System processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a system processing method and device, a storage medium and electronic equipment, and relates to the technical field of optical storage. The method comprises the following steps: acquiring historical generating capacity data of a photovoltaic power generation system, historical state information of an energy storage system and historical consumption data of load equipment; inputting the historical generating capacity data, the historical state information and the historical consumption data into a target prediction model, and predicting to obtain generating capacity data of the photovoltaic power generation system in a first preset time period and load demand data of the load equipment in the first preset time period; generating an initial operation plan according to the generating capacity data and the load demand data; and determining a target operation plan based on the initial operation plan, and adjusting the operation states of the photovoltaic power generation system and the energy storage system according to the target operation plan. According to the invention, the problem that the operation efficiency of the photovoltaic power generation system and the energy storage system is low because the operation state of the photovoltaic and energy storage system is adjusted based on a fixed operation strategy in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of optical storage technology, and in particular to a system processing method and device, a storage medium and an electronic device. Background Art

[0002] The photovoltaic and energy storage integrated system is an energy supply solution that combines a photovoltaic system with an energy storage system. It can make full use of solar power generation and regulate power supply through the energy storage system to achieve efficient use of energy and stable supply. Moreover, the photovoltaic and energy storage integrated system can be divided into two types: centralized and distributed. The centralized photovoltaic and energy storage integrated system usually unifies the photovoltaic array, energy storage system and load in a large centralized site and manages them through a central controller. The distributed photovoltaic and energy storage integrated system disperses the photovoltaic array, energy storage system and load in multiple small sites, each of which can be operated and managed independently.

[0003] In addition, the dispatching and control method of the photovoltaic and energy storage integrated system is the key to achieving efficient operation of the system. And the dispatching and control methods in related technologies usually adjust the operation of photovoltaic power generation and energy storage equipment based on fixed operation strategies. For example, photovoltaic power generation is used first when there is sufficient sunlight, and energy storage systems are used to assist in power generation when there is insufficient sunlight. However, the fixed strategies in related technologies cannot adapt to frequent changes in electricity demand, resulting in low system operation efficiency.

[0004] Currently, no effective solution has been proposed to address the problem that the operating states of photovoltaic and energy storage systems are adjusted based on fixed operating strategies in related technologies, resulting in low operating efficiency of photovoltaic power generation systems and energy storage systems. Summary of the invention

[0005] The main purpose of this application is to provide a system processing method and device, storage medium and electronic device to solve the problem in the related art that the operating status of the photovoltaic and energy storage systems is adjusted based on a fixed operating strategy, resulting in low operating efficiency of the photovoltaic power generation system and the energy storage system.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a system processing method is provided. The method includes: obtaining historical power generation data of a photovoltaic power generation system, historical status information of an energy storage system, and historical consumption data of a load device, wherein the load device is a device powered by a target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; inputting the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system, and the historical consumption data of the load device into a target prediction model for processing, and predicting the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period; generating an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating state of the photovoltaic power generation system and the energy storage system; determining a target operation plan based on the initial operation plan, and adjusting the operating state of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0007] Furthermore, generating an initial operation plan based on the power generation data of the photovoltaic power generation system within a first preset time period and the load demand data of the load device within the first preset time period includes: acquiring current status information of the energy storage system; determining a power generation strategy of the photovoltaic power generation system and a charge-discharge strategy of the energy storage system based on the current status information of the energy storage system, the power generation data of the photovoltaic power generation system within the first preset time period and the load demand data of the load device within the first preset time period; generating the initial operation plan based on the power generation strategy of the photovoltaic power generation system and the charge-discharge strategy of the energy storage system.

[0008] Furthermore, based on the initial operation plan, determining the target operation plan includes: using a preset formula to calculate the power generation data of the photovoltaic power generation system in a second preset time period, wherein the second preset time period is later than the first preset time period; and determining the target operation plan according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan.

[0009] Further, according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan, determining the target operation plan includes: judging whether the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data; if the power generation data of the photovoltaic power generation system in the second preset time period is not less than the preset power generation data, prohibiting adjustment of the initial operation plan, and taking the initial operation plan as the target operation plan; if the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data, adjusting the initial operation plan to obtain an adjusted initial operation plan; taking the adjusted initial operation plan as the target operation plan.

[0010] Furthermore, the target prediction model is obtained in the following manner: obtaining a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load device; using the sample data set to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

[0011] Furthermore, using the sample data set to learn and train the original prediction model to obtain the target prediction model includes: using the sample data set to learn and train the original prediction model to obtain a trained prediction model; judging whether the trained prediction model meets preset requirements; if the trained prediction model meets the preset requirements, using the trained prediction model as the target prediction model; if the trained prediction model does not meet the preset requirements, adjusting the model structure of the trained prediction model to obtain the target prediction model.

