Energy storage charging and discharging strategy optimization method in light storage integrated system

Abstract

The invention relates to an energy storage charging and discharging strategy optimization method in an optical storage integrated system, and belongs to the technical field of energy storage equipment discharging optimization. Constructing a performance prediction model of the optical storage integrated system according to the historical discharge performance change characteristic data of the optical storage integrated system under each charging strategy data, and finally predicting the discharge performance characteristic data of the optical storage integrated system within a preset time through the performance prediction model of the optical storage integrated system. And carrying out discharge optimization according to the discharge performance characteristic data of the optical storage integrated system within the preset time through a genetic algorithm. The electric energy capacity of the optical storage integrated system is configured according to the power supply demand information of the target area, the charging and discharging strategy of the optical storage integrated system is dynamically adjusted according to the discharging performance condition of the optical storage integrated system, and the service life of the optical storage integrated system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid charging and discharging optimization, and in particular to a method for optimizing energy storage charging and discharging strategies in a photovoltaic and energy storage integrated system. Background Art

[0002] Clean energy generation such as wind power, solar energy, and hydrogen energy is volatile, and the introduction of energy storage technology can effectively deal with the instability of renewable energy. Energy storage technology is a technology that converts different forms of energy such as electrical energy, thermal energy, and chemical energy into other forms of energy, stores them through equipment or physical media, and releases them when necessary. The integrated photovoltaic and storage system has the advantages of low cost, reliable power supply, and less environmental pollution. In recent years, it has become an important development direction for energy applications in the world and can be widely used in industrial and commercial areas, residential areas, campuses, islands and other areas. However, during the service life of the integrated photovoltaic and storage system, due to the performance degradation of the battery cells, electrode materials, etc. in the energy storage system, if the original charging and discharging characteristics are maintained, the service life of the integrated photovoltaic and storage system will be reduced. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method for optimizing energy storage charging and discharging strategies in a photovoltaic storage integrated system.

[0004] To achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system, specifically comprising: Constructing a power supply demand prediction model, and predicting power supply demand information of a target area through the power supply demand prediction model; Configure the electric energy capacity of the photovoltaic and storage integrated system according to the power supply demand information of the target area, perform power configuration according to the electric energy capacity of the photovoltaic and storage integrated system, obtain a power configuration strategy, and configure a charging strategy for the photovoltaic and storage integrated system according to the power configuration strategy; Acquire historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, and construct a performance prediction model of the integrated photovoltaic and storage system according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data; The performance prediction model of the integrated photovoltaic and storage system is used to predict the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time, and the discharge optimization is performed according to the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

[0005] Furthermore, in this method, a power supply demand prediction model is constructed, and power supply demand information of a target area is predicted by the power supply demand prediction model, specifically including: Build a power demand prediction model based on deep neural network, and obtain the power demand information of the target area under each factor data through big data, and use each factor data and power demand information as nodes to build a directed description relationship; Connecting the nodes based on the directed description relationship to form a topological structure graph, obtaining a related adjacency matrix based on the topological structure graph, inputting the related adjacency matrix into the power supply demand prediction model for training, and obtaining a trained power supply demand prediction model; Obtain influencing factor data of the target area within a preset time, input the influencing factor data of the target area within the preset time into the trained power supply demand prediction model for prediction, and obtain power supply demand information of the target area.

[0006] Furthermore, in this method, the electric energy capacity of the photovoltaic storage integrated system is configured according to the power supply demand information of the target area, the power configuration is performed according to the electric energy capacity of the photovoltaic storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic storage integrated system is performed according to the power configuration strategy, specifically: Acquire the maximum power capacity information of each integrated photovoltaic and storage system in the target area, and calculate the quantity information of the required integrated photovoltaic and storage systems according to the power supply demand information of the target area and the maximum power capacity information of each integrated photovoltaic and storage system in the target area; Randomly select a working integrated photovoltaic and storage system according to the quantity information of the required integrated photovoltaic and storage systems, and determine a working combination of the integrated photovoltaic and storage systems; Configure the power capacity of the photovoltaic and storage integrated system according to the working combination of the photovoltaic and storage integrated system and the power supply demand information of the target area; The power configuration is performed according to the electric energy capacity of the photovoltaic storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic storage integrated system is performed according to the power configuration strategy.

