Energy scheduling method for household photovoltaic energy storage system and related equipment

Abstract

The invention discloses a household photovoltaic energy storage system energy scheduling method and related equipment, and the method comprises the steps: obtaining photovoltaic power generation power through a photovoltaic prediction model which is constructed based on a convolutional neural network and a long-short term memory network in advance based on weather data and historical photovoltaic data; based on the weather data and the historical load data, obtaining load demand power by using a load prediction model pre-constructed based on a convolutional neural network and a long short-term memory network; and creating a strategy matrix, performing power distribution simulation according to the strategy matrix, the photovoltaic power generation power and the load demand power, and performing iterative optimization on a simulation result through a particle swarm algorithm to obtain an optimal power distribution result. The problems of low energy scheduling accuracy and low user income caused by single energy scheduling strategy of the energy management system in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and in particular to an energy scheduling method and related equipment for a household photovoltaic energy storage system. Background Art

[0002] Photovoltaic technology is one of the most mature and widely used renewable energy technologies, with great development potential. In recent years, the global demand for renewable energy has been growing, and renewable energy such as photovoltaics is accelerating the replacement of traditional fossil energy. Household photovoltaic energy storage system is an important form of photovoltaic power generation into the grid, and has gradually become a hot topic of research. In order to maximize the benefits of its energy management, an energy management system is needed to reasonably plan power generation, power consumption and grid connection. The current energy management system usually has a relatively simple strategy, generally storing excess photovoltaic energy in energy storage batteries or directly connecting to the grid. The lack of strategy makes it difficult to achieve different electricity price ranges for power or grid connection when dispatching household photovoltaic energy storage systems. The accuracy of energy dispatch is low, and the benefits obtained by users from household photovoltaic energy storage systems are not high, which reduces the user's experience of household photovoltaic systems, and thus affects the development of the photovoltaic industry. Summary of the invention

[0003] The main purpose of the present invention is to provide an energy scheduling method and related equipment for a household photovoltaic energy storage system, so as to at least solve the problem that the existing energy management strategy is single, which makes it difficult to obtain electricity or connect to the grid in different electricity price ranges when scheduling the energy of the household photovoltaic energy storage system, resulting in low benefits for users from the household photovoltaic energy system.

[0004] According to one aspect of the present invention, there is provided a method for energy scheduling of a household photovoltaic energy storage system, comprising: Based on weather data and historical photovoltaic data, the photovoltaic power generation is obtained using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network; Based on weather data and historical load data, the load demand power is obtained using a load forecasting model pre-built based on a convolutional neural network and a long short-term memory network; A strategy matrix is ​​created, a power allocation simulation is performed according to the strategy matrix, the photovoltaic power generation power and the load demand power, and the simulation results are iteratively optimized by a particle swarm algorithm to obtain an optimal power allocation result.

[0005] Furthermore, based on weather data and historical photovoltaic data, the photovoltaic power generation power is obtained by using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network, including: Get weather data and historical photovoltaic data; The weather data and the historical photovoltaic data are analyzed by the Pearson correlation analysis method to obtain weather-related characteristics and historical photovoltaic characteristics; Processing the weather-related features through the convolution layer of the photovoltaic prediction model to obtain photovoltaic key features; The photovoltaic key features are processed through the long short-term memory network layer of the photovoltaic prediction model to obtain photovoltaic power time series features; The photovoltaic power time series characteristics are fused with the historical photovoltaic characteristics and processed through the convolution layer of the photovoltaic prediction model to obtain the photovoltaic power generation power.

[0006] Furthermore, based on weather data and historical load data, a load forecasting model pre-built based on a convolutional neural network and a long short-term memory network is used to obtain load demand power, including: Get historical load data; The weather data and the historical load data are analyzed respectively by a Pearson correlation analysis method to obtain weather-related characteristics and historical load characteristics; Processing the weather-related features and historical load features through the convolution layer of the load prediction model to obtain photovoltaic key features and load key features; The photovoltaic key features and the load key features are processed respectively by the long short-term memory network layer of the load prediction model to obtain photovoltaic power timing features and load power timing features; The photovoltaic power timing characteristics and the load power timing characteristics are combined to obtain the load demand power.

