Control method and device of optical storage system, electronic equipment and readable storage medium

Abstract

The invention discloses a control method and device of an optical storage system, electronic equipment and a readable storage medium, and relates to the technical field of optical storage system control, and the method comprises the steps: obtaining the weather type forecast information of a to-be-predicted first preset time period, and obtaining the weather type forecast information of the to-be-predicted first preset time period according to the first preset time period and the position of the optical storage system; generating a photovoltaic power prediction parameter used for predicting the photovoltaic power generation power, and generating a first prediction factor used for adjusting the photovoltaic power prediction parameter according to the weather type prediction information; obtaining the photovoltaic power generation power of a second preset time period, and adjusting the first prediction factor according to the photovoltaic power generation power of the second preset time period to obtain a photovoltaic power prediction factor; generating photovoltaic power prediction information according to the photovoltaic power prediction parameters and the photovoltaic power prediction factors; and controlling a load connected with the optical storage system to operate based on the photovoltaic power prediction information. The method has the advantages that the accuracy of photovoltaic power generation prediction is improved, and therefore the energy scheduling strategy of the optical storage system is optimized.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic storage system control, and in particular to a control method, device, electronic device and readable storage medium of a photovoltaic storage system. Background Art

[0002] At present, the application of photovoltaic storage systems at home and abroad is becoming more and more common. With the development of technology, users' needs are also constantly changing, from the initial solar water heaters to photovoltaic inverters to power household loads, and then to photovoltaic storage systems to power household loads, so that the electricity from the photovoltaic storage system and the power grid can both power household loads. Users can decide whether the household loads are working or not, so as to realize the utilization of green electricity. However, when the household loads are not working, if there is excess green electricity, it cannot be fully utilized. In order to maximize the utilization of green electricity, one approach is to turn on the anti-reverse flow function, so that the photovoltaic storage system can generate electricity at its maximum power generation capacity, and the electricity that is not absorbed by the household loads can be fed back to the main power grid. However, the premise for the implementation of this solution is that local policies allow users to feed back electricity to the main power grid, resulting in poor adaptability of this solution.

[0003] In the related art, the scheduling of household loads is optimized by means of a household scheduling strategy of a photovoltaic storage system. For example, the green electricity surplus is calculated by predicting photovoltaic power generation, and the surplus green electricity is applied to the household load. However, the photovoltaic power generation of the photovoltaic storage system is affected by factors such as weather conditions, and there are unstable factors such as intermittency. If the photovoltaic power generation forecast is inaccurate and a large deviation occurs, most of the electricity used by the household load will be purchased from the municipal power grid, thereby failing to optimize the scheduling. Summary of the invention

[0004] The purpose of this application is to solve at least one of the technical problems existing in the prior art, and to provide a control method, device, electronic device and readable storage medium for a photovoltaic storage system, aiming to improve the accuracy of photovoltaic power generation prediction, thereby optimizing the energy scheduling strategy of the photovoltaic storage system.

[0005] In a first aspect, an embodiment of the present application provides a control method for a photovoltaic storage system, including: Acquire weather type forecast information for a first predetermined time period to be predicted, generate a photovoltaic power prediction parameter for predicting photovoltaic power generation power according to the first predetermined time period and the location of the photovoltaic storage system, and generate a first prediction factor for adjusting the photovoltaic power prediction parameter according to the weather type forecast information; Acquire photovoltaic power generation in a second predetermined time period, adjust the first prediction factor according to the photovoltaic power generation in the second predetermined time period, and obtain a photovoltaic power prediction factor, wherein the second predetermined time period is before the first predetermined time period; Generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor; Based on the photovoltaic power prediction information, the operation of a load connected to the photovoltaic storage system is controlled.

[0006] According to the technical solution of the embodiment of the present application, at least the following beneficial effects are achieved: when the photovoltaic storage system is in normal operation, the photovoltaic panels in the photovoltaic storage system can convert solar energy into electrical energy for consumption by the user's household load or for storage in the energy storage module, and the power generation power of the photovoltaic panels in the photovoltaic storage system is mainly affected by weather factors. Therefore, in order to accurately predict the photovoltaic power generation power, according to the first predetermined time period and the location of the photovoltaic storage system, the parameters such as the light and the sun's position in the first predetermined time period are judged, and then the photovoltaic power generation power is predicted. The first predetermined time period refers to a time period in the future, and the weather type forecast information of the first predetermined time period can be used to judge the degree of light affected in the first predetermined time period, and then generate a first prediction factor for adjusting the photovoltaic power prediction parameters. The first prediction factor makes the photovoltaic The power prediction parameters are more accurate, thereby improving the accuracy of photovoltaic power generation prediction; in addition, the second predetermined time period is before the first predetermined time period, and the photovoltaic power generation power of the second predetermined time period refers to the actual photovoltaic power generation power of the second predetermined time period. The second predetermined time period has photovoltaic power generation, which means that the photovoltaic storage system is in operation during the second predetermined time period. Therefore, the prediction deviation of the second predetermined time period can be judged through the actual photovoltaic power generation power of the second predetermined time period, the predicted photovoltaic power generation power of the second predetermined time period, the weather forecast of the second predetermined time period, the actual weather of the second predetermined time period and other factors, and then the first prediction factor is adjusted according to the prediction deviation, so that the predicted photovoltaic power generation power of the first predetermined time period is more accurate, thereby optimizing the energy scheduling strategy of the photovoltaic storage system.

[0007] According to some embodiments of the present application, generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor includes: Generate a first predicted photovoltaic power generation power according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor, wherein the first predicted photovoltaic power generation power includes the predicted photovoltaic power generation power within the first predetermined time period; The first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation power of multiple third predetermined time periods to obtain photovoltaic power prediction information, wherein the third predetermined time period is within the first predetermined time period and the third predetermined time period is less than the first predetermined time period.

[0008] According to some embodiments of the present application, the first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation power of multiple third predetermined time periods by an interpolation algorithm.

[0009] According to some embodiments of the present application, generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor includes: Photovoltaic installed capacity performance parameters are obtained, and photovoltaic power prediction information is generated according to the photovoltaic installed capacity performance parameters, the photovoltaic power prediction parameters and the photovoltaic power prediction factor.

[0010] According to some embodiments of the present application, the photovoltaic installation performance parameters include the installation area of ​​the photovoltaic panels of the photovoltaic storage system, the usage time of the photovoltaic storage system, the inverter efficiency of the photovoltaic storage system, the component efficiency of the photovoltaic panels of the photovoltaic storage system, and the maximum power temperature coefficient of the photovoltaic panels of the photovoltaic storage system.