[0007] Furthermore, in this method, historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data is obtained, and a performance prediction model of the integrated photovoltaic and storage system is constructed according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, specifically: Constructing timestamps, and counting the discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data in each timestamp, to form the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data; Constructing a performance prediction model of the integrated photovoltaic and storage system based on a deep neural network, inputting the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under various charging strategy data into the performance prediction model of the integrated photovoltaic and storage system for training; When the loss function of the performance prediction model of the integrated photovoltaic and storage system converges to a preset value, the model parameters of the performance prediction model of the integrated photovoltaic and storage system are saved, and the performance prediction model of the integrated photovoltaic and storage system is output.

[0008] Furthermore, in the present method, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is predicted by the performance prediction model of the integrated photovoltaic and storage system, specifically: Acquire discharge performance change characteristic data of each integrated photovoltaic and storage system within a preset time, and input the discharge performance change characteristic data of each integrated photovoltaic and storage system within the preset time into a performance prediction model of the integrated photovoltaic and storage system for prediction; Through prediction, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is obtained, and the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time is output.

[0009] Furthermore, in the present method, the discharge optimization is performed according to the discharge performance characteristic data of the photovoltaic storage system within the preset time by using a genetic algorithm, specifically including: According to the discharge performance characteristic data of the photovoltaic-storage integrated system within the preset time, a discharge performance characteristic threshold of the photovoltaic-storage integrated system is set for each photovoltaic-storage integrated system, and real-time discharge data of each photovoltaic-storage integrated system is initialized; Introducing a genetic algorithm, setting a genetic generation based on the genetic algorithm, and determining whether the real-time discharge data of each photovoltaic-storage integrated system is less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system are not all less than the discharge performance characteristic threshold, performing inheritance based on the genetic algebra, and readjusting the real-time discharge data of the integrated photovoltaic and storage system until they are all less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system is less than the discharge performance characteristic threshold, the real-time discharge data of each integrated photovoltaic and storage system is output, and discharge optimization is performed according to the real-time discharge data of the integrated photovoltaic and storage system.

[0010] A second aspect of the present invention provides a system for optimizing energy storage charging and discharging strategies in a photovoltaic-storage integrated system, the system comprising a memory and a processor, the memory comprising a program for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system, and when the program for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system is executed by the processor, the steps of any one of the methods for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system are implemented.

[0011] A third aspect of the present invention provides a terminal device, including: A power supply demand prediction module is used to construct a power supply demand prediction model and predict the power supply demand information of the target area through the power supply demand prediction model; A charging strategy configuration module configures the electric energy capacity of the photovoltaic and storage integrated system according to the power supply demand information of the target area, performs power configuration according to the electric energy capacity of the photovoltaic and storage integrated system, obtains the power configuration strategy, and performs charging strategy configuration for the photovoltaic and storage integrated system according to the power configuration strategy; A performance prediction model building module is used to obtain the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under various charging strategy data, and to build a performance prediction model of the photovoltaic and storage integrated system according to the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under various charging strategy data; The discharge optimization module predicts the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time through the performance prediction model of the integrated photovoltaic and storage system, and performs discharge optimization according to the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