[0007] Furthermore, a strategy matrix is ​​created, and a power allocation simulation is performed according to the strategy matrix, the photovoltaic power generation power and the load demand power, including: Create a corresponding strategy matrix based on power allocation requirements; Reading a photovoltaic power generation curve corresponding to the photovoltaic power generation power and a load demand power curve corresponding to the load demand power; Extracting the photovoltaic power generation power and the load demand power of each time step based on the photovoltaic power generation power curve and the load demand power curve; Calculate a battery power distribution curve according to the external charge and discharge power and the energy storage battery model parameters, wherein the battery power distribution curve includes a battery state change curve; Calculate the inverter power distribution curve corresponding to the inverter based on the photovoltaic power generation power and the load demand power of each time step in combination with the current inverter operation mode, wherein the inverter power distribution curve includes a charging power curve, a discharging power curve, a buying power curve and a selling power curve; A power distribution simulation result is obtained according to the strategy matrix, the battery distribution power curve and the inverter power distribution curve.

[0008] Furthermore, the inverter operation modes include energy storage priority mode, grid connection priority mode, self-generation and self-use mode, electricity purchase and energy storage mode and energy storage and electricity selling mode.

[0009] Furthermore, the simulation results are iteratively optimized by particle swarm algorithm to obtain the optimal power allocation results, including: generating a particle swarm according to the power allocation simulation result; Determine the control mode of the large load. If it is a fixed period mode, change the large load switch strategy of all particles to user settings. If it is a fixed duration mode, execute the next step. Randomly generate the initial velocity of each particle; The power distribution simulation is performed on each particle to obtain the electricity cost corresponding to each particle, and the control mode of the large load is determined. If it is a fixed period mode, the next step is executed; if it is a fixed duration mode, a penalty coefficient is assigned to the particle. Update the individual optimal electricity expenditure and strategy matrix of each particle, and update the global optimal at the same time; Calculate the distance and direction of each particle, and update the particle velocity based on the learning factor based on the distance and direction of each particle, so that each particle moves towards the individual and global optimal solution; Determine whether it converges or reaches the maximum number of iterations. If it converges or does not reach the maximum number of iterations, return to the step of randomly generating the initial velocity of each particle. If the maximum number of iterations is reached, output the global optimal solution matrix and the global optimal solution expenditure.

[0010] Furthermore, the individual optimal electricity expenditure and strategy matrix of each particle are updated, and the global optimal is updated at the same time, including: For each particle, if its corresponding electricity expenditure on the day is less than its optimal electricity expenditure, it will be used as the individual optimal strategy matrix, and its corresponding electricity expenditure on the day will be used as the individual optimal electricity expenditure; For each particle, if its corresponding electricity expenditure on that day is less than the optimal electricity expenditure experienced by all particles, it will be used as the global optimal strategy matrix, and its corresponding electricity expenditure on that day will be used as the global optimal electricity expenditure.

[0011] According to another aspect of an embodiment of the present invention, there is also provided an energy scheduling device for a household photovoltaic energy storage system, comprising: The photovoltaic prediction module is used to obtain photovoltaic power generation based on weather data and historical photovoltaic data using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network; A load forecasting module is used to obtain load demand power based on weather data and historical load data using a load forecasting model pre-built based on a convolutional neural network and a long short-term memory network; Simulation module of photovoltaic energy storage system, used to simulate power distribution according to the strategy matrix, photovoltaic power generation and load demand power; The strategy calculation module is used to iteratively optimize the simulation results through the particle swarm algorithm to obtain the optimal power allocation result.

[0012] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: a processor, and a memory for storing a program. Furthermore, the program comprises instructions, and when the instructions are executed by the processor, the processor executes the energy scheduling method for a household photovoltaic energy storage system described in an embodiment of the invention.

[0013] According to another aspect of an embodiment of the present invention, a non-transitory machine-readable medium storing computer instructions is also provided. Further, the computer instructions are used to enable the computer to execute the energy scheduling method for a household photovoltaic energy storage system described in an embodiment of the invention.

