A charging and discharging control method, product, device and medium for photovoltaic energy storage system

Abstract

The present application relates to the field of photovoltaic energy storage technology, and in particular to a method, product, device, and medium for controlling charging and discharging of a photovoltaic energy storage system. The method comprises: predicting the future light intensity and future electricity load of the next cycle, where the future electricity load includes power-consuming time periods and non-power-consuming time periods; adjusting the charging parameters of the battery pack of the photovoltaic energy storage system based on the future light intensity; obtaining the historical charging data of the battery pack, and determining a first correspondence between light intensity and charging rate based on the historical charging data; formulating a charging and discharging strategy based on the future light intensity, future electricity load, charging parameters, and the first correspondence, and controlling the charging and discharging of the photovoltaic energy storage system based on the charging and discharging strategy. The present application can improve the flexible adaptability of the energy storage system under different light and power conditions.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic energy storage technology, and in particular to a charge and discharge control method, product, device, and medium for a photovoltaic energy storage system. Background Art

[0002] As global demand for clean energy grows, photovoltaic energy storage systems are attracting widespread attention due to their environmental and economic advantages. While existing photovoltaic energy storage systems can achieve basic energy storage and release functions, they still need to improve in terms of efficiency, stability, and intelligent scheduling.

[0003] Currently, most photovoltaic energy storage systems use a fixed threshold method for charge and discharge management. This method begins charging when the battery pack's SOC (State of Charge) falls below a preset value, and stops charging and enters discharge mode when it rises above another preset value. This simple strategy cannot flexibly adapt to changes in energy supply and demand under varying lighting conditions, and can easily cause battery overcharge or over-discharge, reducing the overall efficiency of the energy storage system. Summary of the Invention

[0004] In order to solve the problem of insufficient flexibility of photovoltaic energy storage systems in the prior art, the present application provides a charging and discharging control method, product, device and medium for a photovoltaic energy storage system.

[0005] In a first aspect, the present application provides a method for controlling charging and discharging of a photovoltaic energy storage system, which adopts the following technical solutions:

[0006] A charge and discharge control method for a photovoltaic energy storage system, comprising:

[0007] Predicting future light intensity and future electricity load for the next cycle, wherein the future electricity load includes a power-on time period and a non-power-on time period;

[0008] adjusting charging parameters of a battery pack of a photovoltaic energy storage system based on the future light intensity;

[0009] Acquiring historical charging data of the battery pack, and determining a first correspondence between light intensity and charging rate based on the historical charging data;

[0010] A charging and discharging strategy is formulated based on the future light intensity, the future power load, the charging parameter, and the first corresponding relationship, and the photovoltaic energy storage system is controlled to perform charging and discharging based on the charging and discharging strategy.

[0011] In a preferred example, the present application may be further configured as follows: the charging parameters include an upper limit of SOC and a lower limit of SOC; and the charging parameters of the battery pack of the photovoltaic energy storage system are adjusted based on the future light intensity, including:

[0012] generating a light intensity curve of light intensity varying with time based on the future light intensity, and dividing the light intensity curve into a plurality of sub-curves;

[0013] Obtaining an SOC upper limit range and an SOC lower limit range of the battery pack;

[0014] Calculate the average light intensity within each sub-curve;

[0015] The SOC upper limit of each sub-curve is determined from the SOC upper limit range based on the average light intensity, and the SOC lower limit of each sub-curve is determined from the SOC lower limit range. The SOC upper limit represents a charge stop threshold, and the SOC lower limit represents a discharge stop threshold.

[0016] In a preferred example, the present application may be further configured as follows: determining the SOC upper limit of each sub-curve from the SOC upper limit range and determining the SOC lower limit of each sub-curve from the SOC lower limit range based on the average light intensity includes:

[0017] Dividing the SOC upper limit range into a plurality of first sub-ranges, dividing the SOC lower limit range into a plurality of second sub-ranges, and calculating an average SOC upper limit of each first sub-range and an average SOC lower limit of each second sub-range, wherein the number of the plurality of first sub-ranges and the number of the plurality of second sub-ranges are both equal to the number of the plurality of sub-curves;

[0018] Arrange the multiple sub-curves from small to large according to the average light intensity to obtain a light intensity list;

[0019] Starting from the first sub-curve in the illumination intensity list, each average SOC upper limit is assigned to the multiple sub-curves one by one in ascending order, and each average SOC lower limit is assigned to the multiple sub-curves one by one in descending order.