[0012] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a program for a method for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system. When the program for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system is executed by a processor, the steps of any one of the methods for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system are implemented.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention constructs a power supply demand prediction model, predicts the power supply demand information of the target area through the power supply demand prediction model, and then configures the electric energy capacity of the photovoltaic storage integrated system according to the power supply demand information of the target area, performs power configuration according to the electric energy capacity of the photovoltaic storage integrated system, obtains the power configuration strategy, and configures the charging strategy of the photovoltaic storage integrated system according to the power configuration strategy, thereby obtaining the historical discharge performance change characteristic data of the photovoltaic storage integrated system under each charging strategy data, and constructs the performance prediction model of the photovoltaic storage integrated system according to the historical discharge performance change characteristic data of the photovoltaic storage integrated system under each charging strategy data, and finally predicts the discharge performance characteristic data of the photovoltaic storage integrated system within a preset time through the performance prediction model of the photovoltaic storage integrated system, and performs discharge optimization according to the discharge performance characteristic data of the photovoltaic storage integrated system within a preset time through a genetic algorithm. The present invention configures the electric energy capacity of the photovoltaic storage integrated system according to the power supply demand information of the target area, and dynamically adjusts the charging and discharging strategy of the photovoltaic storage integrated system according to the discharge performance of the photovoltaic storage integrated system, thereby improving the service life of the photovoltaic storage integrated system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The overall flow chart of the energy storage charging and discharging strategy optimization method in the photovoltaic storage integrated system is shown; Figure 2 A partial method flow chart of the method for optimizing the energy storage charging and discharging strategy in a photovoltaic storage integrated system is shown; Figure 3 The system block diagram of the energy storage charging and discharging strategy optimization system in the photovoltaic storage integrated system is shown; Figure 4 A schematic diagram of a terminal device is shown. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] like Figure 1 As shown, the first aspect of the present invention provides a method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system, specifically comprising: S102: constructing a power supply demand prediction model, and predicting the power supply demand information of the target area through the power supply demand prediction model; S104: configuring the electric energy capacity of the photovoltaic and storage integrated system according to the power supply demand information of the target area, performing power configuration according to the electric energy capacity of the photovoltaic and storage integrated system, obtaining a power configuration strategy, and configuring a charging strategy for the photovoltaic and storage integrated system according to the power configuration strategy; S106: Obtain historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, and construct a performance prediction model of the integrated photovoltaic and storage system according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data; S108: Predicting the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time through the performance prediction model of the integrated photovoltaic and storage system, and performing discharge optimization based on the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

[0019] It should be noted that the present invention configures the power capacity of the integrated photovoltaic and storage system according to the power supply demand information of the target area, and dynamically adjusts the charging and discharging strategy of the integrated photovoltaic and storage system according to the discharge performance of the integrated photovoltaic and storage system, thereby improving the service life of the integrated photovoltaic and storage system.

[0020] Furthermore, in this method, a power supply demand prediction model is constructed, and the power supply demand information of the target area is predicted by the power supply demand prediction model, which specifically includes: Build a power demand prediction model based on deep neural network, and obtain the power demand information of the target area under each factor data through big data, and use each factor data and power demand information as nodes to build a directed description relationship; Connecting nodes based on directed description relationships to form a topological structure graph, obtaining a related adjacency matrix based on the topological structure graph, inputting the related adjacency matrix into a power supply demand prediction model for training, and obtaining a trained power supply demand prediction model; Obtain influencing factor data of the target area within a preset time, input the influencing factor data of the target area within the preset time into the trained power supply demand prediction model for prediction, and obtain power supply demand information of the target area.

[0021] It should be noted that the factor data include meteorological characteristics, environmental characteristics, seasonal characteristics and other data. The power supply demand information of the target area under different factor data is inconsistent, such as summer and winter, cloudy days, sunny days and rainy days, etc.

[0022] Furthermore, in this method, the electric energy capacity of the photovoltaic storage integrated system is configured according to the power supply demand information of the target area, the power configuration is performed according to the electric energy capacity of the photovoltaic storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic storage integrated system is performed according to the power configuration strategy, specifically: Obtain the maximum power capacity information of each integrated photovoltaic and storage system in the target area, and calculate the number of required integrated photovoltaic and storage systems according to the power supply demand information of the target area and the maximum power capacity information of each integrated photovoltaic and storage system in the target area; Randomly select the working integrated photovoltaic and storage systems according to the quantity information of the integrated photovoltaic and storage systems required, and determine the working combination of the integrated photovoltaic and storage systems; Configure the power capacity of the photovoltaic and storage integrated system according to the working combination of the photovoltaic and storage integrated system and the power supply demand information of the target area; The power configuration is performed according to the electric energy capacity of the photovoltaic and storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic and storage integrated system is performed according to the power configuration strategy.