[0014] In the present invention, by combining the photovoltaic prediction model constructed by convolutional neural network (CNN) and long short-term memory network (LSTM), the time series characteristics and spatial characteristics in weather data and historical photovoltaic data can be fully utilized to achieve accurate prediction of photovoltaic power generation, and the influence of complex and changeable weather factors on photovoltaic power generation can be captured, thereby improving the accuracy of prediction. The load prediction model constructed by CNN and LSTM, combined with weather data and historical load data, can more scientifically predict future load demand power, which helps to optimize energy storage and grid connection strategies to meet electricity demand in different time periods. Not only the photovoltaic power generation power and load demand power are predicted, but also the power allocation scheme is preliminarily determined by simulating the power allocation results, considering the dynamic balance between power generation and power consumption. By constructing a strategy matrix and using the particle swarm algorithm to find the optimal solution, the intelligent and refined energy scheduling is realized, and the global optimal solution or approximate optimal solution can be quickly found in a complex multi-dimensional space, thereby ensuring that the energy scheduling scheme can adapt to market changes such as electricity price fluctuations while maximizing user benefits. By optimizing prediction, allocation and scheduling, household photovoltaic energy storage systems can respond to market demand more accurately, reasonably arrange power generation, power consumption and grid connection time, thereby maximizing the economic benefits of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of an energy dispatching method for a household photovoltaic energy storage system disclosed in an embodiment of the present invention; Figure 2 A schematic diagram of the photovoltaic prediction model structure disclosed in an embodiment of the present invention; Figure 3 A schematic diagram of the load prediction model structure disclosed in an embodiment of the present invention; Figure 4 A schematic diagram of an energy dispatching device for a photovoltaic energy storage system disclosed in an embodiment of the present invention; Figure 5 Schematic diagram of the structure of the electronic device of this embodiment. DETAILED DESCRIPTION

[0016] Embodiments of the present embodiment will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present embodiment are shown in the accompanying drawings, it should be understood that the present embodiment can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein, which are instead provided for a more thorough and complete understanding of the present embodiment. It should be understood that the drawings and embodiments of the present embodiment are only for exemplary purposes and are not intended to limit the scope of protection of the present embodiment.

[0017] In related technologies, energy management systems generally store excess photovoltaic energy in energy storage batteries or directly connect to the grid. The strategy is relatively simple. When scheduling the energy of household photovoltaic energy storage systems, it is difficult to obtain electricity or connect to the grid in different electricity price ranges, resulting in low accuracy of energy scheduling and low benefits for users from household photovoltaic energy storage systems.

[0018] In order to solve the problem that the energy scheduling strategy of the energy management system in the related technology is relatively simple, resulting in low accuracy of energy scheduling and low user benefits.

[0019] See also Figure 1 As shown, according to an embodiment of the present application, a household photovoltaic energy storage system energy scheduling method is provided, comprising: Step S101, based on weather data and historical photovoltaic data, using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network to obtain photovoltaic power generation; Step S102, based on weather data and historical load data, using a load prediction model pre-built based on a convolutional neural network and a long short-term memory network to obtain load demand power; Step S103, creating a strategy matrix, performing power allocation simulation according to the strategy matrix, photovoltaic power generation power and load demand power, and iteratively optimizing the simulation results through a particle swarm algorithm to obtain an optimal power allocation result.

[0020] In an exemplary embodiment, step S101 includes: Obtain weather data and historical photovoltaic data; The weather data and historical photovoltaic data were analyzed by Pearson correlation analysis method to obtain weather-related characteristics and historical photovoltaic characteristics; The weather-related features are processed through the convolution layer of the photovoltaic prediction model to obtain the photovoltaic key features; The photovoltaic key features are processed through the long short-term memory network layer of the photovoltaic prediction model to obtain the photovoltaic power time series characteristics; The photovoltaic power time series characteristics are fused with the historical photovoltaic characteristics and processed through the convolution layer of the photovoltaic prediction model to obtain the photovoltaic power generation power.

