Intelligent control method and equipment of optical storage system, medium and product

Abstract

The invention provides an intelligent control method and device for a light storage system, a medium and a product. The light storage system comprises a photovoltaic module, an energy storage module and a heat pump load. The intelligent control method comprises the following steps: acquiring operation data of the optical storage system, wherein the operation data comprises photovoltaic output power of a photovoltaic module, outdoor environment temperature, actual indoor temperature and the like; obtaining decision reference factors of the optical storage system, wherein the decision reference factors comprise a preset energy efficiency ratio curve of the heat pump load and the like; determining an operation strategy of the optical storage system according to a preset objective function, the operation strategy including working power of the heat pump load, charging and discharging power of the energy storage module and interaction power of the optical storage system and an external public power grid, various weight coefficient values of the preset objective function are determined by a pre-constructed intelligent decision model according to the operation data and the decision reference factors; the operation of the optical storage system is controlled according to the determined operation strategy, and the comprehensive benefit of each device of the optical storage system is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic energy storage technology, and in particular to an intelligent control method, device, medium and product for a photovoltaic energy storage system. Background Technology

[0002] Green electricity technology, as a core technological path to address the global energy transition, has been increasingly widely applied, and photovoltaic (PV) and energy storage (ESS) technology is a key form of green electricity technology. The core value of PV-ESS technology lies in converting solar energy resources through photovoltaic modules and relying on energy storage modules to smooth fluctuations in PV output and achieve peak-shaving utilization of electricity. In recent years, in response to the development of PV-ESS technology, integrated PV and energy storage systems, combining PV modules, energy storage modules, and various electrical loads, have increasingly appeared in residential homes, shopping malls, and other scenarios. However, in current PV-ESS systems, each module operates independently or uses a single indicator for system control, resulting in low synergy between the PV modules and requiring further improvement in the overall comprehensive benefits of the PV-ESS system. Summary of the Invention

[0003] One object of the present invention is to provide an intelligent control method, device, medium and product for a photovoltaic energy storage system that helps to improve the overall benefits of the system in terms of energy utilization efficiency, economic benefits and user experience.

[0004] Specifically, this invention provides an intelligent control method for a photovoltaic-energy storage system, the system comprising a photovoltaic module, an energy storage module, and a heat pump load. The intelligent control method includes: The system acquires operational data of the photovoltaic-storage system, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, and state of charge of the energy storage module. The decision reference factors for the photovoltaic-storage system are obtained, including the preset energy efficiency ratio curve of the heat pump load, the health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. The operation strategy of the photovoltaic-storage system is determined according to a preset objective function. The operation strategy includes the working power of the heat pump load, the charging and discharging power of the energy storage module, and the interaction power between the photovoltaic-storage system and the external public power grid. The weight coefficients of each item of the preset objective function are determined by a pre-constructed intelligent decision-making model based on the operation data and the decision reference factors. The operation of the photovoltaic storage system is controlled according to the determined operating strategy.

[0005] Optionally, the preset objective function is: ; in, This indicates finding the minimum value; This indicates the electricity cost for the specified time period; This indicates the actual indoor temperature after a set time period. This indicates the set indoor temperature; This indicates the depth of discharge of the energy storage module after a set time period. , , These represent the electricity cost weighting coefficient, temperature control weighting coefficient, and energy storage safety weighting coefficient, respectively.

[0006] Optionally, the construction process of the intelligent decision-making model includes: Receive multiple sets of training basic data, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, state of charge of the energy storage module, preset energy efficiency ratio curve of the heat pump load, health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. Obtain the preset allowed range of values ​​for each weight coefficient of each group of the training base data; The optimal weight coefficients corresponding to the minimum value of the preset objective function for each set of training data are calculated using a preset algorithm. The intelligent decision-making model is obtained by using the multiple sets of training data as the model input values ​​of the model to be trained, and using the optimal weight coefficient values ​​corresponding to each set of training data as the model output values ​​of the model to be trained.

[0007] Optionally, a trigger event for ending the running strategy is obtained; Re-execute the steps of acquiring the operating data of the photovoltaic storage system, acquiring the decision reference factors of the photovoltaic storage system, and determining the operating strategy of the photovoltaic storage system according to the preset objective function.