[0012] Furthermore, using the sample data set to learn and train the original prediction model to obtain the trained prediction model includes: cleaning the sample data in the sample data set to obtain processed sample data, wherein the cleaning processing method includes at least one of the following: filling missing values, removing outliers and data standardization; performing feature extraction on the processed sample data to obtain a feature vector; using the feature vector to learn and train the original prediction model to obtain the trained prediction model.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a system processing device is provided. The device includes: a first acquisition unit, which is used to acquire the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; a first prediction unit, which is used to input the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into the target prediction model for processing, and predict the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period; a first generation unit, which is used to generate an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operation status of the photovoltaic power generation system and the energy storage system; a first processing unit, which is used to determine a target operation plan based on the initial operation plan, and adjust the operation status of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0014] Furthermore, the first generating unit includes: a first acquisition module, used to acquire current status information of the energy storage system; a first determination module, used to determine the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system based on the current status information of the energy storage system, the power generation data of the photovoltaic power generation system within a first preset time period, and the load demand data of the load device within the first preset time period; the first generating module, used to generate the initial operation plan based on the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system.

[0015] Furthermore, the first processing unit includes: a first calculation module, used to calculate the power generation data of the photovoltaic power generation system in a second preset time period using a preset formula, wherein the second preset time period is later than the first preset time period; a second determination module, used to determine the target operation plan based on the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan.

[0016] Furthermore, the second determination module includes: a first judgment submodule, used to judge whether the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data; a first determination submodule, used to prohibit the adjustment of the initial operation plan if the power generation data of the photovoltaic power generation system in the second preset time period is not less than the preset power generation data, and use the initial operation plan as the target operation plan; a first adjustment submodule, used to adjust the initial operation plan to obtain an adjusted initial operation plan if the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data; and a second determination submodule, used to use the adjusted initial operation plan as the target operation plan.

[0017] Furthermore, the target prediction model is obtained through the following units: a second acquisition unit, used to acquire a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system and historical consumption data of the load device; a first training unit, used to use the sample data set to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

[0018] Furthermore, the first training unit includes: a first training module, used to use the sample data set to learn and train the original prediction model to obtain a trained prediction model; a first judgment module, used to judge whether the trained prediction model meets the preset requirements; a third determination module, used to use the trained prediction model as the target prediction model if the trained prediction model meets the preset requirements; and a first adjustment module, used to adjust the model structure of the trained prediction model if the trained prediction model does not meet the preset requirements to obtain the target prediction model.

[0019] Furthermore, the first training module includes: a first processing submodule, used to clean the sample data in the sample data set to obtain processed sample data, wherein the cleaning processing method includes at least one of the following: filling missing values, removing outliers and data standardization processing; a second processing submodule, used to perform feature extraction processing on the processed sample data to obtain a feature vector; a first training submodule, used to use the feature vector to learn and train the original prediction model to obtain the trained prediction model.

[0020] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein the program executes any one of the above-mentioned system processing methods.

[0021] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is provided, which includes one or more processors and a memory, and the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the processing method of the system described in any one of the above-mentioned items.

[0022] Through the present application, the following steps are adopted: obtaining historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system and historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; inputting the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into the target prediction model for processing, and predicting the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period; generating an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating state of the photovoltaic power generation system and the energy storage system; determining a target operation plan based on the initial operation plan, and adjusting the operating state of the photovoltaic power generation system and the energy storage system according to the target operation plan, thereby solving the problem in the related art that the operating state of the photovoltaic and energy storage systems is adjusted based on a fixed operation strategy, resulting in low operating efficiency of the photovoltaic power generation system and the energy storage system. By comprehensively monitoring the real-time data of the photovoltaic system, energy storage equipment and load, and inputting the real-time data of the photovoltaic system, energy storage equipment and load into the prediction model for prediction, the photovoltaic power generation and load demand in the future period are output, and then a detailed operation plan (i.e., the initial operation plan) is formed based on the prediction results. The initial operation plan is then adjusted online to obtain the final operation plan (i.e., the target operation plan), and the operating status of the photovoltaic power generation system and the energy storage system is adjusted according to the final operation plan (i.e., the target operation plan), so that the operation of the photovoltaic power generation and energy storage equipment can be dynamically adjusted, thereby achieving the effect of improving the operating efficiency of the photovoltaic power generation system and the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 is a flowchart of a processing method of a system provided in an embodiment of the present application;

[0025] Figure 2 is a flow chart of a photovoltaic-storage integrated adaptive resource scheduling control method in this embodiment;

[0026] Figure 3 It is a flow chart of a photovoltaic energy storage integrated adaptive resource scheduling control method in this embodiment, which monitors photovoltaic power generation performance, energy storage status, load consumption and environmental variables in real time by installing sensors and intelligent metering equipment;

[0027] Figure 4 It is a flowchart of establishing a high-precision prediction model of photovoltaic power generation and load demand in a photovoltaic storage integrated adaptive resource scheduling control method in this embodiment to form an operation plan at a future moment;