[0011] According to some embodiments of the present application, generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor includes: The temperature forecast information of the first predetermined time period is acquired, photovoltaic power prediction information is generated according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor, and the photovoltaic power prediction information is adjusted according to the temperature forecast information.

[0012] According to some embodiments of the present application, adjusting the first prediction factor according to the photovoltaic power generation power in the second predetermined time period to obtain the photovoltaic power prediction factor includes: Acquire photovoltaic power prediction information for a second predetermined time period, and obtain prediction difference information for the second predetermined time period based on the photovoltaic power generation power and photovoltaic power prediction information for the second predetermined time period; The first prediction factor is adjusted according to the prediction difference information of the second predetermined time period to obtain a photovoltaic power prediction factor.

[0013] According to some embodiments of the present application, adjusting the first prediction factor according to the photovoltaic power generation power in the second predetermined time period to obtain the photovoltaic power prediction factor includes: Acquire weather type information for a second predetermined time period, and generate a second prediction factor corresponding to the second predetermined time period according to the weather type information for the second predetermined time period; generating photovoltaic power prediction information for the second predetermined time period according to the second prediction factor; The first prediction factor is adjusted according to the photovoltaic power prediction information and the photovoltaic power generation power in the second predetermined time period to obtain a photovoltaic power prediction factor.

[0014] According to some embodiments of the present application, generating a photovoltaic power prediction parameter for predicting photovoltaic power generation power according to the first predetermined time period and the location of the photovoltaic storage system includes: According to the first predetermined time period and the position of the photovoltaic storage system, the zenith angle parameters of the position of the photovoltaic storage system are obtained, and according to the zenith angle parameters of the position of the photovoltaic storage system, irradiance parameters for predicting photovoltaic power generation are generated, and the photovoltaic power prediction parameters include irradiance parameters.

[0015] According to some embodiments of the present application, the load includes a heat pump load and a consumption load; The step of controlling the operation of a load connected to the photovoltaic storage system based on the photovoltaic power prediction information includes: When the heat pump load is not in a user-started operation state, determining the available power of the heat pump load based on the photovoltaic power prediction information and the power demand of the consumption load; When the available power of the heat pump load meets the power demand of the heat pump load, the heat pump load is controlled to operate.

[0016] According to some embodiments of the present application, generating a first prediction factor for adjusting the photovoltaic power prediction parameter according to the weather type forecast information includes: According to the weather type forecast information, a first prediction factor for adjusting the photovoltaic power prediction parameter is generated through a fuzzy control algorithm, and different weather type forecast information corresponds to different first prediction factors.

[0017] In a second aspect, an embodiment of the present application provides an operation control device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the control method described in the first aspect above.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, comprising the operation control device of the second aspect mentioned above.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to enable a computer to execute the control method of the first aspect as described above.

[0020] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0022] The present application is further described below with reference to the accompanying drawings and embodiments; Figure 1 is a flow chart of a control method of a photovoltaic storage system provided by an embodiment of the present application; Figure 2 is a system block diagram of a photovoltaic storage system provided by another embodiment of the present application; Figure 3 This is a comparison chart of photovoltaic power generation on sunny, cloudy and rainy days in a certain year and month; Figure 4 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Figure 5 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Figure 6 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Figure 7 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Figure 8 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Fig. 9 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Fig.10 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Fig.11 is a flow chart of a control method of a photovoltaic storage system provided by another embodiment of the present application; Fig.12 It is a schematic diagram of an operation control device for executing a control method of a photovoltaic storage system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] This section will describe in detail the specific embodiments of the present application. The preferred embodiments of the present application are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present application, but it cannot be understood as a limitation on the scope of protection of the present application.

[0024] In the description of the present application, it should be understood that descriptions involving orientation, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0025] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0026] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0027] The following further describes various embodiments of the control method of the photovoltaic storage system of the present application in conjunction with the accompanying drawings.

[0028] like Figure 1 As shown, Figure 1 It is a flowchart of a control method of a photovoltaic storage system provided by an embodiment of the present application. The control method may include but is not limited to step S110, step S120, step S130 and step S140.

[0029] Step S110, obtaining weather type forecast information for a first predetermined time period to be predicted, generating photovoltaic power prediction parameters for predicting photovoltaic power generation according to the first predetermined time period and the location of the photovoltaic storage system, and generating a first prediction factor for adjusting the photovoltaic power prediction parameters according to the weather type forecast information; Step S120, obtaining photovoltaic power generation in a second predetermined time period, adjusting the first prediction factor according to the photovoltaic power generation in the second predetermined time period, and obtaining a photovoltaic power prediction factor, wherein the second predetermined time period is before the first predetermined time period; Step S130, generating photovoltaic power prediction information according to the photovoltaic power prediction parameters and the photovoltaic power prediction factors; Step S140: Based on the photovoltaic power prediction information, control the operation of the load connected to the photovoltaic storage system.

[0030] It can be understood that the photovoltaic storage system refers to a system that combines photovoltaic power generation and energy storage technology, mainly including photovoltaic modules, energy storage devices, inverters, controllers, etc. Among them, photovoltaic modules are used to convert solar energy into electrical energy, energy storage devices are used to store excess electrical energy, inverters are used to convert direct current into alternating current for use by loads or to be connected to the power grid, and controllers are used to manage the charging and discharging of the system; through the controller, after the photovoltaic modules generate electricity, the electricity can be used for loads first, and the excess is stored in the energy storage device. When photovoltaic power generation is insufficient, the energy storage device releases electricity to ensure stable power supply. In this way, the photovoltaic storage system can be used in a variety of different electricity usage scenarios.