[0008] Optionally, after the step of obtaining the trigger event for ending the running strategy, the method further includes: Obtain the electricity cost used for the operation strategy, the actual indoor temperature, the depth of discharge of the energy storage module, and the actual value of the preset objective function; If the absolute value of the difference between the actual value and the original predicted value of the preset objective function and the ratio of the original predicted value of the preset objective function exceeds a preset threshold, the running data and the decision reference factors corresponding to the running strategy are used as supplementary training data, and the supplementary optimal weight coefficient values ​​corresponding to the minimum value of the preset objective function are calculated. The supplementary training data and the supplementary optimal weight coefficient values ​​are used as model input values ​​and model output values, respectively, to supplement the training of the intelligent decision model.

[0009] Optionally, the step of determining the operating strategy based on a preset objective function further includes: The operating strategy is determined by combining the preset objective function and preset constraints; The preset constraints include: The reverse power of the power grid is controlled to be less than a preset power threshold, wherein the reverse power of the power grid is the power transmitted by the photovoltaic energy storage system to the external public power grid; The difference between the actual indoor temperature after the set time period and the set indoor temperature is controlled to be within a preset temperature fluctuation range. The energy storage module is controlled to maintain its state of charge within a preset range after a set time period.

[0010] Optionally, the health status value of the energy storage module is determined by a preset energy storage health model; The pre-set energy storage health model is as follows: ; in, The health status value; The initial attenuation coefficient of the energy storage module; It is a natural constant; The attenuation rate coefficient of the energy storage module is a negative number. The number of cycles for the energy storage module.

[0011] Optionally, the decision reference factors may also include user instructions; In the process of determining the operation strategy of the photovoltaic energy storage system according to the preset objective function: If the user instruction is a preset power-saving priority instruction, the electricity cost weighting coefficient is set to the maximum. If the user instruction is a preset comfort-first instruction, set the temperature control weighting coefficient to the maximum. If the user instruction is a preset safety priority instruction, the energy storage safety weight coefficient is set to the maximum.

[0012] According to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer-executable program stored in the memory and running on the processor, wherein the processor executes the computer-executable program to implement the intelligent control method of the optical storage system according to any one of the preceding claims.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer-executable program is stored, wherein the computer-executable program, when executed by a processor, implements the intelligent control method of the optical storage system according to any one of the preceding claims.

[0014] According to another aspect of the present invention, a computer program product is also provided, comprising a computer executable program that, when executed by a processor, implements the intelligent control method for the optical storage system according to any one of the preceding claims.

[0015] The intelligent control method, device, medium, and product of the photovoltaic-storage system of the present invention acquires the operating data of the photovoltaic-storage system, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, and state of charge of the energy storage module. It also acquires decision reference factors for the photovoltaic-storage system, including the preset energy efficiency ratio curve of the heat pump load, the health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. Then, it determines the operating strategy of the photovoltaic-storage system according to a preset objective function. The operating strategy includes the working power of the heat pump load, the charging and discharging power of the energy storage module, and the interaction power between the photovoltaic-storage system and the external public power grid. The weight coefficients of each item in the preset objective function are determined by a pre-constructed intelligent decision-making model based on the operating data and decision reference factors. Finally, it controls the operation of the photovoltaic-storage system according to the determined operating strategy. Therefore, this solution can quickly and intelligently generate an operating strategy that achieves a good balance between electricity costs, user comfort, and the safety of energy storage modules by acquiring sufficient decision-making data and comprehensively considering the three factors of electricity costs, user comfort, and the safety of energy storage modules. This operating strategy takes into account the benefits of electricity costs, user comfort, and the safety of energy storage modules, thereby helping to improve the synergy of various devices in the photovoltaic-energy storage system, and ultimately improving the overall benefits of the photovoltaic-energy storage system in terms of energy utilization efficiency, economic benefits, and user experience.

[0016] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0017] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a schematic architecture diagram of an optical storage system according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of an intelligent control method for a photovoltaic energy storage system according to an embodiment of the present invention; Figure 3 This is a schematic flowchart illustrating the construction of an intelligent decision-making model in an intelligent control method for a photovoltaic energy storage system according to an embodiment of the present invention. Figure 4 This is a schematic flowchart of an intelligent control method for a photovoltaic energy storage system according to another embodiment of the present invention; Figure 5 This is a schematic flowchart of an intelligent control method for a photovoltaic energy storage system according to yet another embodiment of the present invention; Figure 6 This is a schematic flowchart of an intelligent control method for a photovoltaic energy storage system according to yet another embodiment of the present invention; Figure 7 This is a schematic diagram of a computer device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; Figure 9 This is a schematic diagram of a computer program product according to an embodiment of the present invention. Detailed Implementation

[0018] Those skilled in the art should understand that the embodiments described below are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. These partial embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present invention.