[0028] Figure 5 It is a flow chart of online adjustment according to the current photovoltaic and energy storage operation plan in a photovoltaic and energy storage integrated adaptive resource scheduling control method in this embodiment;

[0029] Figure 6 This is a flow chart of obtaining the power generation efficiency and time period electricity price at the current moment and adjusting the proportion of photovoltaic power in a photovoltaic-storage integrated adaptive resource scheduling control method in this embodiment;

[0030] Figure 7 It is a flow chart of a photovoltaic array, an energy storage unit and a load interaction managed by a coordination controller in a photovoltaic storage integrated adaptive resource scheduling control method in this embodiment to achieve supply and demand balance;

[0031] Figure 8 It is a flow chart of continuously monitoring the efficiency of the current photovoltaic and energy storage execution process in a photovoltaic and energy storage integrated adaptive resource scheduling control method in this embodiment;

[0032] Fig. 9 is a schematic diagram of a photovoltaic-storage integrated adaptive resource scheduling control system in this embodiment;

[0033] Fig.10 is a schematic diagram of a processing device of a system provided in an embodiment of the present application;

[0034] Fig.11 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0039] The present invention is described below in conjunction with preferred implementation steps. Figure 1 is a flowchart of a processing method of a system provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0040] Step S101, obtaining historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system and historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system.

[0041] For example, by installing sensors and intelligent metering equipment, the photovoltaic power generation performance (corresponding to the historical power generation data of the photovoltaic power generation system mentioned above), the energy storage status (corresponding to the historical status information of the energy storage system mentioned above) and the load consumption (corresponding to the historical consumption data of the load device mentioned above) are monitored in real time. Moreover, the above-mentioned target system can be an integrated photovoltaic storage system, and the photovoltaic storage system can include a photovoltaic power generation system and an energy storage system, and the above-mentioned load device can be a device or application connected to the integrated photovoltaic storage system and obtaining electrical energy from the system to complete its function. Therefore, the load can be either a device that directly obtains electrical energy from the photovoltaic power generation system, or an electrical appliance that obtains electricity indirectly through the energy storage system. In addition, the above-mentioned load equipment can include electrical appliances for household, commercial and industrial purposes, such as lighting, air conditioning, heating systems, production equipment and electric vehicle charging stations.

[0042] Step S102, inputting historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system and historical consumption data of the load equipment into the target prediction model for processing, and predicting the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load equipment in the first preset time period.

[0043] For example, the preset prediction model is applied and continuously optimized according to the real-time monitoring data to adapt to environmental changes and data updates to form a trained model (corresponding to the above-mentioned target prediction model); and the trained model (corresponding to the above-mentioned target prediction model) is used to predict the photovoltaic power generation and load demand in a future period of time (corresponding to the above-mentioned first preset time period).

[0044] Step S103, generating an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating status of the photovoltaic power generation system and the energy storage system.

[0045] For example, a real-time optimized model (corresponding to the target prediction model) is used to predict the photovoltaic power generation and load demand in the future (corresponding to the first preset time period). Combined with the prediction results, the photovoltaic system power generation scheduling and energy storage charging and discharging strategy is formulated to form a detailed operation plan (corresponding to the initial operation plan).

[0046] Step S104, based on the initial operation plan, determine the target operation plan, and adjust the operation status of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0047] For example, the detailed operation plan (corresponding to the above-mentioned initial operation plan) can be adjusted online to obtain the final operation plan (corresponding to the above-mentioned target operation plan), and then the operating status of the photovoltaic power generation system and the energy storage system can be adjusted according to the final operation plan (corresponding to the above-mentioned target operation plan).

[0048] Through the above steps S101 to S104, the real-time data of the photovoltaic system, energy storage equipment and load are comprehensively monitored, and the real-time data of the photovoltaic system, energy storage equipment and load are input into the prediction model for prediction, and the photovoltaic power generation and load demand in the future period are output. Then, according to the prediction results, a detailed operation plan (i.e., the initial operation plan) is formed, and then the formed initial operation plan is adjusted online to obtain the final operation plan (i.e., the target operation plan), and the operating status of the photovoltaic power generation system and the energy storage system is adjusted according to the final operation plan (i.e., the target operation plan), so that the operation of the photovoltaic power generation and energy storage equipment can be dynamically adjusted, thereby achieving the effect of improving the operating efficiency of the photovoltaic power generation system and the energy storage system.

[0049] Optionally, in the processing method of the system provided in an embodiment of the present application, the target prediction model is obtained in the following manner: obtaining a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load equipment; using the sample data set to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

[0050] For example, collect historical photovoltaic power generation data, historical load demand data, weather forecast information, seasonal variables, special events (such as holidays) and other influencing factors (corresponding to the above sample data set). Then you can select a suitable prediction model (corresponding to the above original prediction model), such as random forest, neural network or long short-term memory network (LSTM). Then use the historical data (corresponding to the above sample data set) to train the model (corresponding to the above original prediction model) to obtain the final prediction model to be used (corresponding to the above target prediction model).