[0020] In a preferred example, the present application may be further configured as follows: formulating a charging and discharging strategy based on the future light intensity, the future power load, the charging parameter, and the first corresponding relationship includes:

[0021] When the current time is in any non-power consumption time period, determining the start time of the next power consumption time period from the future power load as the discharge time of the battery pack, and using the time period between the current time and the discharge time as the target time period;

[0022] determining a charge amount within the target time period based on the future light intensity and the first corresponding relationship;

[0023] Obtaining a current SOC of the battery pack, determining a target SOC upper limit for the target time period from the charging parameter, and determining a to-be-charged amount of the battery pack based on the current SOC and the target SOC upper limit;

[0024] A charge and discharge strategy is formulated based on the charged amount and the amount to be charged.

[0025] In a preferred example, the present application may be further configured as follows: formulating a charge and discharge strategy based on the charged amount and the amount to be charged includes:

[0026] determining a second correspondence between light intensity and charging current based on historical charging data of the battery pack;

[0027] Comparing the charged capacity with the to-be-charged capacity, and if the charged capacity is greater than the to-be-charged capacity, calculating a ratio of the to-be-charged capacity to the charged capacity;

[0028] determining an initial charging current within the target time period based on the future light intensity and the second corresponding relationship;

[0029] The initial charging current is adjusted based on the ratio to obtain a target charging current, so as to control the photovoltaic energy storage system to charge the battery pack according to the target charging current within the target time period.

[0030] In a preferred example, the present application may be further configured as follows: adjusting the initial charging current based on the ratio to obtain a target charging current includes:

[0031] Calculating the product of the initial charging current and the ratio at each moment in the target time period as the intermediate charging current at the corresponding moment;

[0032] Get the upper limit of charging current;

[0033] The difference between the intermediate charging current at each moment and the upper limit of the charging current is determined, and the intermediate charging current within the target time period is adjusted based on the difference obtained at each moment to obtain an adjusted target charging current.

[0034] In a preferred example, the present application may be further configured as follows: the prediction of the future light intensity and future power load in the next cycle includes:

[0035] Acquire historical light intensity data and historical electricity load data, and process the historical light intensity data and the historical electricity load data using a Kalman filter algorithm;

[0036] Training a prediction model based on the historical light intensity data and the historical electricity load data;

[0037] The future illumination intensity and future electricity load of the next cycle are predicted by the prediction model.

[0038] In a second aspect, the present application provides a computer program product that employs the following technical solution:

[0039] A computer program product includes a computer program. When the computer program is executed by a processor, it implements the charge and discharge control method of the photovoltaic energy storage system as described in any one of the first aspects.

[0040] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0041] one or more processors;

[0042] Memory;

[0043] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the charge and discharge control method for the photovoltaic energy storage system as described in any one of the first aspects.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0045] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the charge and discharge control method for a photovoltaic energy storage system as described in any one of the first aspects.

[0046] In summary, this application has the following beneficial technical effects:

[0047] By predicting light intensity and power load, this application can more accurately determine when there is sufficient solar energy available for charging and when discharge is needed to meet power demand, thereby achieving optimal energy allocation. Adjusting charging parameters according to light intensity can avoid overcharging or over-discharging of the battery, thereby extending the battery life. Analyzing historical charging data can understand the optimal charging rate of the battery under different light intensities, so as to formulate personalized charging strategies. Based on the corresponding relationship of historical data, the charging process can be more accurately controlled to improve overall performance and efficiency. By formulating intelligent charging and discharging strategies and accurately controlling the charging and discharging process, a stable operating state can be maintained, avoiding system fluctuations caused by mismatch between energy supply and demand, and improving the flexible adaptability of the energy storage system under different light and power conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for controlling charging and discharging of a photovoltaic energy storage system provided in an embodiment of the present application;

[0049] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following is combined with Figure 1 -Attached Figure 2 This application is described in further detail.

[0051] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0052] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0054] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0055] The present application provides a method for controlling charging and discharging of a photovoltaic energy storage system. Figure 1As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein:

[0056] S101 . Predict the future light intensity and future electricity load of the next cycle, where the future electricity load includes a power-consuming time period and a non-power-consuming time period.