[0019] It should be noted that, in the description of this invention, each functional module can be a physical module composed of multiple structures, components, or electronic devices, or a virtual module composed of multiple programs; each functional module can be an independent module or a module divided from a whole module according to its function. Those skilled in the art should understand that, provided the technical solution described in this invention can be implemented, any changes in the configuration, implementation, or positional relationship of the functional modules will not deviate from the technical principles of this invention, and therefore should all fall within the protection scope of this invention.

[0020] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0021] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0022] like Figure 1 As shown, in one embodiment, the photovoltaic-storage system includes a photovoltaic module 100, an energy storage module 200, and a heat pump load 300. The photovoltaic module 100 is a photovoltaic power generation component including photovoltaic panels and related electrical equipment (such as combiner boxes). The energy storage module 200 is an energy storage battery. The heat pump load 300 is a device used for heating and cooling indoor spaces. The electricity generated by the photovoltaic module 100 can be supplied to the heat pump load 300, thereby driving the heat pump load 300 to operate. Simultaneously, when the photovoltaic module 100 generates excess electricity, it can also charge the energy storage module 200 and transmit power to the external public power grid 400. The energy storage module 200 can store electrical energy, and the stored electrical energy can be supplied to the heat pump load 300 to drive its operation. Additionally, the external public power grid 400 can also supply power to the heat pump load 300, thereby driving its operation.

[0023] The optical storage system also includes a control module 500, which includes a memory and a processor. The memory stores a machine-executable program, which, when executed by the processor, implements the intelligent control method of the optical storage system according to any of the following embodiments.

[0024] like Figure 2 As shown, in one embodiment, the intelligent control method for a photovoltaic energy storage system generally includes: Step S201: Obtain the operating data of the photovoltaic and energy storage system. The operating data includes the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, and state of charge of the energy storage module.

[0025] Specifically, the photovoltaic output power can be the real-time photovoltaic output power obtained through direct detection, or the photovoltaic output power over a set time period predicted by a pre-built photovoltaic power generation model. The pre-built photovoltaic power generation model can be a model trained using atmospheric environmental information such as light intensity, temperature, wind direction, and humidity as input data, and the photovoltaic output power over the set time period as output data; for example, a neural network model. Furthermore, the model for predicting photovoltaic output power is existing technology, and its construction process will not be elaborated upon here.

[0026] The outdoor ambient temperature and the actual indoor temperature can be obtained from temperature sensors installed in the corresponding environments. The set indoor temperature is the temperature set by the user. The real-time electricity price is the electricity price for the current time period. The state of charge (SOC) of the energy storage module is expressed as a value from 0% to 100%.

[0027] Step S202: Obtain the decision reference factors for the photovoltaic-storage system. The decision reference factors include the preset energy efficiency ratio curve of the heat pump load, the health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature.

[0028] Specifically, the energy efficiency ratio (EER) curve of a heat pump load is a curve formed by plotting the outdoor ambient temperature on the x-axis and the heat pump's energy efficiency ratio on the y-axis. The memory stores preset EER curves corresponding to various operating power levels of the heat pump load. The EER curve of the heat pump load can be used to calculate the change in indoor temperature that the heat pump load can achieve after operating at a certain power level for a set period of time, and to more accurately determine the electricity consumption of the heat pump load after operating at a certain power level for a set period of time.

[0029] The health status value of the energy storage module is determined by a pre-configured energy storage health model. The pre-configured energy storage health model is as follows: ; in, This represents the health status value. This represents the initial attenuation coefficient of the energy storage module; It is a natural constant; This is the attenuation rate coefficient of the energy storage module, which is a negative number; This represents the number of cycles for the energy storage module. The initial attenuation coefficient and attenuation rate coefficient can be obtained from the product parameter table or through experiments.

[0030] In other words, the health status value of the energy storage module is a parameter value that is dynamically updated as the energy storage module is used.

[0031] Step S203: Determine the operation strategy of the photovoltaic-storage system based on the preset objective function. The operation strategy includes the operating power of the heat pump load, the charging and discharging power of the energy storage modules, and the interaction power between the photovoltaic-storage system and the external public power grid. The weight coefficients of each item in the preset objective function are determined by a pre-built intelligent decision-making model based on operating data and decision reference factors.

[0032] Specifically, the operating power of the heat pump load refers to the power at which the heat pump load operates. The charging and discharging power of the energy storage module refers to whether the energy storage module is charging or discharging, and the charging and discharging power. The interaction power between the photovoltaic-energy storage system and the external public grid refers to whether the photovoltaic-energy storage system supplies power to or draws power from the external public grid.