[0051] In summary, by using the training data to train the original prediction model, the prediction model that needs to be used in the end can be obtained quickly and accurately.

[0052] Optionally, in the processing method of the system provided in the embodiment of the present application, the sample data set is used to learn and train the original prediction model to obtain the trained prediction model, which includes: cleaning the sample data in the sample data set to obtain processed sample data, wherein the cleaning processing method includes at least one of the following: filling missing values, removing outliers and data standardization; performing feature extraction on the processed sample data to obtain a feature vector; and using the feature vector to learn and train the original prediction model to obtain a trained prediction model.

[0053] For example, collect data on historical photovoltaic power generation data, historical load demand data, weather forecast information, seasonal variables, special events (such as holidays) and other influencing factors (corresponding to the above sample data). Clean these data (corresponding to the above sample data), including filling missing values, removing outliers and data standardization. Then, based on the cleaned data (corresponding to the above processed sample data), extract features that are beneficial to prediction (corresponding to the above feature vectors), such as historical averages, trend changes, seasonal patterns, weather forecast related parameters (such as temperature, wind speed, cloud cover), calendar events, etc. Then use the extracted feature vectors to learn and train the pre-built model (corresponding to the above original prediction model) to obtain the final prediction model to be used (corresponding to the above target prediction model).

[0054] In summary, the quality of the data can be improved by cleaning the collected sample data.

[0055] Optionally, in the processing method of the system provided in the embodiment of the present application, the original prediction model is learned and trained using the sample data set to obtain the target prediction model, including: learning and training the original prediction model using the sample data set to obtain a trained prediction model; judging whether the trained prediction model meets preset requirements; if the trained prediction model meets the preset requirements, using the trained prediction model as the target prediction model; if the trained prediction model does not meet the preset requirements, adjusting the model structure of the trained prediction model to obtain the target prediction model.

[0056] For example, when evaluating and verifying the model, the prediction accuracy of the model (corresponding to the above-mentioned trained prediction model) can be evaluated through statistical indicators such as mean square error (MSE) and mean absolute error (MAE). If the model performance does not meet expectations (corresponding to the above-mentioned preset requirements), the structure of the model (corresponding to the above-mentioned trained prediction model) can be adjusted or the feature engineering stage can be returned to according to the error analysis results; if the model performance meets expectations (corresponding to the above-mentioned preset requirements), the trained prediction model can be directly used for prediction processing.

[0057] Through the above scheme, the performance of the trained prediction model can be easily evaluated.

[0058] Optionally, in the processing method of the system provided in the embodiment of the present application, generating an initial operation plan based on the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period includes: obtaining current status information of the energy storage system; determining the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system based on the current status information of the energy storage system, the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period; generating an initial operation plan based on the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system.

[0059] For example, a real-time optimized model (corresponding to the target prediction model mentioned above) is used to predict photovoltaic power generation and load demand in the future. Then, combined with the prediction results, the energy storage unit status (corresponding to the current status information of the energy storage system mentioned above), grid demand response and other limiting factors are considered to formulate the photovoltaic system's power generation scheduling (corresponding to the photovoltaic system's power generation strategy) and energy storage charging and discharging strategy (corresponding to the energy storage system's charging and discharging strategy) to form a detailed operation plan (corresponding to the initial operation plan mentioned above).

[0060] Through the above scheme, according to the results predicted by the prediction model, an operation plan for controlling the operating status of the photovoltaic power generation system and the energy storage system can be obtained quickly and accurately.

[0061] Optionally, in the processing method of the system provided in the embodiment of the present application, based on the initial operation plan, determining the target operation plan includes: using a preset formula to calculate the power generation data of the photovoltaic power generation system in a second preset time period, wherein the second preset time period is later than the first preset time period; determining the target operation plan according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan.

[0062] For example, the first calculation formula may be used to calculate the future photovoltaic power generation (corresponding to the power generation data of the above-mentioned photovoltaic power generation system in the second preset time period), and the first calculation formula is: Among them, G is the future photovoltaic power generation, t is time, T is the estimated time period length, and Yt is the power of the operation plan at time t. Then, according to the calculated future photovoltaic power generation (corresponding to the power generation data of the photovoltaic power generation system in the second preset time period), the initially formed detailed operation plan (corresponding to the above-mentioned initial operation plan) can be adjusted online to obtain the final operation plan (corresponding to the above-mentioned target operation plan).

[0063] In summary, by adjusting the initially formed operation plan, the final operation plan for controlling the operation of the photovoltaic power generation system and the energy storage system can be quickly and accurately determined.

