S62, the u, the With the C in c k According to the character matching of the real-time charging voltage parameter of the energy storage battery pack and the power generation voltage parameter of the solar panel, the u, the The corresponding c k , and built as energy storage Battery pack real-time optimal DC boost module specification type data c k , execute to generate the real-time optimal DC boost of the energy storage battery pack Pressure module specification type data c k The specific steps are as follows: S621, population initialization, setting the maximum update iteration number T, DC boost module specification type identification wild dog population randomly updating the spatial position in the C search space; S622, group attack phase, the DC boost module specification type identification wild dog searches for the C search space with the u, the Match the c k Position and surround the prey; S623, persecution attack phase, the DC boost module specification type identifies the wild dog in the C search space until it hunts the wild dog that is related to u, Match the c k Until the prey; S624, cleaning phase, the cleaning behavior is defined as when the DC boost module specification type identification wild dog walks randomly in the C search space and finds the u, the Match the c k carrion-eating behavior; S625. When the algorithm meets the maximum number of iterations T, output the value corresponding to u and the value The best match for the c k ; S626, according to the c in step S625 k Build the optimal DC boost module specifications for energy storage battery packs Data c k .

8. A photovoltaic energy storage management method according to claim 7, characterized in that: The S7 comprises the following steps: S71, the energy storage battery pack real-time optimal DC boost module specification type data c k Perform data identification to generate energy storage battery pack charging operation energy storage inverter control data f; S72. The photovoltaic energy storage management platform controls the energy storage inverter to perform energy storage battery group charging operations according to f.

9. A photovoltaic energy storage management system based on cloud computing, used to implement a photovoltaic energy storage management method according to any one of claims 1 to 8, characterized in that: The system includes an energy storage battery pack optimal charging voltage evaluation module, an energy storage battery pack optimal DC boost module parameter analysis module, and an energy storage battery pack charging control module.

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