[0033] Furthermore, the preset objective function is: ; in, This indicates finding the minimum value; This indicates the electricity cost for the specified time period; This indicates the actual indoor temperature after a set time period. This indicates the set indoor temperature; This indicates the depth of discharge of the energy storage module after a set time period. The depth of discharge of the energy storage module is added to its state of charge (SOC) value, which equals 100%. , , These represent the electricity cost weighting coefficient, temperature control weighting coefficient, and energy storage safety weighting coefficient, respectively.

[0034] It should be noted that the term "set time period" in this embodiment refers to the adjustment cycle time of the preset photovoltaic system operation strategy, or the set operation time of the operation strategy after it is determined.

[0035] In other words, determining the operating strategy of the photovoltaic-storage system based on a preset objective function is essentially determining an operating strategy that minimizes the objective function. This can be achieved using methods such as exhaustive search or genetic algorithms. The relationships between the preset objective function and the operating strategy include: the operating power of the heat pump load is related to the actual indoor temperature after a set time period, i.e., the difference between the actual indoor temperature after the set time period and the set indoor temperature; the charging and discharging power of the energy storage modules is related to the depth of discharge of the energy storage modules after the set time period; and the interaction power between the photovoltaic-storage system and the external public power grid is related to the electricity cost within the set time period.

[0036] Additionally, refer to Figure 3 As shown, in one implementation, the process of constructing an intelligent decision-making model includes: Step S301: Receive multiple sets of training baseline data. The training baseline data includes the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, state of charge of the energy storage module, preset energy efficiency ratio curve of the heat pump load, health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. Specifically, the training baseline data is based on operational data and decision-making reference factors.

[0037] Step S302: Obtain the preset allowed range of values ​​for each weight coefficient of each set of training data. Specifically, when inputting the training data, the preset allowed range of values ​​for each weight coefficient of each set of training data is also input, thereby preventing the weight coefficients from deviating from the actual requirements.

[0038] For example, when the real-time electricity price is the preset peak price, the electricity cost weighting coefficient needs to be greater than a set value to ensure that the objective function value is reduced by saving electricity costs, rather than by excessively reducing the electricity cost weighting coefficient; when the difference between the actual indoor temperature and the set indoor temperature is greater than a preset temperature threshold, the temperature control weighting coefficient needs to be greater than a set value to ensure the importance of reducing the difference between the actual indoor temperature and the set indoor temperature as quickly as possible; when the health status value of the energy storage module is less than a preset health threshold, the energy storage safety weighting coefficient needs to be greater than a set value to ensure the importance of protecting the energy storage module.

[0039] In other words, for the preset allowable range of values ​​for each weighting coefficient, the electricity cost weighting coefficient is positively correlated with the real-time electricity price, the temperature control weighting coefficient is positively correlated with the difference between the actual indoor temperature and the set indoor temperature, and the energy storage safety weighting coefficient is negatively correlated with the health status value of the energy storage module.

[0040] Step S303: Calculate the optimal weight coefficients corresponding to the minimum value of the preset objective function for each set of training data using a preset algorithm. Specifically, the preset algorithm can be an exhaustive search method, a Gauss-Newton algorithm, a genetic algorithm, etc.

[0041] Step S304 involves using multiple sets of training data as input values ​​for the model to be trained, and using the optimal weight coefficients corresponding to each set of training data as output values ​​for the model to be trained, thereby training the model to obtain an intelligent decision-making model. Specifically, the intelligent decision-making model can be a neural network model.

[0042] Step S204: Control the operation of the photovoltaic energy storage system according to the determined operation strategy. Specifically, this means controlling the operation of each device according to the operating power of each device in the operation strategy.

[0043] In this embodiment, the operation data of the photovoltaic-storage system is acquired, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, and the state of charge of the energy storage module. Decision reference factors for the photovoltaic-storage system are also acquired, including the preset energy efficiency ratio curve of the heat pump load, the health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. Then, the operation strategy of the photovoltaic-storage system is determined according to a preset objective function. The operation strategy includes the operating power of the heat pump load, the charging and discharging power of the energy storage module, and the interaction power between the photovoltaic-storage system and the external public power grid. The weight coefficients of each item in the preset objective function are determined by a pre-constructed intelligent decision-making model based on the operation data and decision reference factors. Finally, the operation of the photovoltaic-storage system is controlled according to the determined operation strategy. Therefore, this solution can quickly and intelligently generate an operating strategy that achieves a good balance between electricity costs, user comfort, and the safety of energy storage modules by acquiring sufficient decision-making data and comprehensively considering the three factors of electricity costs, user comfort, and the safety of energy storage modules. This operating strategy takes into account the benefits of electricity costs, user comfort, and the safety of energy storage modules, thereby helping to improve the synergy of various devices in the photovoltaic-energy storage system, and ultimately improving the overall benefits of the photovoltaic-energy storage system in terms of energy utilization efficiency, economic benefits, and user experience.