[0064] Optionally, in the processing method of the system provided in the embodiment of the present application, determining the target operation plan based on the power generation data of the photovoltaic power generation system within the second preset time period and the initial operation plan includes: judging whether the power generation data of the photovoltaic power generation system within the second preset time period is less than the preset power generation data; if the power generation data of the photovoltaic power generation system within the second preset time period is not less than the preset power generation data, prohibiting adjustment of the initial operation plan, and taking the initial operation plan as the target operation plan; if the power generation data of the photovoltaic power generation system within the second preset time period is less than the preset power generation data, adjusting the initial operation plan to obtain an adjusted initial operation plan; and taking the adjusted initial operation plan as the target operation plan.

[0065] For example, determine whether the expected photovoltaic power generation G (corresponding to the power generation data of the above-mentioned photovoltaic power generation system in the second preset time period) is lower than the preset minimum power generation Y (corresponding to the above-mentioned preset power generation data); if the expected photovoltaic power generation G (corresponding to the power generation data of the above-mentioned photovoltaic power generation system in the second preset time period) is not less than the preset minimum power generation Y (corresponding to the above-mentioned preset power generation data), there is no need to adjust the energy storage operation plan (corresponding to the above-mentioned initial operation plan); if the expected photovoltaic power generation G (corresponding to the power generation data of the above-mentioned photovoltaic power generation system in the second preset time period) is less than the preset minimum power generation Y (corresponding to the above-mentioned preset power generation data), it indicates that the expected photovoltaic power generation is insufficient to meet the minimum requirements, and it is necessary to adjust the energy storage operation plan (corresponding to the above-mentioned initial operation plan), and then obtain the final operation plan (corresponding to the above-mentioned target operation plan).

[0066] Through the above scheme, the operation plan of energy storage can be conveniently optimized and adjusted.

[0067] For example, Figure 2 is a flow chart of a method for adaptive resource scheduling control of integrated photovoltaic and storage in this embodiment. Figure 2 As shown, a photovoltaic-storage integrated adaptive resource scheduling control method includes:

[0068] Step S201, by installing sensors and intelligent metering equipment, real-time monitoring of photovoltaic power generation performance, energy storage status, load consumption and environmental variables is performed;

[0069] Step S202, establishing a high-precision prediction model for photovoltaic power generation and load demand to form an operation plan for the future;

[0070] Step S203, performing online adjustment according to the current photovoltaic and energy storage operation plans;

[0071] Step S204, obtaining the power generation efficiency and time period electricity price at the current moment, and adjusting the proportion of photovoltaic power;

[0072] Step S205, managing the interaction between the photovoltaic array, the energy storage unit and the load through a coordination controller to achieve a balance between supply and demand;

[0073] Step S206, continuously monitoring the efficiency of the current photovoltaic and energy storage execution process.

[0074] In this embodiment, it is intended to achieve efficient, adaptive and optimized resource scheduling control of the photovoltaic storage integrated system, improve energy utilization, reduce operating costs, and use sensors and smart metering equipment to continuously track photovoltaic power generation performance, energy storage status, load consumption and environmental variables.

[0075] For example, Figure 3 This is a flow chart of a photovoltaic storage integrated adaptive resource scheduling control method in this embodiment, which monitors photovoltaic power generation performance, energy storage status, load consumption and environmental variables in real time by installing sensors and intelligent metering equipment, such as Figure 3 As shown, by installing sensors and smart metering equipment, real-time monitoring of photovoltaic power generation performance, energy storage status, load consumption and environmental variables is carried out, including:

[0076] Step S301, deploying high-precision sensors at the photovoltaic panel array, energy storage unit and key load points to monitor light intensity, temperature, voltage, current and power in real time;

[0077] Step S302: The real-time data collected by the sensors and smart metering devices are transmitted to the central control system via a wireless or wired network to ensure data synchronization and real-time update.

[0078] For example, light intensity sensors and temperature sensors are installed on each photovoltaic panel of the photovoltaic panel array to monitor the photovoltaic panel's photoelectric conversion efficiency and operating temperature in real time. At the same time, state monitoring sensors such as electricity meters and temperature sensors are deployed in energy storage units to grasp the state of charge (SoC) and health state (SoH) of energy storage units in real time. Current and voltage sensors are installed at key load points, such as important production lines in factories or server clusters in data centers, to accurately measure the real-time power consumption of loads. These sensors can capture the subtle differences in load changes and provide data support for subsequent energy consumption analysis and optimization. All sensors and smart metering devices are connected to the central control system via wireless LAN (Wi-Fi) or wired Ethernet. After appropriate analog-to-digital conversion and signal processing at the sensor end, the data is transmitted to the central control system through the network. To ensure the synchronization and real-time nature of the data, time synchronization protocols such as the Network Time Protocol (NTP) are used to ensure the time consistency of all monitoring devices. The central control system uses high-performance servers and runs specially designed data processing software that can receive, store and analyze incoming data in real time. The system performs real-time analysis on the received data, including data validity verification, outlier detection and elimination, and timely updating of the system operation status display. For example, when the light intensity sensor detects that the photovoltaic panel is blocked by clouds, resulting in a decrease in light intensity, the central control system will immediately lower the expected power generation of the photovoltaic panel, and dynamically adjust the charging and discharging strategy of the energy storage unit and the power supply plan of the load according to the current state of the energy storage unit and the load demand, so as to maintain the energy balance and stable operation of the system.