[0021] The weather data (NWP) and historical photovoltaic data were analyzed by Pearson correlation analysis method to obtain weather-related characteristics and historical photovoltaic characteristics. Weather-related characteristics include temperature, cloud opacity, azimuth, solar radiation DHI (horizontal diffuse radiation), DNI (direct normal irradiance), GHI (global horizontal irradiance), relative humidity, and natural time characteristics. Figure 2 The figure shows a schematic diagram of the photovoltaic prediction model. First, the convolutional layer is constructed with the convolutional neural network model as the core. The convolutional layer mainly extracts the key photovoltaic features from the weather-related features, and can determine the difference between weather changes and their impact on photovoltaic power output. Then, a long short-term memory network (LSTM) is constructed. Through the long short-term memory network, effective information in the sequence data can be mined to obtain the photovoltaic power time series characteristics, that is, the trend of photovoltaic power generation over time. At the same time, the historical photovoltaic features are input into the long short-term memory network for time series processing, and then fused in the Cat (splicing) layer and processed through the convolutional layer to obtain the photovoltaic prediction results and the corresponding photovoltaic power generation. The model's ability to understand the input data is strengthened, thereby achieving a highly accurate prediction of the photovoltaic power results on the day before.

[0022] In an exemplary embodiment, step S102 includes: Get historical load data; The weather data and historical load data were analyzed by Pearson correlation analysis method to obtain weather-related characteristics and historical load characteristics; The weather-related features and historical load features are processed through the convolution layer of the load prediction model to obtain the photovoltaic key features and load key features; The photovoltaic key features and the load key features are processed through the long short-term memory network layer of the load forecasting model to obtain the photovoltaic power timing features and the load power timing features; The photovoltaic power timing characteristics are combined with the load power timing characteristics to obtain the load demand power.

[0023] In this embodiment, historical load data uses historical power consumption data, and weather data is analyzed by Pearson correlation analysis to obtain weather-related features. The weather-related features used in constructing the load prediction model in this embodiment include temperature, solar zenith angle, azimuth and other weather factors, which have significant correlations with historical power consumption data. Weather information and time features such as temperature, solar zenith angle, azimuth and other weather information, as well as historical load conditions are selected as input variables of the load prediction model. Figure 3 The figure shows a schematic diagram of the load forecasting model. The convolutional layer of the load forecasting model is built based on the convolutional neural network. The convolutional layer of the load forecasting model accurately extracts the key features of photovoltaic power from the main weather information and physical factors such as time, and captures the relationship between weather, time changes and household loads. Then, the long short-term memory network layer of the load forecasting model is constructed. The effective information of the features in the sequence direction is mined through the long short-term memory network layer to obtain the photovoltaic power time series features and the load power time series features, that is, the trend of photovoltaic power or load power changing over time. The photovoltaic power time series features and the load power time series features are fused together through the Cat layer, and further extracted and output through the convolutional layer to obtain the day-ahead load forecast result, that is, the load demand power.

[0024] In an exemplary embodiment, a strategy matrix is ​​created, and a power allocation simulation is performed according to the strategy matrix, photovoltaic power generation power, and load demand power, including: Create a corresponding strategy matrix based on power allocation requirements; Read the photovoltaic power generation curve corresponding to the photovoltaic power generation and the load demand power curve corresponding to the load demand power; Extract the photovoltaic power generation power and load demand power at each time step based on the photovoltaic power generation power curve and the load demand power curve; Calculate the battery power distribution curve according to the external charging and discharging power and the energy storage battery model parameters, the battery power distribution curve includes a battery state change curve; The inverter power distribution curve corresponding to the inverter is calculated based on the photovoltaic power generation power and the load demand power of each time step combined with the current inverter operation mode. The inverter power distribution curve includes a charging power curve, a discharging power curve, a buying power curve and a selling power curve; The power allocation simulation results are obtained based on the strategy matrix, battery allocation power curve and inverter power allocation curve.