[0044] Furthermore, by constructing an intelligent decision-making model to determine the weight coefficients of the preset objective function, the values ​​of each weight coefficient can be determined more quickly, improving decision-making efficiency. By using a pre-set energy storage health model to determine the health status value of the energy storage module, the system can dynamically update the health status value in real time based on the usage of the energy storage module, thus ensuring better timeliness and accuracy of the energy storage module's health status value.

[0045] It should be noted that in some other embodiments, the health status value of the energy storage module can also be changed by the user periodically in the system.

[0046] It should be noted that there may be other electrical loads in the photovoltaic energy storage system besides the heat pump, such as refrigerators and lights. During the decision-making process, the operating power of other electrical loads can be set within their respective allowable operating power ranges.

[0047] like Figure 4 As shown, in one embodiment, the intelligent control method for a photovoltaic energy storage system generally includes: Step S401: Obtain the operating data of the optical storage system.

[0048] Step S402: Obtain decision-making reference factors for the photovoltaic storage system.

[0049] Step S403: Determine the operation strategy of the photovoltaic storage system according to the preset objective function.

[0050] Step S404: Control the operation of the photovoltaic storage system according to the determined operating strategy.

[0051] The contents of steps S401 to S404 are the same as those described in steps S201 to S204.

[0052] Step S405: Obtain the trigger event for ending the operation strategy. Specifically, the trigger event can be the operation strategy reaching a preset operating time, i.e., the time period mentioned above. Alternatively, it can be the preset operating parameters of the photovoltaic-storage system reaching a set parameter threshold, such as the energy storage module's state of charge reaching the minimum allowable value or the energy storage module's state of charge reaching the maximum allowable value, or the absolute value of the difference between the real-time photovoltaic output power and the photovoltaic output power used in the determined operation strategy reaching the set parameter threshold. Additionally, it can be a change in the set indoor temperature.

[0053] The steps of acquiring the operating data of the photovoltaic storage system, acquiring the decision reference factors of the photovoltaic storage system, and determining the operating strategy of the photovoltaic storage system according to the preset objective function are re-executed. That is, steps S401 to S404 are re-executed to generate a new operating strategy and control the photovoltaic storage system to operate according to the new operating strategy.

[0054] In this embodiment, after receiving a trigger event for ending the operation strategy, the steps of acquiring the operating data of the photovoltaic storage system, acquiring the decision reference factors of the photovoltaic storage system, and determining the operating strategy of the photovoltaic storage system according to a preset objective function are re-executed. In other words, the photovoltaic storage system can dynamically update its operating strategy based on the trigger event for ending the operation strategy and control the photovoltaic storage system to operate according to the new operating strategy, thereby improving the flexibility of the operating strategy and enabling it to better cope with unexpected situations.

[0055] like Figure 5 As shown, in one embodiment, after obtaining the trigger event for ending the operation strategy, the method further includes: obtaining the electricity cost used by the operation strategy, the actual indoor temperature, the discharge depth of the energy storage module, and the actual value of the preset objective function; if the absolute value of the difference between the actual value of the preset objective function and the original predicted value of the preset objective function and the ratio of the original predicted value of the preset objective function exceed a preset threshold, the operation data and decision reference factors corresponding to the operation strategy are used as supplementary training data, and the supplementary optimal weight coefficient values ​​corresponding to the minimum value of the preset objective function are calculated. The supplementary training data and the supplementary optimal weight coefficient values ​​are used as the model input and model output values, respectively, to perform compensatory training on the intelligent decision model.

[0056] Specifically, in this embodiment, the intelligent control method for the photovoltaic energy storage system generally includes: Step S501: Obtain the trigger event for ending the running strategy. The trigger event is when the running strategy reaches the preset running time.