[0057] Specifically, a prediction model is pre-trained, and the prediction model is used to predict future light intensity and future electricity load. Historical light intensity data and historical electricity load data over a period of time are obtained from a weather station or a historical database. The duration of the historical light intensity data and historical electricity load data can be flexibly selected according to actual needs. The obtained historical light intensity data and historical electricity load data are used as inputs to the prediction model, and the prediction model outputs the future light intensity and future electricity load of the next cycle. The duration of the cycle can be set according to actual needs, and this embodiment does not make specific restrictions. The predicted future electricity load includes several power consumption time periods and several non-power consumption time periods. The future light intensity and future electricity load of the next cycle include the future light intensity and future electricity load at multiple moments. Each moment can be used as an adjustment moment for subsequent dynamic adjustment of the charge and discharge strategy. The time difference between two adjacent moments can be set according to actual needs, and this embodiment does not make specific restrictions.

[0058] S102: Adjust charging parameters of the battery pack of the photovoltaic energy storage system based on future light intensity.

[0059] Specifically, charging parameters include an upper SOC limit and a lower SOC limit. During charging, if the SOC reaches the upper limit, the system automatically stops charging, indicating that the battery pack is fully charged. During discharge, if the SOC drops to the lower limit, the battery pack stops discharging. The lower SOC limit represents the state when the battery pack has discharged to the lowest safe level. Dynamically adjusting charging parameters based on future changes in light intensity can extend the battery pack's lifespan.

[0060] S103: Acquire historical charging data of the battery pack, and determine a first correspondence between light intensity and charging rate based on the historical charging data.

[0061] Specifically, historical charging data includes the change of light intensity over time, real-time charging current and real-time charging rate (the amount of charge per unit time). When the light intensity at multiple moments in the historical charging data is equal, the average of the charging rates corresponding to these multiple moments is calculated as the charging rate under the light intensity.

[0062] S104: Formulate a charging and discharging strategy based on the future light intensity, the future power load, the charging parameters, and the first corresponding relationship, and control the photovoltaic energy storage system to charge and discharge based on the charging and discharging strategy.

[0063] Specifically, the charging and discharging strategy includes determining the charging parameters and charging current at each moment in the next cycle, so as to charge the battery pack according to the charging parameters and charging current during the non-power consumption period of the next cycle, and the photovoltaic energy storage system supplies power to the load during the power consumption period of the next cycle.

[0064] In one possible implementation of the embodiment of the present application, the charging parameters include an upper SOC limit and a lower SOC limit; and adjusting the charging parameters of the battery pack of the photovoltaic energy storage system based on future light intensity includes:

[0065] Generate a light intensity curve showing how light intensity changes over time based on future light intensity, and divide the light intensity curve into multiple sub-curves;

[0066] Get the SOC upper limit range and SOC lower limit range of the battery pack;

[0067] Calculate the average light intensity within each sub-curve;

[0068] Based on the average light intensity, the SOC upper limit of each sub-curve is determined from the SOC upper limit range, and the SOC lower limit of each sub-curve is determined from the SOC lower limit range. The SOC upper limit represents the stop charging threshold, and the SOC lower limit represents the stop discharging threshold.

[0069] In this embodiment, multiple light intensity ranges can be pre-set. The setting method is: select the minimum and maximum values ​​in the historical light intensity, divide the range between the minimum and maximum values, and the number of divisions can be flexibly set to obtain multiple light intensity ranges after division.

[0070] After obtaining the light intensity curve, take the first point of the light intensity curve as the first point, take the light intensity range of the first point as the first range, traverse each point after the first point in sequence until the traversed point is not in the first range, take the point as the second point, and divide the curve segment from the first point to the second point into the first curve segment; starting from the second point, take the light intensity range of the second point as the second range, traverse each point after the second point in sequence until the traversed point is not in the second range, take the point as the third point, and divide the curve segment from the second point to the third point into the second curve segment, and so on, to obtain a multi-segment sub-curve with the light intensity curve divided.