[0025] In an exemplary embodiment, the inverter operation modes include energy storage priority mode, grid connection priority mode, self-generation and self-use mode, electricity purchase and energy storage mode, and energy storage and electricity selling mode.

[0026] In this embodiment, a corresponding strategy matrix is ​​first created according to the actual power allocation requirements, and then the photovoltaic energy storage system is simulated through a software simulation program to simulate the power allocation of the inverter in different operating modes, different photovoltaic inputs, different load requirements and different battery states (SOC). And the electricity bill expenditure is calculated based on the obtained power allocation combined with the electricity price. The simulated photovoltaic energy storage system consists of two mathematical models, including an energy storage battery model and an inverter model. In this embodiment, the parameters of the energy storage battery model include the maximum battery capacity, battery SOC, maximum battery charge and discharge power, charge and discharge efficiency, and charge and discharge power. The energy storage battery model will calculate the change in SOC based on the external charge and discharge power parameter input. The corresponding calculation formula is as follows: ; in: express t The remaining power status at the moment; express t -1 The remaining power status at the moment; Indicates the maximum capacity of the energy storage battery; Indicates unit time; Indicates the efficiency of the energy storage battery charging process; Indicates the efficiency of the energy storage battery discharge process; and Represent the charging power and discharging power respectively.

[0027] In this embodiment, the parameters of the inverter model include inverter power, grid-connected limit power, operation mode, photovoltaic input power, and load demand power. The inverter model calculates the power distribution of the inverter according to the parameter inputs of the external operation mode, photovoltaic input power, and load demand power. The output includes a charging power curve, a discharging power curve, a buying power curve, and a selling power curve. The overall system follows the law of energy conservation, and the corresponding formula is as follows: ; in: , Represents the purchased and sold power of the household system. , Represents the photovoltaic power generation power and load demand power of the household system.

[0028] It can be understood that the inverter in this embodiment includes five operating modes, namely energy storage priority mode, grid connection priority mode, self-generation and self-use mode, electricity purchase and energy storage mode and energy storage and electricity selling mode. Each operating mode is actually a priority ranking of energy flow. The specific priority ranking is as follows: Mode 1: Energy storage priority mode: energy storage > load > grid connection; Mode 2: Grid-connected priority mode: load > grid-connected > energy storage; Mode 3: Self-generation and self-use mode: load > energy storage > grid connection; Mode 4: Purchase electricity and store energy: Based on Mode 3, the battery purchases electricity and stores energy; Mode 5: Energy storage and electricity selling mode: Based on mode 3, the battery is discharged and connected to the grid.

[0029] The power allocation calculation of the inverter is based on the above priorities. For example, in mode 3, the inverter energy is allocated to the load demand first, the photovoltaic surplus power is stored, and the surplus power is connected to the grid. Based on the above mode, the subsequent algorithm can control the power allocation of the inverter to achieve the effect of saving electricity bills or generating electricity bill benefits. Therefore, the output of the simulated photovoltaic energy storage system is a power allocation result based on the current input power data and the control of the operating mode. Within the dimension of one day, at any time granularity, the power allocation result of one day is calculated, and the user's electricity bill benefits are directly estimated based on the result and electricity price. The simulated photovoltaic energy storage system is used as the adaptability function when calculating the strategy, and the indicator of the adaptability function is the electricity bill expenditure.

[0030] In an exemplary embodiment, the simulation results are iteratively optimized by a particle swarm algorithm to obtain an optimal power allocation result, including: Generate a particle swarm based on the power allocation simulation results; Determine the control mode of the large load. If it is a fixed period mode, change the large load switch strategy of all particles to user settings. If it is a fixed duration mode, execute the next step. Randomly generate the initial velocity of each particle; The power distribution simulation is performed on each particle to obtain the electricity cost corresponding to each particle, and the control mode of the large load is determined. If it is a fixed period mode, the next step is executed; if it is a fixed duration mode, a penalty coefficient is assigned to the particle. Update the individual optimal electricity expenditure and strategy matrix of each particle, and update the global optimal at the same time; Calculate the distance and direction of each particle, and update the particle velocity based on the learning factor based on the distance and direction of each particle, so that each particle moves towards the individual and global optimal solution; Determine whether it converges or reaches the maximum number of iterations. If it does not converge or does not reach the maximum number of iterations, return to the step of randomly generating the initial velocity of each particle. If it reaches the maximum number of iterations, output the global optimal solution matrix and the global optimal solution expenditure.