[0031] In one embodiment, if Figure 2 As shown, Figure 2 It is a system block diagram of a photovoltaic storage system provided by another embodiment of the present application. The photovoltaic storage system is applied to household electricity consumption scenarios. The photovoltaic storage system includes photovoltaic panels and household storage integrated machines. The household storage integrated machine includes components such as energy storage batteries, energy management systems, and inverters. The photovoltaic components convert solar energy into electrical energy and input it into the household storage integrated machine. The household storage integrated machine controls the electrical energy for use by loads, energy storage, or integration into the power grid. The household electricity consumption scenarios applied by the photovoltaic storage system may include a variety of loads, such as heat pump loads and household electrical appliance loads. Users can control the power supply direction and power supply strategy of the household storage integrated machine through a mobile terminal APP that communicates with the household storage integrated machine. For example, the user sends instructions to the household storage integrated machine through the mobile terminal APP to control the household storage integrated machine to supply power to the heat pump load, to the household electrical appliance load, and to the time and amount of power to be integrated into the power grid. In addition, the household storage integrated machine can also intelligently control the time and amount of power supplied to the heat pump load, the household appliance load, and the connection to the grid. For example, the power supply to the heat pump load, the household appliance load, and the connection to the grid can be controlled by predicting the photovoltaic power generation. Accurate prediction of photovoltaic power generation is conducive to the optimal scheduling of household loads. However, the actual photovoltaic power generation is affected by many factors such as weather conditions, and there are unstable factors such as intermittent. If the photovoltaic power generation forecast is inaccurate, it may cause most of the household load electricity to purchase electricity from the municipal power grid, thereby failing to optimize the scheduling. In the scenario where there is no photovoltaic power generation forecast, the maximum photovoltaic power generation capacity is unknown. In order to maximize the use of photovoltaic power generation, one approach is to turn on the anti-reverse flow enable to allow the photovoltaic system to generate electricity at its maximum power generation capacity, and the power that is not absorbed by the household load is fed back to the municipal power grid. However, the premise for the implementation of this solution is that local policies allow users to feed back power to the municipal power grid.

[0032] In this embodiment, the user's mobile APP can communicate with the household storage integrated machine through the cloud server platform, and the cloud server platform can collect the operating parameters of the household storage integrated machine and the user's electricity consumption data.

[0033] Based on this, in this embodiment, for the prediction of photovoltaic power generation, when the photovoltaic storage system is in normal operation, the weather type forecast information for the first predetermined time period to be predicted can be obtained first. The first predetermined time period refers to a future time period for which photovoltaic power generation prediction is required. Through the prediction of photovoltaic power generation in the first predetermined time period, combined with the electricity demand in the first predetermined time period, the power supply can be intelligently controlled. The first predetermined time period can be a predetermined or user-defined time length, for example, one hour, two hours, etc., which is not limited here.

[0034] It can be understood that when the photovoltaic storage system is in normal operation, the implementation of photovoltaic power generation prediction can be carried out periodically, or it can be triggered by special time, special event or user control. In the case of periodic implementation of photovoltaic power generation prediction, the first predetermined time period can be used as the period for implementing photovoltaic power generation prediction. For example, the first predetermined time period is one hour, and the photovoltaic power generation prediction is to predict the photovoltaic power generation of the next hour, and the photovoltaic power generation prediction is performed once every hour to achieve continuous prediction of photovoltaic power generation; in the case of non-periodic implementation of photovoltaic power generation prediction, for example, the first predetermined time period is one hour, then the photovoltaic power generation prediction is to predict the photovoltaic power generation of the next hour, and each implementation of photovoltaic power generation prediction is to predict the photovoltaic power generation of the next hour, and the time point of implementing the prediction is non-periodic.

[0035] In one embodiment, the first predetermined time period is one hour, and weather type forecast information can be obtained with 24 hours as a node, that is, the weather type forecast information obtained is the weather type forecast information for each hour of the 24 hours of the next day; and, when generating a first prediction factor for adjusting the photovoltaic power prediction parameter, 24 hours can also be used as a node, that is, after fully using the weather type forecast information for each hour of 24 hours to generate the first prediction factor, the next round of first prediction factor generation is performed; and, when generating photovoltaic power prediction information, 24 hours can also be used as a node, that is, after fully performing the photovoltaic power prediction for each hour of 24 hours, the next round of photovoltaic power prediction is performed, or the parameters of the model or algorithm used for photovoltaic power prediction can be reset before the next round of photovoltaic power prediction is performed to ensure data accuracy.

[0036] It is understandable that weather type forecast information includes the forecasted weather type. Relevant data show that there are 48 known weather types, including common sunny days, cloudy days and heavy rain, as well as extreme sandstorms and blizzards. Under different weather types, the photovoltaic power generation power is different. For example, under common sunny days, cloudy days, rainy days and other weather types, the photovoltaic power generation power is different. The impact of different weather types on the accuracy of photovoltaic power generation cannot be ignored. In addition, weather type forecast information can also include cloud cover, air humidity, temperature, precipitation, sky clarity and other information. This information is used to describe different weather conditions and has an impact on the accuracy of photovoltaic power generation.

[0037] It should be noted that the time period of the weather type forecast information obtained needs to correspond to the first predetermined time period to be predicted. For example, when the first predetermined time period is one hour, it is necessary to predict the photovoltaic power generation for each hour. Therefore, the weather type forecast information obtained is the daily hourly weather type forecast information.

[0038] In this embodiment, weather type forecast information can be obtained by accessing a public weather forecast data interface or a weather forecast data interface of a meteorological data service provider through a cloud server platform that is communicatively connected to the household storage integrated machine.

[0039] Therefore, weather type forecast information for a first predetermined time period to be predicted is obtained, photovoltaic power prediction parameters for predicting photovoltaic power generation are generated according to the first predetermined time period and the location of the photovoltaic storage system, and a first prediction factor for adjusting the photovoltaic power prediction parameters is generated according to the weather type forecast information; It can be understood that the photovoltaic power prediction parameters are parameters used to predict photovoltaic power generation. The parameters used to predict photovoltaic power generation and the prediction methods may include multiple types. For example, the parameters used to predict photovoltaic power generation may include solar irradiance, temperature, humidity, wind speed, cloud cover, precipitation probability, atmospheric pressure, geographical location, photovoltaic characteristics, etc. The prediction methods may include prediction based on physical models, prediction based on statistical models, prediction based on machine learning models, prediction based on data assimilation methods, etc.; in this embodiment, the solar irradiance at the location of the photovoltaic storage system within the first predetermined time period can be judged by the first predetermined time period and the location of the photovoltaic storage system, so as to obtain the photovoltaic power prediction parameters for predicting photovoltaic power generation. In addition, the atmospheric pressure at the location of the photovoltaic storage system within the first predetermined time period and the location of the photovoltaic storage system can be judged by the first predetermined time period and the location of the photovoltaic storage system, and other photovoltaic power prediction parameters for predicting photovoltaic power generation can be judged.