[0023] It should be noted that the power supply demand information includes descriptions such as the power supply amount per unit time and the power supply power per unit time. Through this method, the charging strategy of the integrated photovoltaic storage system can be dynamically set according to the power supply demand.

[0024] Furthermore, in this method, historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data are obtained, and a performance prediction model of the integrated photovoltaic and storage system is constructed according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, specifically: Constructing timestamps, and counting the discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data in each timestamp, to form the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data; A performance prediction model for the integrated photovoltaic and storage system is constructed based on a deep neural network, and the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under various charging strategy data are input into the performance prediction model of the integrated photovoltaic and storage system for training; When the loss function of the performance prediction model of the integrated photovoltaic and storage system converges to a preset value, the model parameters of the performance prediction model of the integrated photovoltaic and storage system are saved, and the performance prediction model of the integrated photovoltaic and storage system is output.

[0025] It should be noted that the discharge performance data includes data such as the discharge amount per unit time and the discharge power per unit time. After a certain number of years of use, the photovoltaic and storage integrated system will lead to the degradation of related electrode materials, battery cells, etc., which will further lead to a decrease in discharge performance. This method can construct a performance prediction model for the photovoltaic and storage integrated system to predict the discharge performance of the photovoltaic and storage integrated system.

[0026] Furthermore, in this method, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is predicted by the performance prediction model of the integrated photovoltaic and storage system, specifically: Obtain discharge performance change characteristic data of each integrated photovoltaic and storage system within a preset time, and input the discharge performance change characteristic data of each integrated photovoltaic and storage system within the preset time into a performance prediction model of the integrated photovoltaic and storage system for prediction; Through prediction, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is obtained, and the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time is output.

[0027] like Figure 2As shown, further, in this method, a genetic algorithm is used to perform discharge optimization according to the discharge performance characteristic data of the photovoltaic storage system within a preset time, specifically including: S202: setting a discharge performance characteristic threshold of the integrated photovoltaic and storage system for each integrated photovoltaic and storage system according to the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time, and initializing the real-time discharge data of each integrated photovoltaic and storage system; S204: Introducing a genetic algorithm, setting a genetic generation based on the genetic algorithm, and determining whether the real-time discharge data of each photovoltaic-storage integrated system is less than a discharge performance characteristic threshold; S206: When the real-time discharge data of each integrated photovoltaic and energy storage system is not all less than the discharge performance characteristic threshold, genetic inheritance is performed based on genetic algebra to readjust the real-time discharge data of the integrated photovoltaic and energy storage system until all are less than the discharge performance characteristic threshold; S208: When the real-time discharge data of each integrated photovoltaic and storage system is less than the discharge performance characteristic threshold, the real-time discharge data of each integrated photovoltaic and storage system is output, and discharge optimization is performed according to the real-time discharge data of the integrated photovoltaic and storage system.

[0028] It should be noted that genetic algorithm is a search algorithm used in computational mathematics to solve optimization problems and is a type of evolutionary algorithm. Evolutionary algorithms were originally developed based on some phenomena in evolutionary biology, including inheritance, mutation, natural selection, and hybridization. Genetic algorithms are usually implemented as a computer simulation. For an optimization problem, a population of abstract representations (called chromosomes) of a certain number of candidate solutions (called individuals) evolves towards a better solution. Traditionally, solutions are represented in binary (i.e., strings of 0 and 1), but other representations can also be used. Evolution starts with a population of completely random individuals and then occurs generation after generation. In each generation, the fitness of the entire population is evaluated, and multiple individuals are randomly selected from the current population (based on their fitness), and a new population of life is generated through natural selection and mutation, which becomes the current population in the next iteration of the algorithm. The photovoltaic storage system in the target area is clustered and controlled by a genetic algorithm, so that the real-time discharge data of each photovoltaic storage system is less than the discharge performance characteristic threshold, thereby improving the service life of the photovoltaic storage system.