[0079] For example, Figure 4 It is a flow chart of establishing a high-precision prediction model of photovoltaic power generation and load demand in a photovoltaic storage integrated adaptive resource scheduling control method in this embodiment to form an operation plan at a future moment, such as Figure 4 As shown, a high-precision prediction model for photovoltaic power generation and load demand is established to form an operation plan for the future, including:

[0080] Step S401, applying the preset prediction model and continuously optimizing it according to the real-time monitoring data to adapt to environmental changes and data updates to form a trained model;

[0081] Step S402: Use the trained model to predict photovoltaic power generation and load demand in the future.

[0082] For example, collect data on historical photovoltaic power generation data, historical load demand data, weather forecast information, seasonal variables, special events (such as holidays) and other influencing factors. Clean these data, including filling missing values, removing outliers and standardizing data to improve data quality. Feature engineering: Based on the cleaned data, extract features that are beneficial to prediction, such as historical averages, trend changes, seasonal patterns, weather forecast related parameters (such as temperature, wind speed, cloud cover), calendar events, etc. Model selection and training: Select a suitable prediction model, such as random forest, neural network or long short-term memory network (LSTM). Use historical data to train the model and optimize model performance through cross-validation and parameter tuning. Model evaluation and verification: Evaluate the prediction accuracy of the model through statistical indicators such as mean square error (MSE) and mean absolute error (MAE). If the model performance does not meet expectations, adjust the model structure or return to the feature engineering stage based on the error analysis results. Real-time optimization and adaptation: Apply the trained model to actual operation and continuously receive new monitoring data. Use the rolling window method, that is, regularly update the model with new data to ensure that the prediction model can adapt to environmental changes and data updates. Forecasting and plan formation: Using a real-time optimized model, the photovoltaic power generation and load demand in the future are predicted. Combined with the forecast results, considering the limiting factors such as the state of the energy storage unit and the grid demand response, the photovoltaic system's power generation scheduling and energy storage charging and discharging strategies are formulated to form a detailed operation plan. For example: Suppose the weather forecast on a certain day shows that it will be mainly sunny in the next few days, and the model predicts that the photovoltaic power generation will increase significantly. At the same time, based on historical load data and upcoming holiday activities, the load demand is also predicted to increase. Therefore, the operation plan will guide the energy storage unit to charge when the photovoltaic power generation is higher than the average, and release energy when the demand peaks, so as to maximize the benefits of photovoltaic power generation and ensure stable power supply.

[0083] For example, Figure 5 is a flowchart of an integrated photovoltaic and energy storage adaptive resource scheduling control method in this embodiment for online adjustment according to the current photovoltaic and energy storage operation plan, such as Figure 5 As shown, online adjustments are made based on the current photovoltaic and energy storage operation plans, including:

[0084] Step S501, obtaining the current photovoltaic and energy storage operation plan;

[0085] Step S502, calculating the future photovoltaic power generation using the first calculation formula;

[0086] Step S503, calculating the minimum energy storage value using a second calculation formula according to the energy storage operation plan;

[0087] Step S504, determining whether the third calculation formula is satisfied, if satisfied, adjusting the energy storage operation plan using the fourth calculation formula, otherwise no processing is performed;

[0088] Among them, the first calculation formula is: Among them, G is the future power generation of photovoltaic power generation, t is time, T is the estimated period length, and Yt is the power of the operation plan at time t;

[0089] And the second calculation formula is: EM = Min (Et), where Min() is a function to extract the minimum value within the estimated time period length in the future T, Et is the energy storage capacity of the operation plan at time t, and EM is the minimum energy storage value;

[0090] In addition, the third calculation formula is: G<Y, where Y is the preset minimum power generation;

[0091] Moreover, the fourth calculation formula is: AFt=Et+ΔE, wherein AFt is the modified energy storage operation plan, Et is the energy storage capacity of the operation plan at time t, and ΔE is a preset correction value.