[0040] It can be understood that the photovoltaic power prediction parameter is obtained according to the first predetermined time period and the location of the photovoltaic storage system, and the weather factor is not considered at this time. Therefore, the first prediction factor for adjusting the photovoltaic power prediction parameter can be generated according to the weather type forecast information, that is, the parameter actually used for photovoltaic power generation prediction is the photovoltaic power prediction parameter, and the first prediction factor is used to optimize and adjust the photovoltaic power prediction parameter to improve the prediction accuracy; refer to Figure 3 , Figure 3 This is a comparison chart of photovoltaic power generation on sunny, cloudy and rainy days in a certain year and month. Figure 3 It can be seen that in sunny weather, the maximum power generation at noon exceeds 8kW, and the fluctuation is small; in cloudy weather, the maximum power generation at noon can reach about 5.5kW; in rainy weather, the maximum power generation at noon can reach about 3kW, and the power generation on rainy days fluctuates greatly. Therefore, according to the historical data of current photovoltaic use, the change of photovoltaic power generation under different weather conditions can be judged, so as to determine the first prediction factor, which can be a coefficient value or an adjustment ratio. The first prediction factor is used to correct the photovoltaic power prediction parameters to improve the accuracy of photovoltaic power generation prediction.

[0041] In this embodiment, the method of generating the first prediction factor may include a rule-based method, a statistical method, or a machine learning method. For example, based on historical data, the changing pattern of photovoltaic power generation under different weather types can be analyzed to set the corresponding adjustment coefficient or adjustment ratio as the first prediction factor. A machine learning model can also be trained to input weather type forecast information to output the corresponding first prediction factor.

[0042] Furthermore, considering that photovoltaic power generation is not only affected by weather type, but may also be affected by many other factors, such as season, geographical location, degree of pollution of photovoltaic components, etc., these factors can also be considered when generating the first prediction factor to improve the comprehensiveness and accuracy of the prediction; at the same time, the first prediction factor can also be a multivariate function, which inputs multiple influencing factors including weather type and outputs adjusted photovoltaic power prediction parameters.

[0043] In addition, the accuracy of the first prediction factor can also be improved through historical data. In this embodiment, the photovoltaic power generation power of the second predetermined time period before the first predetermined time period is obtained, that is, the historical photovoltaic power generation power. For example, the photovoltaic power generation of the next hour is predicted at the current moment, and the next hour is the first predetermined time period. The second predetermined time period can be the current moment, and the photovoltaic power generation power of the second predetermined time period is the actual photovoltaic power generation power at the current moment, or the actual photovoltaic power generation power in the history before the current moment. The length of the second predetermined time period can be the same as the length of the first predetermined time period, or it can be different from the length of the first predetermined time period. Then, the first prediction factor is adjusted according to the photovoltaic power generation power of the second predetermined time period to obtain the photovoltaic power prediction factor. For example, the prediction error can be analyzed based on the comparison between the actual photovoltaic power generation power of the second predetermined time period and the predicted photovoltaic power generation power, so as to adjust the first prediction factor and obtain a more accurate photovoltaic power prediction factor.

[0044] After obtaining the photovoltaic power prediction parameters and the photovoltaic power prediction factors, the photovoltaic power prediction parameters and the photovoltaic power prediction factors can be combined to predict the photovoltaic power generation in the first predetermined time period, thereby obtaining the photovoltaic power prediction information for the first predetermined time period. In one embodiment, the photovoltaic power prediction information may include the predicted size of the photovoltaic power generation power, the change trend, etc., which is used for subsequent control decisions.

[0045] In addition, the impact of factors such as aging and pollution of photovoltaic modules on photovoltaic power generation can also be considered. The accuracy of the prediction can be improved by regularly testing and maintaining photovoltaic modules and adjusting the prediction model according to the test results. At the same time, factors such as the stability of the power grid and changes in user demand can also be considered. By formulating reasonable control strategies, the stable operation of the photovoltaic storage system and the user's power experience can be ensured.

[0046] Based on the photovoltaic power prediction information, the operation of the load connected to the photovoltaic storage system can be controlled. For example, according to the predicted photovoltaic power generation power and the power demand of the load, the charging and discharging of the energy storage device can be intelligently scheduled, and the working state of the inverter can be controlled to ensure the stability and economy of the power supply; when it is predicted that the photovoltaic power generation power is high, photovoltaic power generation is used for power supply first, and the excess electric energy is stored in the energy storage device; when it is predicted that the photovoltaic power generation power is insufficient, electric energy can be released from the energy storage device, or electricity can be purchased from the municipal power grid to meet the power demand of the load.

[0047] like Figure 4 As shown, Figure 4 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S130, it may include but is not limited to step S230 and step S330.

[0048] Step S230: Generate a first predicted photovoltaic power generation based on photovoltaic power prediction parameters and photovoltaic power prediction factors. The first predicted photovoltaic power generation includes the predicted photovoltaic power generation within a first predetermined time period. Step S330: Convert the first predicted photovoltaic power generation into predicted photovoltaic power generations for multiple third predetermined time periods to obtain photovoltaic power prediction information. The third predetermined time period is within the first predetermined time period and is less than the first predetermined time period.

[0049] It can be understood that the first predicted photovoltaic power generation generated based on the photovoltaic power prediction parameters and photovoltaic power prediction factors is the photovoltaic power generation of the photovoltaic within the first predetermined time period. The first predetermined time period can be a predetermined or user-defined different duration, or a duration that is continuously adjusted according to the actual weather. Therefore, the duration of the first predetermined time period may be relatively long, which is to correspond to the time unit of the obtained weather type forecast information or to meet the user's needs and adapt to the weather conditions to ensure the accuracy of the prediction. However, in the case of a large number of user load types, if the duration of the first predetermined time period is long and the predicted photovoltaic power generation is less than the actual photovoltaic power generation, when performing operation control based on the predicted photovoltaic power generation within the first predetermined time period, it may occur that the control operation of some loads is too short to meet the user's needs and the photovoltaic power generation cannot be maximally utilized; if the duration of the first predetermined time period is long and the predicted photovoltaic power generation is more than the actual photovoltaic power generation, when performing operation control based on the predicted photovoltaic power generation within the first predetermined time period, it may occur that the control operation of some loads is too long to meet the user's energy-saving needs, wasting energy and requiring mains power supply, resulting in a reduction in economic benefits. Therefore, the first predicted photovoltaic power generation can be converted into predicted photovoltaic power generations for multiple third predetermined time periods to obtain photovoltaic power prediction information. The third predetermined time period is within the first predetermined time period and is less than the first predetermined time period. In this way, the photovoltaic power prediction information includes the predicted photovoltaic power generations for multiple third predetermined time periods within the first predetermined time period, so that more accurate scheduling can be performed based on the predicted photovoltaic power generations for multiple third predetermined time periods within the first predetermined time period, optimizing the energy scheduling strategy, improving the user experience, and increasing the utilization rate of photovoltaic power generation.