[0071] The battery pack's SOC upper and lower limits can be set by technicians based on the battery pack type, capacity, and usage conditions. The maximum value of the SOC upper limit range indicates the maximum safe value that can be selected for the SOC upper limit, and the minimum value of the SOC upper limit range can prevent the battery pack from being considered fully charged before it reaches the optimal charging state, thereby affecting the overall performance and life of the battery pack. The minimum value of the SOC lower limit range is used to avoid battery damage caused by excessive discharge, and the maximum value of the SOC lower limit range is used to ensure that the battery pack can provide sufficient power during the discharge process to avoid insufficient power supply due to insufficient discharge.

[0072] A possible implementation of the embodiment of the present application is to determine the SOC upper limit of each sub-curve from the SOC upper limit range and the SOC lower limit of each sub-curve from the SOC lower limit range based on the average light intensity, including:

[0073] Divide the SOC upper limit range into a plurality of first sub-ranges, divide the SOC lower limit range into a plurality of second sub-ranges, and calculate an average SOC upper limit of each first sub-range and an average SOC lower limit of each second sub-range, wherein the number of the plurality of first sub-ranges and the number of the plurality of second sub-ranges are both equal to the number of the plurality of sub-curves;

[0074] Arrange the multiple sub-curves from small to large according to the average light intensity to obtain a light intensity list;

[0075] Starting from the first sub-curve in the light intensity list, each average SOC upper limit is assigned to the multiple sub-curves one by one in ascending order, and each average SOC lower limit is assigned to the multiple sub-curves one by one in descending order.

[0076] In this embodiment, the number of sub-curve segments can be determined first, and then the upper and lower SOC ranges can be divided evenly based on this number. Ultimately, the upper and lower SOC limits for each sub-curve segment can be determined. The higher the light intensity, the higher the upper SOC limit and the lower the lower SOC limit. Each sub-curve segment corresponds to a time period, and within this time period, the upper and lower SOC limits of the battery pack are determined. As the time period changes, the upper and lower SOC limits of the battery pack change dynamically.

[0077] In a specific application scenario, if a long period of rainy weather is expected on a certain day, the electronic equipment will lower the SOC discharge threshold in advance to reserve more power storage space to prepare for subsequent high power consumption periods.

[0078] A possible implementation of the embodiment of the present application is to formulate a charging and discharging strategy based on future light intensity, future power load, charging parameters, and the first correspondence, including:

[0079] When the current time is in any non-power consumption time period, the start time of the next power consumption time period is determined from the future power load as the discharge time of the battery pack, and the time period between the current time and the discharge time is used as the target time period;

[0080] determining a charge amount within a target time period based on the future light intensity and the first correspondence;

[0081] Obtaining the current SOC of the battery pack, determining a target SOC upper limit for a target time period from the charging parameters, and determining the remaining charge capacity of the battery pack based on the current SOC and the target SOC upper limit;

[0082] Develop charging and discharging strategies based on the amount of charge and the amount to be charged.

[0083] In this embodiment, the target time period is the time window during which the battery pack can be charged before being discharged. The light intensity at each time point in the target time period is determined from the future light intensity. The light intensity at each time point is converted into a charging rate using the first correspondence (the correspondence between light intensity and charging rate). Based on the charging rate at each time point in the target time period, the charge capacity that the battery pack can obtain during the target time period is calculated.

[0084] The current SOC of the battery pack is obtained through the battery management system or sensor. The charging parameters include the charging parameters at each time point in the next cycle. The target SOC upper limit of the battery pack in the target time period is found from the charging parameters. Among them, the light intensity in the next cycle is represented as multiple sub-curves, and the time period corresponding to each sub-curve is regarded as a sub-time period. The charging parameters in each sub-time period are fixed. In one case, the target time period completely overlaps with a sub-time period or is contained in a sub-time period; in another case, there are multiple small time periods in the target time period, and each small time period belongs to a sub-time period. In this case, any small time period is used as the target time period to formulate a charge and discharge strategy, and finally the charge and discharge strategies of each small time period are obtained, which are combined into a charge and discharge strategy between the current moment and the discharge moment.

[0085] Furthermore, the SOC difference between the target SOC upper limit and the current SOC is calculated, and then the rated capacity of the battery pack is obtained, and the product of the rated capacity and the SOC difference is calculated as the amount of charge to be taken from the battery pack.