[0029] In addition, the method may further include: By performing a performance test on the integrated photovoltaic and storage system under a working environment and obtaining a discharge performance attenuation coefficient under each working environment data, a knowledge graph is constructed based on the discharge performance attenuation coefficient under each working environment data; Acquire the working environment data of each integrated photovoltaic and storage system, and input the working environment data of the current integrated photovoltaic and storage system into the knowledge graph for data matching; Through data matching, the discharge performance attenuation coefficient of each photovoltaic storage system under the working environment data is obtained; Based on the discharge performance attenuation coefficient of each integrated photovoltaic and storage system under the working environment data, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is updated, and discharge optimization is performed based on the updated discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

[0030] It should be noted that the discharge performance attenuation coefficient is the ratio between the discharge performance characteristic data of the integrated photovoltaic and storage system at the standard temperature (temperature environment is 25 degrees Celsius) at the time stamp and the discharge performance characteristic data under the real-time temperature conditions. This method can further optimize the discharge strategy of the integrated photovoltaic and storage system.

[0031] In addition, the power configuration is performed according to the power capacity of the photovoltaic storage integrated system, and the power configuration strategy is obtained, which may also include: Acquire historical service data information of the photovoltaic and energy storage integrated system, introduce a Bayesian network, input the historical service data information of the photovoltaic and energy storage integrated system into the Bayesian network for training, and obtain a trained Bayesian network; Acquiring service data information of the photovoltaic-storage integrated system within a preset time, and inputting the service data information of the photovoltaic-storage integrated system within the preset time into the trained Bayesian network for prediction; Obtain the predicted fault time of each integrated photovoltaic and energy storage system, and obtain the power supply period corresponding to the power supply demand in the target area, and determine whether the predicted fault time of the integrated photovoltaic and energy storage system is within the power supply period corresponding to the power supply demand in the target area; When the predicted fault time is not in the power supply period corresponding to the power supply demand in the target area, the corresponding photovoltaic integrated system is used as the recommended photovoltaic integrated system, and the power capacity of the photovoltaic integrated system is configured according to the power capacity of the photovoltaic integrated system to obtain a power configuration strategy; It should be noted that this method can improve the rationality of power configuration.

[0032] like Figure 3 As shown, the second aspect of the present invention provides a system 3 for optimizing energy storage charging and discharging strategies in a photovoltaic-storage integrated system. The system 3 includes a memory 31 and a processor 32. The memory 31 includes a program for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system. When the program for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system is executed by the processor 32, any step of the method for optimizing energy storage charging and discharging strategies in the photovoltaic-storage integrated system is implemented.

[0033] like Figure 4As shown, the third aspect of the present invention provides a terminal device, including: The power supply demand prediction module 10 constructs a power supply demand prediction model and predicts the power supply demand information of the target area through the power supply demand prediction model; The charging strategy configuration module 20 configures the power capacity of the photovoltaic storage integrated system according to the power supply demand information of the target area, performs power configuration according to the power capacity of the photovoltaic storage integrated system, obtains the power configuration strategy, and configures the charging strategy of the photovoltaic storage integrated system according to the power configuration strategy; The performance prediction model building module 30 obtains the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data, and builds a performance prediction model of the photovoltaic and storage integrated system according to the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data; The discharge optimization module 40 predicts the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time through the performance prediction model of the integrated photovoltaic and storage system, and performs discharge optimization based on the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

[0034] Furthermore, in this device, a power supply demand prediction model is constructed, and the power supply demand information of the target area is predicted by the power supply demand prediction model, specifically including: Build a power demand prediction model based on deep neural network, and obtain the power demand information of the target area under each factor data through big data, and use each factor data and power demand information as nodes to build a directed description relationship; Connecting nodes based on directed description relationships to form a topological structure graph, obtaining a related adjacency matrix based on the topological structure graph, inputting the related adjacency matrix into a power supply demand prediction model for training, and obtaining a trained power supply demand prediction model; Obtain influencing factor data of the target area within a preset time, input the influencing factor data of the target area within the preset time into the trained power supply demand prediction model for prediction, and obtain power supply demand information of the target area.