[0050] It can be understood that the third predetermined time period is less than the first predetermined time period. For example, if the first predetermined time period is one hour, the third predetermined time period can be 15 minutes, and there are 4 third predetermined time periods within the first predetermined time period.

[0051] In this embodiment, there are multiple ways to convert the first predicted photovoltaic power generation power into predicted photovoltaic power generation power for multiple third predetermined time periods. For example, the prediction result of the first predetermined time period may be evenly distributed to each third predetermined time period; the predicted power may be weighted averaged according to the proportion of each third predetermined time period in the first predetermined time period; the power of each third predetermined time period may be further predicted based on the power change trend within the first predetermined time period; the predicted power may be corrected using real-time observation data, and the predicted power of each third predetermined time period may be updated based on the corrected data, etc.

[0052] In a control method provided in another embodiment of the present application, the first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation powers of multiple third predetermined time periods by an interpolation algorithm.

[0053] In this embodiment, the first predicted photovoltaic power generation power can be converted into predicted photovoltaic power generation power of multiple third predetermined time periods through an interpolation algorithm, that is, the predicted photovoltaic power generation power of the first predetermined time period is linearly distributed to multiple third predetermined time periods. For example, the power generation power at the beginning and end of the first predetermined time period can be determined first, and the power change rate in each third predetermined time period is calculated based on the time interval and power difference between the two predicted points, and the photovoltaic power generation power in each third predetermined time period is linearly estimated using this change rate.

[0054] In one embodiment, the first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation power of multiple third predetermined time periods through an interpolation algorithm. When operation control is performed based on the predicted photovoltaic power generation power of multiple third predetermined time periods, the self-generation and self-use rate of photovoltaic power generation can reach more than 95%, which greatly improves the utilization rate of photovoltaic power generation.

[0055] like Figure 5 As shown, Figure 5 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S130, it may include but is not limited to step S430.

[0056] Step S430: Acquire photovoltaic installed capacity performance parameters, and generate photovoltaic power prediction information according to the photovoltaic installed capacity performance parameters, photovoltaic power prediction parameters and photovoltaic power prediction factors.

[0057] It is understandable that in addition to natural factors such as solar irradiance, the hardware parameters of the photovoltaic equipment itself are also required to improve the prediction accuracy when predicting photovoltaic power generation. Therefore, the photovoltaic installed performance parameters are obtained, and after obtaining the adjusted more accurate photovoltaic power prediction parameters based on the photovoltaic power prediction parameters and the photovoltaic power prediction factors, the photovoltaic power generation is predicted by combining the adjusted more accurate photovoltaic power prediction parameters and the photovoltaic installed performance parameters to generate photovoltaic power prediction information.

[0058] In this embodiment, the photovoltaic installation performance parameters may include peak power, efficiency, installed capacity, photovoltaic module type, photovoltaic module quantity, photovoltaic module layout, inverter efficiency, system loss, historical performance data, etc. Based on the photovoltaic installation performance parameters and the adjusted more accurate photovoltaic power prediction parameters, a more accurate photovoltaic power generation prediction can be achieved through the prediction model.

[0059] In the control method provided in another embodiment of the present application, the photovoltaic installation performance parameters include the installation area of ​​the photovoltaic panels of the photovoltaic storage system, the usage time of the photovoltaic storage system, the inverter efficiency of the photovoltaic storage system, the component efficiency of the photovoltaic panels of the photovoltaic storage system, and the maximum power temperature coefficient of the photovoltaic panels of the photovoltaic storage system.

[0060] It is understandable that the installation area of ​​PV panels determines how much solar energy the PV system can capture. The larger the area, the more electricity it can theoretically generate under the same conditions. Therefore, the maximum possible power generation of the system can be estimated through the installation area of ​​PV panels. The installation area of ​​PV panels can be calculated by the length and width of each PV panel, as well as the number of PV panels. The usage time of the photovoltaic storage system refers to the time the photovoltaic storage system has been in use. The inverter efficiency refers to the efficiency of the inverter in converting direct current into alternating current. The lower the inverter efficiency, the greater the energy loss. The actual output power of the system can be calculated more accurately through the inverter efficiency. The module efficiency is the ability of PV panels to convert solar energy into electrical energy under standard strategy conditions. The higher the module efficiency, the more power can be generated under the same area and lighting conditions. The maximum power temperature coefficient characterizes the degree of influence of temperature changes on the maximum power. The maximum power temperature coefficient can be used to predict the power output under different temperature conditions.

[0061] In this embodiment, the relationship between photovoltaic installation performance parameters and photovoltaic system performance can be described by using a physical model or a statistical model. For example, the power output of the photovoltaic system can be predicted using the following formula: actual power output = peak power of photovoltaic panel * (1 + maximum power temperature coefficient * (operating temperature of photovoltaic panel - temperature under standard test conditions)) * inverter efficiency * actual solar irradiance. The accuracy of the model is then verified using historical data, so that the power generation power of the photovoltaic system can be predicted more accurately.

[0062] like Figure 6 As shown, Figure 6 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S130, it may include but is not limited to step S530.

[0063] Step S530: Acquire temperature forecast information for a first predetermined time period, generate photovoltaic power prediction information according to photovoltaic power prediction parameters and photovoltaic power prediction factors, and adjust the photovoltaic power prediction information according to the temperature forecast information.

[0064] It is understandable that the operating efficiency of photovoltaic panels is affected not only by solar irradiance but also by ambient temperature. Therefore, the parameters used to predict photovoltaic power generation may also include temperature parameters.

[0065] In this embodiment, the temperature forecast information of the first predetermined time period is obtained, and after the photovoltaic power prediction information is generated according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor, the temperature forecast information of the first predetermined time period is used to compensate and correct the generated photovoltaic power prediction information. For example, the relationship between the temperature change and the photovoltaic power output is analyzed according to the maximum power temperature coefficient of the photovoltaic panel, and then the photovoltaic power prediction is corrected according to the relationship between the temperature change and the photovoltaic power output. In one embodiment, for each prediction time point, correction can be made using the following formula: Corrected photovoltaic power prediction = preliminary photovoltaic power prediction * (1 + maximum power temperature coefficient * (operating temperature of photovoltaic panel - temperature under standard test conditions)).

[0066] like Figure 7 As shown, Figure 7 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S120, it may include but is not limited to step S220 and step S320.

[0067] Step S220, obtaining photovoltaic power prediction information for a second predetermined time period, and obtaining prediction difference information for the second predetermined time period according to the photovoltaic power generation power and photovoltaic power prediction information for the second predetermined time period; Step S320: adjust the first prediction factor according to the prediction difference information of the second predetermined time period to obtain a photovoltaic power prediction factor.