[0031] In an exemplary embodiment, updating the individual optimal electricity cost and strategy matrix of each particle and updating the global optimum at the same time include: For each particle, if its corresponding electricity expenditure on the day is less than its optimal electricity expenditure, it will be used as the individual optimal strategy matrix, and its corresponding electricity expenditure on the day will be used as the individual optimal electricity expenditure; For each particle, if its corresponding electricity expenditure on that day is less than the optimal electricity expenditure experienced by all particles, it will be used as the global optimal strategy matrix, and its corresponding electricity expenditure on that day will be used as the global optimal electricity expenditure.

[0032] In this embodiment, for example, 24 hours a day is divided into 96 time periods, that is, the time granularity is 15 minutes, and the strategy is updated once at each time point. Taking the inverter, heat pump, charging pile, air conditioner and washing machine as examples, the strategy for a single time point is [inverter operation mode, heat pump switch, charging pile switch, air conditioner switch, washing machine switch]. The strategy dimension is 1*n, the inverter operation mode value range is an integer of [1,5], and the switch value range is 0 and 1. Therefore, the strategy dimension of a day is 96*n, which represents the operation mode of the inverter at each time point, and the switch state of each large controllable load, which is the strategy matrix. Among them, loads such as heat pumps, charging piles, air conditioners and washing machines are represented as large loads, and the user can freely set a fixed operating time, which is a fixed period mode. The time required for a day can also be set to be calculated by the algorithm, which is a fixed duration mode. When optimizing through the particle swarm algorithm, this embodiment represents the strategy evidence as a particle, and the specific process includes: Step 1: Generate several particles , each particle is a 96×n strategy matrix, and the value is random within the range; Step 2: Determine the large load control mode: If it is a fixed period mode, change the large load switch strategy of all particles to the user's settings; if it is a fixed duration mode, continue; Step 3: Randomly generate initial velocity for each particle ; Step 4: Each particle As the input of the adaptability function of the photovoltaic energy storage system module, the output of the electricity expenditure for the day is ; Determine the large load control mode: if it is a fixed period mode, continue; if it is a fixed duration mode, count each particle In the case of large load switching, a penalty factor is imposed on particles ,Right now ,in The particle after penalty is imposed The corresponding electricity bill for the day, It is a variable coefficient. The farther away from the user-set value, the greater the penalty coefficient. Step 5: For each particle, calculate its electricity bill for the day The optimal electricity cost For comparison, if it is better, it will be used as the individual optimal strategy matrix , individual optimal electricity expenditure ; Step 6: For each particle, compare its electricity expenditure for the day with the optimal electricity expenditure of all particles. For comparison, if it is better, it will be used as the global optimal strategy matrix , the global optimal electricity expenditure ; Step 7: Calculate each particle and , The distance and direction of and The weighted sum is updated, and the update formula is: ; ; in, Represents particles Updated location, Represents particles The speed at time t+1 is Represents particles The speed at time t, represents the weight parameter, Represents particles The current optimal strategy matrix, is a random number between (0,1).

[0033] Step 8: Determine whether it has converged or reached the maximum number of iterations. If it has not converged or has not reached the maximum number of iterations, jump to step 3, otherwise output the global optimal strategy matrix , the global optimal electricity expenditure .

[0034] The number of iterations of this embodiment is set to 100-10000. When the update formula reaches convergence within the number of iterations, the iteration is stopped, and the result of the update formula is output to obtain the final global optimal strategy matrix and the global optimal electricity expenditure. When the update formula has not converged after reaching the maximum number of iterations of 10,000, the result of the formula is also output, and the final result is used as the global optimal strategy matrix and the global optimal electricity expenditure. This embodiment uses photovoltaic prediction values, load prediction values, electricity price information, and battery status SOC as inputs to the optimization algorithm, takes minimizing electricity expenditure as the optimization goal, iteratively optimizes the strategy matrix, and obtains the optimal solution. After obtaining the optimal solution, the EMS can perform operations to achieve the purpose of saving electricity bills or generating electricity bill revenue.