[0086] A possible implementation of the embodiment of the present application is to formulate a charge and discharge strategy based on the charged amount and the amount to be charged, including:

[0087] determining a second correspondence between light intensity and charging current based on historical charging data of the battery pack;

[0088] Compare the charged capacity with the capacity to be charged. If the charged capacity is greater than the capacity to be charged, calculate the ratio of the capacity to be charged to the charged capacity.

[0089] determining an initial charging current within a target time period based on the future light intensity and a second corresponding relationship;

[0090] The initial charging current is adjusted based on the ratio to obtain a target charging current, so as to control the photovoltaic energy storage system to charge the battery pack according to the target charging current within a target time period.

[0091] In one possible scenario, the charged amount is not greater than the amount to be charged, indicating that the amount of electricity that can be charged into the battery pack within the target time period is insufficient to fully charge the battery pack. At this time, the initial charging current corresponding to the future light intensity at each moment within the target time period is determined according to the second corresponding relationship, and the battery pack is charged within the target time period based on the determined initial charging current.

[0092] In another possible situation, the charged amount is greater than the amount to be charged, the ratio of the amount to be charged to the charged amount is calculated, and the future light intensity is converted into a corresponding charging current using a second corresponding relationship, which is represented by the initial charging current.

[0093] A possible implementation of the embodiment of the present application is to adjust the initial charging current based on the ratio to obtain the target charging current, including:

[0094] Calculate the product of the initial charging current and the ratio at each moment in the target time period as the intermediate charging current at the corresponding moment;

[0095] Get the upper limit of charging current;

[0096] The difference between the intermediate charging current and the upper limit of the charging current at each moment is determined, and the intermediate charging current within the target time period is adjusted based on the difference obtained at each moment to obtain an adjusted target charging current.

[0097] In this embodiment, the intermediate charging current is adjusted based on the ratio between actual demand (to-be-charged capacity) and available resources (charged capacity). The maximum charging current (i.e., the upper limit of the charging current) for the battery pack is obtained from the battery management system or the battery pack's technical documentation. This represents the maximum current that can be safely charged. For each moment in the target time period, the difference between the intermediate charging current and the upper limit of the charging current is calculated. This difference reflects the margin for adjustment of the intermediate charging current without exceeding safety limits. A positive difference indicates that the intermediate charging current exceeds the upper limit and needs to be reduced. The sum of the positive differences is calculated as the first value to obtain the total current that needs to be reduced during the target time period. A negative difference indicates that the intermediate charging current is less than the upper limit of the charging current and can be increased to improve the charging rate. The sum of the absolute values ​​of the negative differences is calculated as the second value to obtain the margin for increased current.

[0098] When the first value is no greater than the second value, the total current that needs to be reduced is no greater than the available current that can be increased. This means that there is sufficient room to adjust the intermediate charging current to balance charging demand throughout the target time period without exceeding the upper limit of the charging current. Intermediate charging currents that exceed the upper limit are adjusted to equal the upper limit. The moment when the difference is negative is considered the time to increase the current. The ratio of the first value to the second value is calculated. For each time to increase the current, the product of the absolute value of the difference corresponding to that moment and the ratio is used as the adjustment difference. The sum of the intermediate charging current and the adjustment difference is used as the adjusted target charging current at that moment.

[0099] When the first value is greater than the second value, the total current that needs to be reduced is greater than the current headroom that can be increased, which means that there is insufficient room to adjust the intermediate charging current without exceeding the charging current upper limit. The moment when the difference is negative is used as the moment to be increased. For any moment to be increased, the intermediate charging current at that moment is adjusted to the charging current upper limit, and the adjusted current is used as the target charging current.

[0100] This embodiment automatically adjusts the charging current according to the current SOC state, future light intensity prediction and power load prediction to avoid overcharging and discharging.

[0101] A possible implementation of the embodiment of the present application is to predict the future light intensity and future power load of the next cycle, including:

[0102] Obtain historical light intensity data and historical electricity load data, and process the historical light intensity data and historical electricity load data using the Kalman filter algorithm;

[0103] Training prediction models based on historical light intensity data and historical electricity load data;

[0104] The prediction model is used to predict the future light intensity and future electricity load in the next cycle.