[0068] It can be understood that photovoltaic power prediction parameters for predicting photovoltaic power generation are generated based on the first predetermined time period and the location of the photovoltaic storage system, and the first prediction factor for adjusting the photovoltaic power prediction parameters is generated based on the weather type forecast information. The photovoltaic power generation is predicted by the photovoltaic power prediction parameters, the first prediction factor and parameters such as the system installed capacity. This is the feedforward prediction method; the first prediction factor can be fed back to correct the photovoltaic power generation power in the second predetermined time period before the first predetermined time period; the prediction accuracy of photovoltaic power generation is improved by combining feedforward and feedback.

[0069] In this embodiment, photovoltaic power prediction information of a second predetermined time period can be obtained, and the second predetermined time period is before the first predetermined time period. For example, the first predetermined time period is the next hour of the current moment, and the photovoltaic power prediction information of the second predetermined time period can be the photovoltaic power prediction information of the current hour, or the photovoltaic power prediction information of the previous hour of the current moment, etc.; according to the actual photovoltaic power generation power and photovoltaic power prediction information of the second predetermined time period, the deviation between the actual photovoltaic power generation power and the predicted photovoltaic power generation power in the second predetermined time period can be determined, thereby obtaining the prediction difference information of the second predetermined time period; Then, according to the prediction difference information of the second predetermined time period, the first prediction factor is adjusted through the self-learning feedback network to obtain a corrected photovoltaic power prediction factor; for the self-learning feedback network, for example, the prediction difference information can be used to define a loss function, the gradient of the loss function relative to the parameter is calculated, and then the back propagation algorithm is used to update the weights, so as to adjust the model parameters of the model used to generate the first prediction factor according to gradient descent or other optimization algorithms.

[0070] like Figure 8 As shown, Figure 8 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S120, it may include but is not limited to step S420, step S520 and step S620.

[0071] Step S420: obtaining weather type information for a second predetermined time period, and generating a second prediction factor corresponding to the second predetermined time period according to the weather type information for the second predetermined time period; Step S520: generating photovoltaic power prediction information for a second predetermined time period according to a second prediction factor; Step S620: adjust the first prediction factor according to the photovoltaic power prediction information and the photovoltaic power generation power in the second predetermined time period to obtain a photovoltaic power prediction factor.

[0072] In this embodiment, the prediction accuracy of photovoltaic power generation can be improved by combining feedforward and feedback. When the first prediction factor is corrected by feedback, in addition to using the actual power generation power of the second predetermined time period, the actual weather type information of the second predetermined time period can also be used as a feedback factor to correct the first prediction factor.

[0073] Specifically, the actual weather type information of the second predetermined time period may be obtained, and the actual weather type information of the second predetermined time period may be different from the predicted weather type information of the second predetermined time period. Therefore, the second prediction factor generated according to the actual weather type information of the second predetermined time period may be different from the second prediction factor generated according to the predicted weather type information of the second predetermined time period. Furthermore, photovoltaic power prediction information for the second predetermined time period is generated based on a second prediction factor generated based on actual weather type information for the second predetermined time period. In addition, previous photovoltaic power prediction information for the second predetermined time period before the second predetermined time period can be obtained, that is, photovoltaic power prediction information generated by the second prediction factor generated based on predicted weather type information for the second predetermined time period. Thus, previous photovoltaic power prediction information is obtained through predicted weather type information for the second predetermined time period, photovoltaic power prediction information is obtained through actual weather type information for the second predetermined time period, and photovoltaic power generation power is actual power generation power for the second predetermined time period. Through three different parameters in the second predetermined time period and combined with a self-learning feedback network, the first prediction factor can be corrected with more parameter dimensions, thereby further improving the accuracy of the first prediction factor.

[0074] In one embodiment, the first predetermined time period is one hour, and weather type forecast information can be obtained with 24 hours as a node, that is, the weather type forecast information obtained is the weather type forecast information for each hour of the 24 hours of the next day; and when generating photovoltaic power prediction information, 24 hours can also be used as a node, that is, after the photovoltaic power prediction for each hour of 24 hours is completely performed, the next round of photovoltaic power prediction can be performed, or the parameters of the model or algorithm used for photovoltaic power prediction can be reset before the next round of photovoltaic power prediction is performed to ensure data accuracy. For example, the weather type forecast information for the first predetermined time period to be predicted, the weight of the photovoltaic power prediction factor (such as initialized to 0.4), the parameter weight of the self-learning feedback network used to correct the photovoltaic power prediction factor, and the photovoltaic capacity parameters can be reset.

[0075] like Fig. 9 As shown, Fig. 9 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S110, it may include but is not limited to step S210.

[0076] Step S210: obtain the zenith angle parameters of the location of the photovoltaic storage system according to the first predetermined time period and the location of the photovoltaic storage system; generate irradiance parameters for predicting photovoltaic power generation according to the zenith angle parameters of the location of the photovoltaic storage system; the photovoltaic power prediction parameters include irradiance parameters.

[0077] In this embodiment, the photovoltaic power prediction parameter includes an irradiance parameter, that is, solar irradiance. Therefore, the solar irradiance in the first predetermined time period can be determined according to the first predetermined time period and the location of the photovoltaic storage system. Specifically, the location of the solar storage system may include longitude and latitude data of the solar storage system, and specific date data corresponding to the first predetermined time period may be obtained through the first predetermined time period; The specific date data corresponding to the first predetermined time period can be used to determine the DOY of the specific date corresponding to the first predetermined time period in the current year. For example, New Year's Day is taken as the starting point 1, that is, the DOY value of January 1 is 1, and February is calculated as 28 days; the solar declination angle is calculated by the specific date corresponding to the first predetermined time period on the current year. , solar declination , and the true solar time can be determined according to the first predetermined time period , determine the hour angle based on the true solar time data , , and then calculate the zenith angle parameters through the hour angle .

[0078] After obtaining the zenith angle parameter, it is determined whether the zenith angle parameter is greater than 90 degrees. If the zenith angle parameter is greater than 90 degrees, it can be considered that the specific time period corresponding to the first predetermined time period is night, and therefore, the irradiance parameter can be set to zero; When the zenith angle parameter is not greater than 90 degrees, in order to take into account the revolution of the earth, the deviation correction factor can be calculated using the specific date data corresponding to the first predetermined time period. , to correct for the deviation caused by the change in the distance between the sun and the earth, ; Get the solar constant ISC, and use the solar constant to calculate the value of solar radiation at the top of the atmosphere , ; Finally, the clear sky irradiance corresponding to the first predetermined time period is calculated by the value of solar radiation at the top of the atmosphere and the zenith angle. , .