[0035] The embodiment of the present invention also provides an inverter power control device, such as Figure 4 As shown, the device comprises: The photovoltaic prediction module 10 is used to obtain photovoltaic power generation based on weather data and historical photovoltaic data by using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network; The load prediction module 20 is used to obtain the load demand power based on weather data and historical load data by using a load prediction model pre-built based on a convolutional neural network and a long short-term memory network; A photovoltaic energy storage system simulation module 30 is used to simulate power distribution according to the strategy matrix, photovoltaic power generation power and load demand power; The strategy estimation module 40 is used to iteratively optimize the simulation results by using a particle swarm algorithm to obtain an optimal power allocation result.

[0036] An embodiment of the present invention further provides an electronic device, comprising: a processor and a memory storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the energy scheduling method for a household photovoltaic energy storage system described in an embodiment of the present invention.

[0037] An embodiment of the present invention further provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the energy scheduling method for a household photovoltaic energy storage system described in an embodiment of the present invention.

[0038] An embodiment of the present invention further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the method of the embodiment of the present invention.

[0039] refer to Figure 5 , a block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0040] like Figure 5As shown, the electronic device includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0041] Multiple components in the electronic device are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device, and the input unit 506 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device. The output unit 507 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0042] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may be implemented as a computer program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 502 and / or a communication unit 509. In some embodiments, the computing unit 501 may be configured to perform the above-described method in any other appropriate manner (e.g., by means of firmware).

[0043] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0044] In the context of an embodiment of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0045] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0046] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0047] The various steps described in the method implementation methods provided in the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0048] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments refer to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0049] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A household photovoltaic energy storage system energy scheduling method, characterized in that: include: Based on weather data and historical photovoltaic data, the photovoltaic power generation is obtained using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network; Based on weather data and historical load data, the load demand power is obtained using a load forecasting model pre-built based on a convolutional neural network and a long short-term memory network; A strategy matrix is ​​created, a power allocation simulation is performed according to the strategy matrix, the photovoltaic power generation power and the load demand power, and the simulation results are iteratively optimized by a particle swarm algorithm to obtain an optimal power allocation result.

2. The energy dispatching method for household photovoltaic energy storage system according to claim 1, characterized in that: Based on weather data and historical photovoltaic data, the photovoltaic power generation power is obtained using the photovoltaic prediction model pre-built based on convolutional neural network and long short-term memory network, including: Get weather data and historical photovoltaic data; The weather data and the historical photovoltaic data are analyzed by the Pearson correlation analysis method to obtain weather-related characteristics and historical photovoltaic characteristics; Processing the weather-related features through the convolution layer of the photovoltaic prediction model to obtain photovoltaic key features; The photovoltaic key features are processed through the long short-term memory network layer of the photovoltaic prediction model to obtain photovoltaic power time series features; The photovoltaic power time series characteristics are fused with the historical photovoltaic characteristics and processed through the convolution layer of the photovoltaic prediction model to obtain the photovoltaic power generation power.

3. The energy dispatching method for household photovoltaic energy storage system according to claim 1, characterized in that: Based on weather data and historical load data, the load demand power is obtained using the load forecasting model pre-built based on convolutional neural networks and long short-term memory networks, including: Get historical load data; The weather data and the historical load data are analyzed respectively by a Pearson correlation analysis method to obtain weather-related characteristics and historical load characteristics; Processing the weather-related features and historical load features through the convolution layer of the load prediction model to obtain photovoltaic key features and load key features; The photovoltaic key features and the load key features are processed respectively by the long short-term memory network layer of the load prediction model to obtain photovoltaic power timing features and load power timing features; The photovoltaic power timing characteristics and the load power timing characteristics are combined to obtain the load demand power.