[0105] In this embodiment, the initial parameters of the Kalman filter are manually set based on the characteristics of historical light intensity data and historical electricity load data, including the initial state estimate, state transition matrix, observation matrix, process noise covariance, and observation noise covariance. The Kalman filter algorithm is applied to the historical light intensity data and the historical electricity load data, respectively. By iteratively updating the state estimate and error covariance, smoothed light intensity and electricity load series are obtained. Based on the smoothed data, features are extracted, such as the lag term, moving average, and trend term of the time series. External factors such as date (holiday, weekday) and weather conditions (sunny, rainy) can be considered as features. Based on the data characteristics and prediction requirements, an appropriate prediction model is selected, such as a time series model (ARIMA, SARIMA), a machine learning model (support vector machine, random forest, gradient boosting tree), or a deep learning model (LSTM, GRU). The prediction model is trained using the training set data, and the model parameters are adjusted to minimize the prediction error. The adaptive filter performance is regularly evaluated, and the model parameters are dynamically updated based on error feedback to continuously optimize energy conversion efficiency.

[0106] Using a trained prediction model, the system takes current and recent light intensity and power load data and predicts future light intensity and power load for the next cycle. The addition of an adaptive filter and optimized anti-interference strategy improves system stability in changing environments and extends battery life.

[0107] Furthermore, it can connect to microgrids, receiving weather forecasts and grid dispatch instructions via a remote communication interface, and dynamically adjusting charging and discharging plans based on grid demand. The system includes a controller with an embedded sensor network that monitors system operating status, including battery voltage, temperature, current, and ambient light intensity. It is equipped with high-performance computing and storage units, supporting complex algorithms and long-term data logging. It can also intelligently determine the optimal charging and discharging timing based on real-time grid electricity price fluctuations, maximizing economic benefits.

[0108] During implementation, the system monitors sunlight intensity through a built-in light sensor. Once a decreasing light trend is detected, the upper limit of the charging current is immediately lowered to prevent ineffective charging attempts due to insufficient sunlight, thereby saving energy. To further enhance the system's interoperability within a microgrid environment, a distributed consensus mechanism is introduced, enabling each energy storage node to autonomously negotiate the optimal charging and discharging strategy, achieving global optimization without the need for central scheduling. Each energy storage unit regularly exchanges its status information, including SOC, available capacity, and expected load demand. Distributed algorithms, such as the Raft consensus protocol, are run to ensure that all participating nodes jointly determine the optimal charging and discharging plan for the next time window. Based on this consensus result, each node synchronously updates its local policy table to guide subsequent operations. This achieves intelligent linkage under the distributed consensus mechanism, enhancing synergy with the microgrid, reducing operating costs, and improving the flexibility and reliability of the entire energy network.

[0109] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the contents shown in the embodiment of the charge and discharge control method of the photovoltaic energy storage system are implemented.

[0110] An electronic device is provided in an embodiment of the present application, such as Figure 2 As shown, Figure 2 The electronic device 200 shown includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in actual applications, the number of transceivers 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present application.

[0111] Processor 201 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 201 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0112] The bus 202 may include a path for transmitting information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but it does not mean that there is only one bus or one type of bus.

[0113] The memory 203 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0114] The memory 203 is used to store application code for executing the solution of the present application, and is controlled by the processor 201. The processor 201 is used to execute the application code stored in the memory 203 to implement the content shown in the embodiment of the charge and discharge control method of the photovoltaic energy storage system.

[0115] Figure 2 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0116] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the contents of the aforementioned embodiment of the method for controlling charging and discharging of a photovoltaic energy storage system.

[0117] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0118] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A charge and discharge control method for a photovoltaic energy storage system, characterized in that: include: Predicting future light intensity and future electricity load for the next cycle, wherein the future electricity load includes a power-on time period and a non-power-on time period; adjusting charging parameters of a battery pack of a photovoltaic energy storage system based on the future light intensity; Acquiring historical charging data of the battery pack, and determining a first correspondence between light intensity and charging rate based on the historical charging data; Formulate a charging and discharging strategy based on the future light intensity, the future power load, the charging parameter, and the first corresponding relationship, and control the photovoltaic energy storage system to charge and discharge based on the charging and discharging strategy; The charging parameters include an upper limit of SOC and a lower limit of SOC; and the charging parameters of the battery pack of the photovoltaic energy storage system adjusted based on the future light intensity include: generating a light intensity curve of light intensity varying with time based on the future light intensity, and dividing the light intensity curve into a plurality of sub-curves; Obtaining an SOC upper limit range and an SOC lower limit range of the battery pack; Calculate the average light intensity within each sub-curve; The SOC upper limit of each sub-curve is determined from the SOC upper limit range based on the average light intensity, and the SOC lower limit of each sub-curve is determined from the SOC lower limit range. The SOC upper limit represents a charge stop threshold, and the SOC lower limit represents a discharge stop threshold.