[0035] Furthermore, in this device, the power capacity of the photovoltaic storage integrated system is configured according to the power supply demand information of the target area, the power is configured according to the power capacity of the photovoltaic storage integrated system, the power configuration strategy is obtained, and the charging strategy of the photovoltaic storage integrated system is configured according to the power configuration strategy, specifically: Obtain the maximum power capacity information of each integrated photovoltaic and storage system in the target area, and calculate the number of required integrated photovoltaic and storage systems according to the power supply demand information of the target area and the maximum power capacity information of each integrated photovoltaic and storage system in the target area; Randomly select the working integrated photovoltaic and storage systems according to the quantity information of the integrated photovoltaic and storage systems required, and determine the working combination of the integrated photovoltaic and storage systems; Configure the power capacity of the photovoltaic and storage integrated system according to the working combination of the photovoltaic and storage integrated system and the power supply demand information of the target area; The power configuration is performed according to the electric energy capacity of the photovoltaic and storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic and storage integrated system is performed according to the power configuration strategy.

[0036] Furthermore, in this device, historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data is obtained, and a performance prediction model of the integrated photovoltaic and storage system is constructed according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, specifically: Constructing timestamps, and counting the discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data in each timestamp, to form the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under each charging strategy data; A performance prediction model for the integrated photovoltaic and storage system is constructed based on a deep neural network, and the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under various charging strategy data are input into the performance prediction model of the integrated photovoltaic and storage system for training; When the loss function of the performance prediction model of the integrated photovoltaic and storage system converges to a preset value, the model parameters of the performance prediction model of the integrated photovoltaic and storage system are saved, and the performance prediction model of the integrated photovoltaic and storage system is output.

[0037] Furthermore, in this device, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is predicted by the performance prediction model of the integrated photovoltaic and storage system, specifically: Obtain discharge performance change characteristic data of each integrated photovoltaic and storage system within a preset time, and input the discharge performance change characteristic data of each integrated photovoltaic and storage system within the preset time into a performance prediction model of the integrated photovoltaic and storage system for prediction; Through prediction, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is obtained, and the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time is output.

[0038] Furthermore, in this device, a genetic algorithm is used to optimize discharge according to the discharge performance characteristic data of the photovoltaic storage system within a preset time, specifically including: According to the discharge performance characteristic data of the photovoltaic-storage integrated system within a preset time, a discharge performance characteristic threshold of the photovoltaic-storage integrated system is set for each photovoltaic-storage integrated system, and real-time discharge data of each photovoltaic-storage integrated system is initialized; A genetic algorithm is introduced, a genetic generation is set based on the genetic algorithm, and it is determined whether the real-time discharge data of each photovoltaic storage system is less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system is not all less than the discharge performance characteristic threshold, genetic inheritance is performed based on genetic algebra to readjust the real-time discharge data of the integrated photovoltaic and storage system until all are less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system is less than the discharge performance characteristic threshold, the real-time discharge data of each integrated photovoltaic and storage system is output, and discharge optimization is performed according to the real-time discharge data of the integrated photovoltaic and storage system.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a program for a method for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system. When the program for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system is executed by a processor, any step of the method for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system is implemented.