[0092] For example, first obtain the scheduled operation plan of photovoltaic power generation and energy storage at the current moment from the central control system. This includes the expected power generation value and energy storage charging and discharging plan calculated based on the latest new energy supply and demand forecast and historical data. Calculate the future photovoltaic power generation: Use the first calculation formula to calculate the expected power generation G of the photovoltaic array within the estimated time period length T. Here, Yt represents the photovoltaic output power at time t. By integrating Yt in the T period, the estimated total power generation can be obtained. Calculate the minimum energy storage value: According to the operation plan of energy storage, use the second calculation formula to calculate the minimum energy storage value EM in the next estimated period T. Function Min() is used to extract the minimum value of energy storage capacity Et in the future estimated period length T. Determine whether the power generation is sufficient: Use the third calculation formula to determine whether the expected photovoltaic power generation G is lower than the preset minimum power generation Y. If G is less than Y, it indicates that the expected photovoltaic power generation may not meet the demand. Adjust the energy storage operation plan: If the judgment result of the third step is true, that is, the photovoltaic power generation is not enough to meet the minimum requirement, then use the fourth calculation formula to adjust the energy storage operation plan. Here, AFt is the adjusted energy storage operation plan, Et is the energy storage capacity before adjustment, and △E is a pre-set correction value based on the difference to ensure that the energy storage system can supplement the insufficient photovoltaic power generation. For example: Assume that on a clear afternoon, due to the sudden increase in cloud cover, the actual power generation of the photovoltaic array may be lower than the predicted value. At this time, the system detects that the photovoltaic power generation G in the next hour is 5kWh, while the preset minimum power generation Y is 6kWh. According to the real-time status of energy storage, the system calculates the minimum energy storage value EM to be 3kWh. Since G<Y, the system will automatically trigger the adjustment mechanism. Assuming that the preset correction value △E is 2kWh, the adjusted energy storage operation plan AFt will be the original energy storage capacity Et plus 2kWh to ensure the stability of energy supply.

[0093] For example, Figure 6 This is a flow chart of obtaining the power generation efficiency and time period electricity price at the current moment and adjusting the proportion of photovoltaic power in a photovoltaic integrated adaptive resource scheduling control method in this embodiment, such as Figure 6 As shown, the power generation efficiency and electricity price at the current moment are obtained, and the proportion of photovoltaic power is adjusted, including:

[0094] Step S601, obtaining the future photovoltaic power generation to determine whether it satisfies the fifth calculation formula;

[0095] Step S602, adjusting the photovoltaic power ratio by using the sixth calculation formula according to the photovoltaic power ratio at the current moment;

[0096] Step S603, determining whether the energy storage life and power generation efficiency do not satisfy the seventh calculation formula, if not, adjusting the preset correction value to 10% of the original value;

[0097] Moreover, the fifth calculation formula is: G>Y2×(SJ÷PJ), where Y2 is the judgment margin, SJ is the time period electricity price, and PJ is the average electricity price; the sixth calculation formula is: GB2=GB+△g, where GB2 is the adjusted photovoltaic power ratio, GB is the photovoltaic power ratio before adjustment, and △g is the adjustment range of the photovoltaic ratio; the seventh calculation formula is: Among them, F is the power generation efficiency judgment margin, f is the power generation efficiency, S is the remaining life of the energy storage, and L is the life limit.

[0098] For example, the fifth calculation formula is used to determine whether the estimated photovoltaic power generation G is greater than Y2 multiplied by (SJ ÷ PJ). Here, Y2 is the preset judgment margin, SJ is the electricity price in the current period, and PJ is the average electricity price. This formula is used to evaluate whether the economic benefits of photovoltaic power generation are high enough under the current period electricity price to decide whether to increase the proportion of photovoltaic power. Adjust the proportion of photovoltaic power: If the result of the fifth calculation formula is true, that is, the photovoltaic power generation is sufficient to provide good benefits under the current higher period electricity price, then adjust the proportion of photovoltaic power GB2 according to the sixth calculation formula. GB is the proportion of photovoltaic power before adjustment, and △g is the adjustment range of the photovoltaic proportion calculated based on the strategy and forecast data. Consider the energy storage life and power generation efficiency: Before adjusting the proportion of photovoltaic power, use the seventh calculation formula to determine whether the current power generation efficiency f and the remaining life of energy storage S meet the conditions. F is the power generation efficiency judgment margin, and L is the life limit. If the power generation efficiency or energy storage life does not meet the conditions, it indicates that the system may need to reduce the proportion of photovoltaic power to protect the energy storage unit and avoid excessive use affecting the life. Correction value adjustment: If the judgment result of the seventh calculation formula is false, that is, the power generation efficiency or energy storage life does not meet the conditions, the preset correction value will be adjusted to 10% of the original value to reduce dependence on energy storage and extend its service life. For example: Assume that the electricity price SJ in the current period is high, the average electricity price PJ is 0.5 yuan / kWh, and the electricity price SJ in the current period is 0.7 yuan / kWh, and the preset judgment margin Y2 is 1.2. If the photovoltaic power generation G is sufficiently higher than Y2×(SJ÷PJ), the system will calculate the adjustment range △g and increase the photovoltaic power share GB2 accordingly to make full use of the high returns in the high-price period. At the same time, if it is detected that the energy storage life is close to the limit L or the power generation efficiency f is lower than the preset margin F, the system will automatically reduce the adjustment range of the photovoltaic power share to ensure the stability of system operation and the long-term use of equipment.