2. The charge and discharge control method of the photovoltaic energy storage system according to claim 1, characterized in that: The determining of the SOC upper limit of each sub-curve from the SOC upper limit range and the determining of the SOC lower limit of each sub-curve from the SOC lower limit range based on the average light intensity includes: Dividing the SOC upper limit range into a plurality of first sub-ranges, dividing the SOC lower limit range into a plurality of second sub-ranges, and calculating an average SOC upper limit of each first sub-range and an average SOC lower limit of each second sub-range, wherein the number of the plurality of first sub-ranges and the number of the plurality of second sub-ranges are both equal to the number of the plurality of sub-curves; Arrange the multiple sub-curves from small to large according to the average light intensity to obtain a light intensity list; Starting from the first sub-curve in the illumination intensity list, each average SOC upper limit is assigned to the multiple sub-curves one by one in ascending order, and each average SOC lower limit is assigned to the multiple sub-curves one by one in descending order.

3. The charge and discharge control method of the photovoltaic energy storage system according to claim 1, characterized in that: The formulating a charging and discharging strategy based on the future light intensity, the future power load, the charging parameter, and the first corresponding relationship includes: When the current time is in any non-power consumption time period, determining the start time of the next power consumption time period from the future power load as the discharge time of the battery pack, and using the time period between the current time and the discharge time as the target time period; determining a charge amount within the target time period based on the future light intensity and the first corresponding relationship; Obtaining a current SOC of the battery pack, determining a target SOC upper limit for the target time period from the charging parameter, and determining a to-be-charged amount of the battery pack based on the current SOC and the target SOC upper limit; A charge and discharge strategy is formulated based on the charged amount and the amount to be charged.

4. The charge and discharge control method of the photovoltaic energy storage system according to claim 3, characterized in that: The formulating a charge and discharge strategy based on the charged amount and the amount to be charged includes: determining a second correspondence between light intensity and charging current based on historical charging data of the battery pack; Comparing the charged capacity with the to-be-charged capacity, and if the charged capacity is greater than the to-be-charged capacity, calculating a ratio of the to-be-charged capacity to the charged capacity; determining an initial charging current within the target time period based on the future light intensity and the second corresponding relationship; The initial charging current is adjusted based on the ratio to obtain a target charging current, so as to control the photovoltaic energy storage system to charge the battery pack according to the target charging current within the target time period.

5. The charge and discharge control method of the photovoltaic energy storage system according to claim 4, characterized in that: The adjusting the initial charging current based on the ratio to obtain a target charging current includes: Calculating the product of the initial charging current and the ratio at each moment in the target time period as the intermediate charging current at the corresponding moment; Get the upper limit of charging current; The difference between the intermediate charging current at each moment and the upper limit of the charging current is determined, and the intermediate charging current within the target time period is adjusted based on the difference obtained at each moment to obtain an adjusted target charging current.

6. The charge and discharge control method of the photovoltaic energy storage system according to claim 1, characterized in that: The prediction of the future light intensity and future electricity load in the next cycle includes: Acquire historical light intensity data and historical electricity load data, and process the historical light intensity data and the historical electricity load data using a Kalman filter algorithm; Training a prediction model based on the historical light intensity data and the historical electricity load data; The future illumination intensity and future electricity load of the next cycle are predicted by the prediction model.

7. A computer program product, characterized in that The method comprises a computer program, which, when executed by a processor, implements the steps of the method for controlling charging and discharging of a photovoltaic energy storage system according to any one of claims 1 to 6.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the charge and discharge control method for a photovoltaic energy storage system according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the charge and discharge control method for a photovoltaic energy storage system according to any one of claims 1 to 6.

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