[0079] It should be noted that the above calculation method is an ideal hourly clear sky irradiance prediction, which is applicable to any region. Because it only takes into account the change in the distance between the earth and the sun and the geographical location information of the observation site, and does not include the change in weather information in the atmosphere, the accuracy is limited. In order to improve the accuracy of the model, the photovoltaic power prediction factor is obtained through the weather type and the photovoltaic power generation power in the second predetermined time period, so as to feedforward and correct the photovoltaic power generation prediction.

[0080] In a control method provided in another embodiment of the present application, the load includes a heat pump load and a consumption load; like Fig.10 As shown, Fig.10 It is a flow chart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S140, it may include but is not limited to step S240 and step S340.

[0081] Step S240: when the heat pump load is not in a user-started operation state, based on the photovoltaic power prediction information and the power demand of the consumption load, determining the available power of the heat pump load; Step S340: When the available power of the heat pump load meets the power demand of the heat pump load, control the heat pump load to operate.

[0082] In this embodiment, the load includes a heat pump load and a consumption load. The heat pump load is used to adjust the user's indoor ambient temperature, and the consumption load may include other household electrical appliance loads except the heat pump load, such as refrigerators, washing machines, televisions, electric lights, etc.

[0083] In this embodiment, after completing the photovoltaic power prediction, it can be determined whether the heat pump load is in a working state. If the heat pump load is not in a working state, it can be determined whether it is necessary to control the heat pump load to start according to the photovoltaic power prediction information; if the heat pump load is in a working state, it is also necessary to determine whether the heat pump load is in a user-started running state, that is, to determine whether the heat pump load is actively turned on by the user. If the heat pump load is determined to be actively turned on by the user, it is possible that the user's indoor ambient temperature is too low, and the user actively turns on the heat pump load to increase the indoor temperature. Photovoltaic power generation has certain instability. Therefore, if it is determined that the heat pump load is actively turned on by the user, the heat pump load may not use photovoltaic power supply; When the heat pump load is not in the user-started operation state, that is, it is judged that the heat pump load is not actively turned on by the user, the heat pump load can use photovoltaic power supply to improve the utilization rate of photovoltaic power supply, thereby realizing intelligent scheduling of household heat pumps and maintaining the user's ambient temperature in a temperature range that the human body feels comfortable; Specifically, the available power of the heat pump load can be judged based on the photovoltaic power prediction information and the power demand of the consumption load, that is, the remaining power after deducting the power demand of the consumption load from the predicted photovoltaic power generation power; then it can be judged whether the heat pump is turned on, and the heat pump can be turned on when the heat pump is turned off, and by comparing the available power of the heat pump load with the power demand of the heat pump load, the heat pump load can be controlled to operate when the available power of the heat pump load meets the power demand of the heat pump load; for example, when the weather forecast temperature in the first predetermined time period is greater than 25°C, the heat pump can be controlled to perform cooling and temperature adjustment, when the weather forecast temperature in the first predetermined time period is less than 15°C, the heat pump can be controlled to perform heating and temperature adjustment, and when the weather forecast temperature in the first predetermined time period is not greater than 25°C and not less than 15°C, the heat pump can be controlled to automatically adjust according to the ambient temperature.

[0084] like Fig.11 As shown, Fig.11 It is a flowchart of a control method provided by another embodiment of the present application; regarding the above-mentioned step S110, it may include but is not limited to step S310.

[0085] Step S310: generating a first prediction factor for adjusting photovoltaic power prediction parameters according to the weather type forecast information through a fuzzy control algorithm, wherein different weather type forecast information corresponds to different first prediction factors.

[0086] In this embodiment, the first prediction factor for adjusting the photovoltaic power prediction parameter is generated by weather type forecast information, and a fuzzy control algorithm may be used; for example, the weather type forecast information may be first defuzzified and classified according to the weather type. Defuzzification is a process in fuzzy logic that converts fuzzy sets into clear values ​​that can be used for decision-making or further processing. When processing weather forecast information, defuzzification classification may convert fuzzy weather forecast data (such as "there may be rain") into specific categories (such as "rainy day" or "non-rainy day"); Specifically, classified weather types can be defined, such as: sunny, cloudy, overcast, rainy, snowy, foggy, etc. For each weather type, its corresponding fuzzy set is determined. For example, for the type of "rain", the fuzzy set may include "light rain", "moderate rain" and "heavy rain"; then a membership function is formulated for each fuzzy set to describe the degree to which the weather type forecast information belongs to a certain fuzzy set. The membership function can be triangular, trapezoidal, Gaussian, etc. Subsequently, fuzzy logic can be used to infer the weather forecast data, calculate the membership of each data point to each fuzzy set, and then select a suitable defuzzification method to convert the fuzzy membership into a specific weather type classification. For example, the maximum membership method, that is, select the category with the highest membership as the classification result. For example, if the membership of "light rain" is the highest, it is classified as "rain"; the weighted average method, that is, the weighted average of the membership of each fuzzy set, the weight can be the membership or other weight factors; the median method, that is, calculate the center of gravity of the fuzzy set membership function, and use the corresponding category as the classification result.

[0087] Based on this, the weather type forecast information can be converted from a vague state to a specific weather type classification, thereby providing clearer data input for photovoltaic power generation prediction.

[0088] Based on the control methods of the above-mentioned embodiments, various embodiments of the operation control device, electronic device and computer-readable storage medium of the present application are respectively proposed below.

[0089] like Fig.12 As shown, Fig.12 1 is a schematic diagram of an operation control device for executing a control method of a photovoltaic storage system provided by an embodiment of the present application. The operation control device 1200 implemented in the present application includes: a processor 1220, a memory 1210, and a computer program stored in the memory 1210 and executable on the processor 1220, wherein: Fig.12 In the figure, a processor 1220 and a memory 1210 are taken as an example.

[0090] The processor 1220 and the memory 1210 may be connected via a bus or other means. Fig.12 The example of connecting through bus is taken in the following.

[0091] The memory 1210, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory 1210 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1210 may optionally include a memory 1210 remotely arranged relative to the processor 1220, and these remote memories 1210 may be connected to the operation control device 1200 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] Those skilled in the art will understand that Fig.12 The device structure shown in the figure does not constitute a limitation on the operation control device 1200, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0093] exist Fig.12 In the operation control device 1200 shown, the processor 1220 can be used to call the control program stored in the memory 1210 to implement the above control method. Specifically, the non-transient software program and instructions required to implement the control method of the above embodiment are stored in the memory 1210, and when executed by the processor 1220, the control method of the above embodiment is executed.