[0057] Step S502: Obtain the electricity cost used for the operation strategy, the actual indoor temperature, the discharge depth of the energy storage module, and the actual value of the preset objective function. Specifically, this means obtaining the electricity cost used from the start of operation according to the operation strategy until the trigger event for ending the operation strategy is obtained, obtaining the actual indoor temperature at the trigger event for ending the operation strategy, obtaining the discharge depth of the energy storage module at the trigger event for ending the operation strategy, and obtaining the actual value of the preset objective function at the trigger event for ending the operation strategy, calculated based on the difference between the electricity cost used, the actual indoor temperature, and the set indoor temperature, and the discharge depth of the energy storage module.

[0058] If the absolute value of the difference between the actual value and the original predicted value of the preset objective function is detected, and the ratio of the original predicted value of the preset objective function to the preset predicted value exceeds a preset threshold, step S503 is executed. Specifically, the original predicted value of the preset objective function is the preset objective function value calculated when determining the operating strategy.

[0059] Step S503: Use the running data and decision reference factors corresponding to the running strategy as supplementary training data and calculate the supplementary optimal weight coefficient values ​​corresponding to the minimum value of the preset objective function.

[0060] Step S504: Use the supplementary training base data and the supplementary optimal weight coefficients as the model input and model output values, respectively, to supplement the training of the intelligent decision-making model.

[0061] Specifically, that is, referring to Figure 3 The process shown utilizes supplementary training data and supplementary optimal weight coefficients to supplement the training of the intelligent decision-making model.

[0062] In addition, in step S503, the allowable range of values ​​for the electricity cost weight coefficient of the supplementary training base data is determined based on the electricity cost already used, the original estimated electricity cost of the operation strategy (the electricity cost when the operation strategy was determined), and the original electricity cost weight coefficient of the operation strategy. For example, if the electricity cost already used exceeds the original estimated electricity cost of the operation strategy by a large margin, the allowable range is set such that the minimum value of the allowable range is greater than the original electricity cost weight coefficient.

[0063] The allowable range of temperature control weight coefficients for supplementary training data is determined based on the actual indoor temperature, the original projected indoor temperature of the operating strategy (the indoor temperature expected to be reached after the set operating period when the operating strategy is determined), and the original temperature control weight coefficients of the operating strategy. For example, if the operating strategy is to raise the indoor temperature, and the actual indoor temperature does not reach the original projected indoor temperature and the difference is large, the allowable range is set such that the minimum value of the allowable range is greater than the original temperature control weight coefficient.

[0064] The allowable range of values ​​for the energy storage safety weight coefficient in the supplementary training data is determined based on the energy storage module's discharge depth, the original projected discharge depth of the operating strategy (the expected discharge depth of the energy storage module after a set operating period when the operating strategy is determined), and the original energy storage safety weight coefficient of the operating strategy. For example, if the operating strategy is for the energy storage module to discharge, and the actual discharge depth is greater than the original projected discharge depth by a significant margin, the allowable range is set such that the minimum value of the allowable range is greater than the original energy storage safety weight coefficient.

[0065] In this embodiment, after receiving the trigger event for ending the operation strategy, the system acquires the electricity cost used, actual indoor temperature, depth of discharge of the energy storage module, and actual value of the preset objective function. If the ratio of the absolute value of the difference between the actual value and the original predicted value of the preset objective function to the original predicted value exceeds a preset threshold, the operation data and decision reference factors corresponding to the operation strategy are used as supplementary training data, and the supplementary optimal weight coefficients corresponding to the minimum value of the preset objective function are calculated. Then, the supplementary training data and the supplementary optimal weight coefficients are used as the model input and output values, respectively, to supplement the training of the intelligent decision-making model. This continuously optimizes the intelligent decision-making model and improves its accuracy.

[0066] like Figure 6 As shown, in one embodiment, the step of determining the operation strategy according to the preset objective function further includes: determining the operation strategy by combining the preset objective function and preset constraints; the preset constraints include: controlling the grid reverse power to be less than a preset power threshold, where the grid reverse power is the power transmitted from the photovoltaic energy storage system to the external public grid; controlling the difference between the actual indoor temperature and the set indoor temperature after a set time period to be within a preset temperature fluctuation range; and controlling the state of charge of the energy storage module after a set time period to be within a preset state of charge range.

[0067] like Figure 6 As shown, in this embodiment, the intelligent control method for the photovoltaic energy storage system generally includes: Step S601: Obtain the operating data of the optical storage system.

[0068] Step S602: Obtain decision-making reference factors for the photovoltaic storage system.

[0069] The contents of steps S601 to S602 are the same as those described in steps S201 to S202.