[0040] In summary, the present invention constructs a power supply demand prediction model, predicts the power supply demand information of the target area through the power supply demand prediction model, and then configures the electric energy capacity of the photovoltaic storage integrated system according to the power supply demand information of the target area, performs power configuration according to the electric energy capacity of the photovoltaic storage integrated system, obtains the power configuration strategy, and configures the charging strategy of the photovoltaic storage integrated system according to the power configuration strategy, thereby obtaining the historical discharge performance change characteristic data of the photovoltaic storage integrated system under each charging strategy data, and constructs the performance prediction model of the photovoltaic storage integrated system according to the historical discharge performance change characteristic data of the photovoltaic storage integrated system under each charging strategy data, and finally predicts the discharge performance characteristic data of the photovoltaic storage integrated system within a preset time through the performance prediction model of the photovoltaic storage integrated system, and performs discharge optimization according to the discharge performance characteristic data of the photovoltaic storage integrated system within a preset time through a genetic algorithm. The present invention configures the electric energy capacity of the photovoltaic storage integrated system according to the power supply demand information of the target area, and dynamically adjusts the charging and discharging strategy of the photovoltaic storage integrated system according to the discharge performance of the photovoltaic storage integrated system, thereby improving the service life of the photovoltaic storage integrated system.

[0041] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0042] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0043] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0044] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0045] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0046] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system, characterized in that: Specifically include: Constructing a power supply demand prediction model, and predicting power supply demand information of a target area through the power supply demand prediction model; Configure the electric energy capacity of the photovoltaic and storage integrated system according to the power supply demand information of the target area, perform power configuration according to the electric energy capacity of the photovoltaic and storage integrated system, obtain a power configuration strategy, and configure a charging strategy for the photovoltaic and storage integrated system according to the power configuration strategy; Acquire historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, and construct a performance prediction model of the integrated photovoltaic and storage system according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data; The performance prediction model of the integrated photovoltaic and storage system is used to predict the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time, and the discharge optimization is performed according to the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

2. The method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system according to claim 1, characterized in that: Constructing a power supply demand prediction model, and predicting the power supply demand information of the target area through the power supply demand prediction model, specifically including: Build a power demand prediction model based on deep neural network, and obtain the power demand information of the target area under each factor data through big data, and use each factor data and power demand information as nodes to build a directed description relationship; Connecting the nodes based on the directed description relationship to form a topological structure graph, obtaining a related adjacency matrix based on the topological structure graph, inputting the related adjacency matrix into the power supply demand prediction model for training, and obtaining a trained power supply demand prediction model; Obtain influencing factor data of the target area within a preset time, input the influencing factor data of the target area within the preset time into the trained power supply demand prediction model for prediction, and obtain power supply demand information of the target area.

3. The method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system according to claim 1, characterized in that: The power capacity of the photovoltaic and storage integrated system is configured according to the power supply demand information of the target area, the power is configured according to the power capacity of the photovoltaic and storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic and storage integrated system is configured according to the power configuration strategy, specifically: Acquire the maximum power capacity information of each integrated photovoltaic and storage system in the target area, and calculate the quantity information of the required integrated photovoltaic and storage systems according to the power supply demand information of the target area and the maximum power capacity information of each integrated photovoltaic and storage system in the target area; Randomly select a working integrated photovoltaic and storage system according to the quantity information of the required integrated photovoltaic and storage systems, and determine a working combination of the integrated photovoltaic and storage systems; Configure the power capacity of the photovoltaic and storage integrated system according to the working combination of the photovoltaic and storage integrated system and the power supply demand information of the target area; The power configuration is performed according to the electric energy capacity of the photovoltaic storage integrated system, the power configuration strategy is obtained, and the charging strategy configuration of the photovoltaic storage integrated system is performed according to the power configuration strategy.