4. The energy dispatching method for household photovoltaic energy storage system according to claim 1, characterized in that: A strategy matrix is ​​created, and a power distribution simulation is performed according to the strategy matrix, the photovoltaic power generation power, and the load demand power, including: Create a corresponding strategy matrix based on power allocation requirements; Reading a photovoltaic power generation curve corresponding to the photovoltaic power generation power and a load demand power curve corresponding to the load demand power; Extracting the photovoltaic power generation power and the load demand power of each time step based on the photovoltaic power generation power curve and the load demand power curve; Calculate a battery power distribution curve according to the external charge and discharge power and the energy storage battery model parameters, wherein the battery power distribution curve includes a battery state change curve; Calculate the inverter power distribution curve corresponding to the inverter based on the photovoltaic power generation power and the load demand power of each time step in combination with the current inverter operation mode, wherein the inverter power distribution curve includes a charging power curve, a discharging power curve, a buying power curve and a selling power curve; A power distribution simulation result is obtained according to the strategy matrix, the battery distribution power curve and the inverter power distribution curve.

5. The energy dispatching method for household photovoltaic energy storage system according to claim 4, characterized in that: The inverter operation modes include energy storage priority mode, grid connection priority mode, self-generation and self-use mode, electricity purchase and energy storage mode and energy storage and electricity selling mode.

6. The energy dispatching method for household photovoltaic energy storage system according to claim 4, characterized in that: The simulation results are iteratively optimized using the particle swarm algorithm to obtain the optimal power allocation results, including: generating a particle swarm according to the power allocation simulation result; Determine the control mode of the large load. If it is a fixed period mode, change the large load switch strategy of all particles to user settings. If it is a fixed duration mode, execute the next step. Randomly generate the initial velocity of each particle; The power distribution simulation is performed on each particle to obtain the electricity cost corresponding to each particle, and the control mode of the large load is determined. If it is a fixed period mode, the next step is executed; if it is a fixed duration mode, a penalty coefficient is assigned to the particle. Update the individual optimal electricity expenditure and strategy matrix of each particle, and update the global optimal at the same time; Calculate the distance and direction of each particle, and update the particle velocity based on the learning factor based on the distance and direction of each particle, so that each particle moves towards the individual and global optimal solution; Determine whether it converges or reaches the maximum number of iterations. If it converges or does not reach the maximum number of iterations, return to the step of randomly generating the initial velocity of each particle. If the maximum number of iterations is reached, output the global optimal solution matrix and the global optimal solution expenditure.

7. The energy dispatching method for household photovoltaic energy storage system according to claim 6, characterized in that: Update the individual optimal electricity expenditure and strategy matrix of each particle, and update the global optimal at the same time, including: For each particle, if its corresponding electricity expenditure on the day is less than its optimal electricity expenditure, it will be used as the individual optimal strategy matrix, and its corresponding electricity expenditure on the day will be used as the individual optimal electricity expenditure; For each particle, if its corresponding electricity expenditure on that day is less than the optimal electricity expenditure experienced by all particles, it will be used as the global optimal strategy matrix, and its corresponding electricity expenditure on that day will be used as the global optimal electricity expenditure.

8. An energy dispatching device for a household photovoltaic energy storage system, characterized in that: include: The photovoltaic prediction module is used to obtain photovoltaic power generation based on weather data and historical photovoltaic data using a photovoltaic prediction model pre-built based on a convolutional neural network and a long short-term memory network; A load forecasting module is used to obtain load demand power based on weather data and historical load data using a load forecasting model pre-built based on a convolutional neural network and a long short-term memory network; Simulation module of photovoltaic energy storage system, used to simulate power distribution according to strategy matrix, photovoltaic power generation and load demand power; The strategy calculation module is used to iteratively optimize the simulation results through the particle swarm algorithm to obtain the optimal power allocation result.

9. An electronic device, comprising: A processor, and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the energy scheduling method for a household photovoltaic energy storage system according to any one of claims 1 to 7.

10. A non-transitory machine-readable medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the energy scheduling method for a household photovoltaic energy storage system according to any one of claims 1 to 7.

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