[0070] Step S603: Determine the operating strategy by combining the preset objective function and preset constraints. The preset constraints include: The reverse power of the power grid is controlled to be less than a preset power threshold. Reverse power refers to the power transmitted from the photovoltaic-storage system to the external public power grid. Specifically, by controlling the reverse power to be less than the preset power threshold, excessive reverse power can be avoided, preventing it from affecting the stability of the power grid. Simultaneously, the utilization rate of photovoltaic power generation is improved.

[0071] The difference between the actual indoor temperature and the set indoor temperature after a set time period is kept within the preset temperature fluctuation range. This helps ensure user comfort and improves the user experience.

[0072] This controls the state of charge (SOC) of the energy storage module to remain within a preset range after a set time period. This helps prevent the energy storage module from being overcharged or over-discharged.

[0073] Step S604: Control the operation of the photovoltaic storage system according to the determined operation strategy.

[0074] In one embodiment, the decision-making reference factors also include user instructions. During the process of determining the operating strategy of the photovoltaic storage system based on a preset objective function: If the user command is a preset power-saving priority command, the electricity cost weighting coefficient is set to the maximum; if the user command is a preset comfort priority command, the temperature control weighting coefficient is set to the maximum; if the user command is a preset safety priority command, the energy storage safety weighting coefficient is set to the maximum. By configuring user commands as a decision-making reference factor, the human-machine interaction between the photovoltaic energy storage system and the user is improved, making the operating strategy more in line with user needs.

[0075] This embodiment also provides a computer device and a computer-readable storage medium. Figure 7 This is a schematic diagram of a computer device according to an embodiment of the present invention. Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.

[0076] The computer device 10 may include a memory 110, a processor 120, and a computer-executable program 11 stored on the memory 110 and running on the processor 120. When the processor 120 executes the computer-executable program 11, it implements the intelligent control method of the optical storage system of any of the above embodiments.

[0077] The computer-readable storage medium 20 stores a computer-executable program 11 thereon, which, when executed by a processor, implements the intelligent control method of the optical storage system of any of the above embodiments.

[0078] This embodiment also provides a computer program product. Figure 9 This is a schematic diagram of a computer program product according to an embodiment of the present invention. The computer program product 30 includes a computer-executable program 11, which, when executed by a processor 120, implements the intelligent control method of the optical storage system of any of the embodiments described above.

[0079] Specifically, the computer executable program 11 used to perform the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, computer instructions, computer-related instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages.

[0080] For the purposes of this embodiment, the computer-readable storage medium 20 can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable storage medium 20 can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0081] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0082] Computer device 10 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 10 can be a cloud acquisition node. Computer device 10 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 10 can be implemented in a distributed cloud acquisition environment where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud acquisition environment, program modules can reside on local or remote acquisition system storage media, including storage devices.

[0083] Computer device 10 may include a processor 120 adapted to execute stored instructions and a memory 110 that provides temporary storage space for the operation of said instructions during operation. Processor 120 may be a single-core processor, a multi-core processor, an acquisition cluster, or any other configuration. Memory 110 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0084] The processor 120 can be connected via a system interconnect (e.g., PCI, PCI-Express, etc.) to an I / O interface (input / output interface) suitable for connecting the computer device 10 to one or more I / O devices (input / output devices). I / O devices may include, for example, a keyboard and indicating devices, where indicating devices may include a touchpad or touchscreen, etc. I / O devices may be built into the computer device 10 or may be external devices connected to the acquisition device.

[0085] The processor 120 may also be linked via a system interconnect to a display interface suitable for connecting the computer device 10 to a display device. The display device may include a display screen that is a built-in component of the computer device 10. The display device may also include an external computer monitor, television, or projector connected to the computer device 10. Furthermore, a network interface controller (NIC) may be adapted to connect the computer device 10 to a network via a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as an Internet Minicomputer System Interface) to transmit data. The network may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. Remote devices may connect to the computer device via the network.

[0086] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A smart control method for a photovoltaic-energy storage system, the photovoltaic-energy storage system comprising a photovoltaic module, an energy storage module, and a heat pump load, characterized in that, The intelligent control method includes: The system acquires operational data of the photovoltaic-storage system, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, and state of charge of the energy storage module. The decision reference factors for the photovoltaic-storage system are obtained, including the preset energy efficiency ratio curve of the heat pump load, the health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. The operation strategy of the photovoltaic-storage system is determined according to a preset objective function. The operation strategy includes the working power of the heat pump load, the charging and discharging power of the energy storage module, and the interaction power between the photovoltaic-storage system and the external public power grid. The weight coefficients of each item of the preset objective function are determined by a pre-constructed intelligent decision-making model based on the operation data and the decision reference factors. The operation of the photovoltaic storage system is controlled according to the determined operating strategy.