[0094] It is worth noting that since the operation control device 1200 of the embodiment of the present application can execute the control method of any of the above-mentioned embodiments, the specific implementation methods and technical effects of the operation control device 1200 of the embodiment of the present application can refer to the specific implementation methods and technical effects of the control method of any of the above-mentioned embodiments.

[0095] In addition, an embodiment of the present application further provides an electronic device, which includes the operation control device of the above embodiment.

[0096] It is worth noting that since the electronic device of the embodiment of the present application includes the operation control device of the above-mentioned embodiment, and the operation control device of the above-mentioned embodiment can execute the control method of any of the above-mentioned embodiments, the specific implementation methods and technical effects of the electronic device of the embodiment of the present application can refer to the specific implementation methods and technical effects of the control method of any of the above-mentioned embodiments.

[0097] In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to execute the above-described control method. Figure 1 , Figures 4 to 11 The method steps in .

[0098] It is worth noting that since the computer-readable storage medium of the embodiments of the present application can execute the control method of any of the above embodiments, the specific implementation methods and technical effects of the computer-readable storage medium of the embodiments of the present application can refer to the specific implementation methods and technical effects of the control method of any of the above embodiments.

[0099] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed methods above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium or a non-transitory medium and a communication medium or a temporary medium. As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk DVD or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0100] In several embodiments provided in the present application, it should be understood that the disclosed systems, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of apparatuses or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0101] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0102] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the technical field without departing from the purpose of the present application.

Claims

1. A control method for a photovoltaic storage system, characterized in that: include: Acquire weather type forecast information for a first predetermined time period to be predicted, generate a photovoltaic power prediction parameter for predicting photovoltaic power generation according to the first predetermined time period and the location of the photovoltaic storage system, and generate a first prediction factor for adjusting the photovoltaic power prediction parameter according to the weather type forecast information; Acquire photovoltaic power generation in a second predetermined time period, adjust the first prediction factor according to the photovoltaic power generation in the second predetermined time period, and obtain a photovoltaic power prediction factor, wherein the second predetermined time period is before the first predetermined time period; Generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor; Based on the photovoltaic power prediction information, the operation of a load connected to the photovoltaic storage system is controlled.

2. The control method according to claim 1, characterized in that: The generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor comprises: Generate a first predicted photovoltaic power generation power according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor, wherein the first predicted photovoltaic power generation power includes the predicted photovoltaic power generation power within the first predetermined time period; The first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation power of multiple third predetermined time periods to obtain photovoltaic power prediction information, wherein the third predetermined time period is within the first predetermined time period and the third predetermined time period is less than the first predetermined time period.

3. The control method according to claim 2, characterized in that: The first predicted photovoltaic power generation power is converted into predicted photovoltaic power generation power of multiple third predetermined time periods through an interpolation algorithm.

4. The control method according to claim 1, characterized in that: The generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor comprises: Photovoltaic installed capacity performance parameters are obtained, and photovoltaic power prediction information is generated according to the photovoltaic installed capacity performance parameters, the photovoltaic power prediction parameters and the photovoltaic power prediction factor.

5. The control method according to claim 4, characterized in that: The photovoltaic installation performance parameters include the installation area of ​​the photovoltaic panels of the photovoltaic storage system, the usage time of the photovoltaic storage system, the inverter efficiency of the photovoltaic storage system, the component efficiency of the photovoltaic panels of the photovoltaic storage system, and the maximum power temperature coefficient of the photovoltaic panels of the photovoltaic storage system.

6. The control method according to claim 1, characterized in that: The generating photovoltaic power prediction information according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor comprises: The temperature forecast information of the first predetermined time period is acquired, photovoltaic power prediction information is generated according to the photovoltaic power prediction parameter and the photovoltaic power prediction factor, and the photovoltaic power prediction information is adjusted according to the temperature forecast information.

7. The control method according to claim 1, characterized in that: The step of adjusting the first prediction factor according to the photovoltaic power generation power in the second predetermined time period to obtain a photovoltaic power prediction factor includes: Acquire photovoltaic power prediction information for a second predetermined time period, and obtain prediction difference information for the second predetermined time period based on the photovoltaic power generation power and photovoltaic power prediction information for the second predetermined time period; The first prediction factor is adjusted according to the prediction difference information of the second predetermined time period to obtain a photovoltaic power prediction factor.

8. The control method according to claim 1, characterized in that: The step of adjusting the first prediction factor according to the photovoltaic power generation power in the second predetermined time period to obtain a photovoltaic power prediction factor includes: Acquire weather type information for a second predetermined time period, and generate a second prediction factor corresponding to the second predetermined time period according to the weather type information for the second predetermined time period; generating photovoltaic power prediction information for the second predetermined time period according to the second prediction factor; The first prediction factor is adjusted according to the photovoltaic power prediction information and the photovoltaic power generation power in the second predetermined time period to obtain a photovoltaic power prediction factor.

9. The control method according to claim 1, characterized in that: The generating, according to the first predetermined time period and the location of the photovoltaic storage system, a photovoltaic power prediction parameter for predicting photovoltaic power generation power comprises: According to the first predetermined time period and the position of the photovoltaic storage system, the zenith angle parameters of the position of the photovoltaic storage system are obtained, and according to the zenith angle parameters of the position of the photovoltaic storage system, irradiance parameters for predicting photovoltaic power generation are generated, and the photovoltaic power prediction parameters include irradiance parameters.

10. The control method according to claim 1, characterized in that: The load includes a heat pump load and a consumption load; The step of controlling the operation of a load connected to the photovoltaic storage system based on the photovoltaic power prediction information includes: When the heat pump load is not in a user-started operation state, determining the available power of the heat pump load based on the photovoltaic power prediction information and the power demand of the consumption load; When the available power of the heat pump load meets the power demand of the heat pump load, the heat pump load is controlled to operate.

11. The control method according to claim 1, characterized in that: The step of generating a first prediction factor for adjusting the photovoltaic power prediction parameter according to the weather type forecast information comprises: According to the weather type forecast information, a first prediction factor for adjusting the photovoltaic power prediction parameter is generated through a fuzzy control algorithm, and different weather type forecast information corresponds to different first prediction factors.

12. An operation control device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method according to any one of claims 1 to 11.

13. An electronic device, characterized in that: Includes the operation control device as claimed in claim 12.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the control method according to any one of claims 1 to 11.

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