4. The method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system according to claim 1, characterized in that: Obtain historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, and build a performance prediction model of the integrated photovoltaic and storage system according to the historical discharge performance change characteristic data of the integrated photovoltaic and storage system under each charging strategy data, specifically: Constructing timestamps, and counting the discharge performance change characteristic data of the photovoltaic and energy storage integrated system under each charging strategy data in each timestamp, so as to form the historical discharge performance change characteristic data of the photovoltaic and energy storage integrated system under each charging strategy data; Constructing a performance prediction model of the integrated photovoltaic and storage system based on a deep neural network, inputting historical discharge performance change characteristic data of the integrated photovoltaic and storage system under various charging strategy data into the performance prediction model of the integrated photovoltaic and storage system for training; When the loss function of the performance prediction model of the integrated photovoltaic and storage system converges to a preset value, the model parameters of the performance prediction model of the integrated photovoltaic and storage system are saved, and the performance prediction model of the integrated photovoltaic and storage system is output.

5. The method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system according to claim 1, characterized in that: The performance prediction model of the photovoltaic-storage integrated system is used to predict the discharge performance characteristic data of the photovoltaic-storage integrated system within a preset time, specifically: Acquire discharge performance change characteristic data of each integrated photovoltaic and storage system within a preset time, and input the discharge performance change characteristic data of each integrated photovoltaic and storage system within the preset time into a performance prediction model of the integrated photovoltaic and storage system for prediction; Through prediction, the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time is obtained, and the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time is output.

6. The method for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system according to claim 1, characterized in that: The discharge optimization is performed according to the discharge performance characteristic data of the photovoltaic energy storage system within the preset time by using a genetic algorithm, specifically including: According to the discharge performance characteristic data of the photovoltaic-storage integrated system within the preset time, a discharge performance characteristic threshold of the photovoltaic-storage integrated system is set for each photovoltaic-storage integrated system, and real-time discharge data of each photovoltaic-storage integrated system is initialized; Introducing a genetic algorithm, setting a genetic generation based on the genetic algorithm, and determining whether the real-time discharge data of each photovoltaic-storage integrated system is less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system are not all less than the discharge performance characteristic threshold, genetic inheritance is performed based on the genetic algebra to readjust the real-time discharge data of the integrated photovoltaic and storage system until they are all less than the discharge performance characteristic threshold; When the real-time discharge data of each integrated photovoltaic and storage system is less than the discharge performance characteristic threshold, the real-time discharge data of each integrated photovoltaic and storage system is output, and discharge optimization is performed according to the real-time discharge data of the integrated photovoltaic and storage system.

7. A system for optimizing energy storage charging and discharging strategy in a photovoltaic storage integrated system, characterized in that: The system includes a memory and a processor, wherein the memory includes a method program for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system, and when the method program for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system is executed by the processor, the steps of the method for optimizing a storage energy charging and discharging strategy in a photovoltaic-storage integrated system as described in any one of claims 1 to 6 are implemented.

8. A terminal device, characterized in that: include: A power supply demand prediction module is used to construct a power supply demand prediction model and predict the power supply demand information of the target area through the power supply demand prediction model; A charging strategy configuration module configures the electric energy capacity of the photovoltaic and storage integrated system according to the power supply demand information of the target area, performs power configuration according to the electric energy capacity of the photovoltaic and storage integrated system, obtains the power configuration strategy, and performs charging strategy configuration for the photovoltaic and storage integrated system according to the power configuration strategy; A performance prediction model building module is used to obtain the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under various charging strategy data, and to build a performance prediction model of the photovoltaic and storage integrated system according to the historical discharge performance change characteristic data of the photovoltaic and storage integrated system under various charging strategy data; The discharge optimization module predicts the discharge performance characteristic data of the integrated photovoltaic and storage system within a preset time through the performance prediction model of the integrated photovoltaic and storage system, and performs discharge optimization according to the discharge performance characteristic data of the integrated photovoltaic and storage system within the preset time through a genetic algorithm.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a method program for optimizing the energy storage charging and discharging strategy in a photovoltaic-storage integrated system. When the method program for optimizing the energy storage charging and discharging strategy in a photovoltaic-storage integrated system is executed by a processor, the steps of the method for optimizing the energy storage charging and discharging strategy in a photovoltaic-storage integrated system as described in any one of claims 1 to 6 are implemented.

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