2. The intelligent control method for a photovoltaic energy storage system according to claim 1, characterized in that... The preset objective function is: ； in, This indicates finding the minimum value; This indicates the electricity cost for the specified time period; This indicates the actual indoor temperature after a set time period. Indicates the set indoor temperature; This indicates the depth of discharge of the energy storage module after a set time period. , , These represent the electricity cost weighting coefficient, temperature control weighting coefficient, and energy storage safety weighting coefficient, respectively.

3. The intelligent control method for a photovoltaic energy storage system according to claim 2, characterized in that... The construction process of the intelligent decision-making model includes: Receive multiple sets of training basic data, including the photovoltaic output power of the photovoltaic module, outdoor ambient temperature, actual indoor temperature, set indoor temperature, real-time electricity price, state of charge of the energy storage module, preset energy efficiency ratio curve of the heat pump load, health status value of the energy storage module, and the difference between the actual indoor temperature and the set indoor temperature. Obtain the preset allowed range of values ​​for each weight coefficient of each group of the training base data; The optimal weight coefficients corresponding to the minimum value of the preset objective function for each set of training data are calculated using a preset algorithm. The intelligent decision-making model is obtained by using the multiple sets of training data as the model input values ​​of the model to be trained, and using the optimal weight coefficient values ​​corresponding to each set of training data as the model output values ​​of the model to be trained.

4. The intelligent control method for a photovoltaic energy storage system according to claim 3, characterized in that... Obtain the trigger event that terminates the aforementioned running strategy; Re-execute the steps of acquiring the operating data of the photovoltaic storage system, acquiring the decision reference factors of the photovoltaic storage system, and determining the operating strategy of the photovoltaic storage system according to the preset objective function.

5. The intelligent control method for a photovoltaic energy storage system according to claim 4, characterized in that... The step of obtaining the trigger event for ending the running strategy further includes: Obtain the electricity cost used for the operation strategy, the actual indoor temperature, the depth of discharge of the energy storage module, and the actual value of the preset objective function; If the absolute value of the difference between the actual value and the original predicted value of the preset objective function and the ratio of the original predicted value of the preset objective function exceeds a preset threshold, the running data and the decision reference factors corresponding to the running strategy are used as supplementary training data, and the supplementary optimal weight coefficient values ​​corresponding to the minimum value of the preset objective function are calculated. The supplementary training data and the supplementary optimal weight coefficient values ​​are used as model input values ​​and model output values, respectively, to supplement the training of the intelligent decision model.

6. The intelligent control method for a photovoltaic energy storage system according to claim 2, characterized in that... The step of determining the operating strategy based on the preset objective function further includes: The operating strategy is determined by combining the preset objective function and preset constraints; The preset constraints include: The reverse power of the power grid is controlled to be less than a preset power threshold, wherein the reverse power of the power grid is the power transmitted by the photovoltaic energy storage system to the external public power grid; The difference between the actual indoor temperature after the set time period and the set indoor temperature is controlled to be within a preset temperature fluctuation range. The energy storage module is controlled to maintain its state of charge within a preset range after a set time period.

7. The intelligent control method for a photovoltaic energy storage system according to claim 2, characterized in that... The health status value of the energy storage module is determined by a pre-set energy storage health model; The pre-set energy storage health model is as follows: ； in, The health status value; The initial attenuation coefficient of the energy storage module; It is a natural constant; The attenuation rate coefficient of the energy storage module is a negative number. The number of cycles for the energy storage module.

8. The intelligent control method for a photovoltaic energy storage system according to claim 2, characterized in that... The decision-making reference factors also include user instructions; In the process of determining the operation strategy of the photovoltaic energy storage system according to the preset objective function: If the user instruction is a preset power-saving priority instruction, the electricity cost weighting coefficient is set to the maximum. If the user instruction is a preset comfort-first instruction, set the temperature control weighting coefficient to the maximum. If the user instruction is a preset safety priority instruction, the energy storage safety weight coefficient is set to the maximum.

9. A computer device, characterized in that, The system includes a memory, a processor, and a computer-executable program stored in the memory and running on the processor, wherein the processor executes the computer-executable program to implement the intelligent control method for the optical storage system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer-executable program, which, when executed by a processor, implements the intelligent control method of the optical storage system according to any one of claims 1 to 8.

11. A computer program product, characterized in that, It includes a computer-executable program, which, when executed by a processor, implements the intelligent control method for the optical storage system according to any one of claims 1 to 8.

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