[0099] For example, Figure 7 is a flow chart of a photovoltaic storage integrated adaptive resource scheduling control method in this embodiment, which manages the interaction between the photovoltaic array, the energy storage unit and the load through a coordinated controller to achieve supply and demand balance, such as Figure 7 As shown in the figure, the interaction between the photovoltaic array, energy storage unit and load is managed by a coordinated controller to achieve a balance between supply and demand, including:

[0100] Step S701, deploying a coordination controller, which is responsible for receiving forecast data from photovoltaic power generation and load demand, status information of energy storage units, and real-time monitoring data;

[0101] Step S702: The coordination controller sends the strategy to the photovoltaic array, energy storage unit and load in real time to implement specific operation commands.

[0102] For example, a high-performance coordination controller is deployed in the system, which can integrate and process data from different sources. This includes the forecast data of photovoltaic power generation, load demand forecast, status information of energy storage units (such as remaining capacity, charging status, health status), and real-time monitored data (such as current power generation, actual load, weather conditions). Data reception and processing: The coordination controller receives the above data and analyzes and processes the data in real time through built-in algorithms. For example, the controller compares the predicted photovoltaic power generation with the actual power generation, evaluates the current status of the energy storage unit, and estimates the load demand in the future based on the forecast data. Formulate coordination strategies: Based on the results of data analysis, the coordination controller formulates corresponding operation strategies. These strategies include adjusting the working point of the photovoltaic array to maximize power generation efficiency, determining the charging and discharging plan of the energy storage unit, and adjusting the energy consumption of the load according to the load demand. Issue control commands: The coordination controller converts the formulated operation strategy into specific operation commands and issues them to the photovoltaic array, energy storage unit and load in real time. For example, the controller may instruct the photovoltaic array to adjust its angle to adapt to the change in the height of the sun, instruct the energy storage unit to start charging or discharging, or instruct the load to reduce unnecessary power consumption to reduce the total load. Dynamic adjustment and optimization: The coordination controller continuously monitors the actual operation status and the effect of the predetermined strategy, and dynamically adjusts the control command according to the real-time data. This feedback mechanism ensures that the system can respond quickly to environmental changes, optimize energy utilization, and achieve a balance between supply and demand. For example: suppose the weather forecast shows that it will change from sunny to cloudy on a certain day. The coordination controller predicts that the photovoltaic power generation will decrease, and the load demand forecast shows that there will be a peak in the afternoon. Therefore, the controller instructs the energy storage unit in advance to store energy when the photovoltaic power generation is high, and releases energy when the photovoltaic power generation decreases and the load peak comes. At the same time, the peak load is reduced by adjusting the power consumption plan of non-critical loads, thereby ensuring the balance of energy supply and demand.

[0103] For example, Figure 8 is a flow chart of continuously monitoring the efficiency of the current photovoltaic and energy storage execution process in a photovoltaic and energy storage integrated adaptive resource scheduling control method in this embodiment, such as Figure 8 As shown, the efficiency of the current photovoltaic and energy storage execution processes is continuously monitored, including:

[0104] Step S801, selecting an online adjustment range of the photovoltaic ratio that makes the execution process most efficient according to a preset database of photovoltaic ratio adjustment ranges;

[0105] Step S802, obtaining the current proportion of the load, and automatically adjusting the current proportion of the load when adjusting the photovoltaic proportion.

[0106] For example, first, a historical database containing different PV ratio adjustment ranges and corresponding execution process efficiencies is established. This database is accumulated through long-term operation data and contains the system execution efficiency corresponding to various PV ratio adjustment ranges under different environmental conditions and different load requirements. Select the optimal PV ratio adjustment range: During operation, based on the current environmental conditions and load requirements, the PV ratio adjustment range that makes the execution process most efficient is selected by querying the PV ratio adjustment range database. This selection process is automatically completed by the central control system to optimize system performance in real time. Obtain the current load ratio: The current load ratio is obtained through the real-time monitoring system. This includes information such as the total load amount and the ratio of critical load to non-critical load. This information is crucial for the subsequent adjustment of the load strategy. Automatically adjust the load ratio: While adjusting the PV ratio, the system automatically adjusts the current load ratio according to the optimization strategy. This may include measures such as reducing the power consumption of non-critical loads and delaying the execution of certain non-real-time tasks to ensure that the overall energy utilization efficiency is optimized when the PV energy supply changes. For example: Assume that at a certain moment, based on the data in the database and the current environmental conditions (such as clear weather and high PV power generation efficiency), the system chooses to increase the adjustment range of the PV ratio. At the same time, it is detected that part of the current load is adjustable non-critical load. In order to maximize the utilization of photovoltaic energy, the system automatically reduces the proportion of this part of non-critical load, so that more photovoltaic power can be used for critical loads or stored in energy storage units, improving the overall energy efficiency and the efficiency of the system execution process.

[0107] For example, Fig. 9 is a schematic diagram of a photovoltaic storage integrated adaptive resource scheduling control system in this embodiment, such as Fig. 9 As shown, a photovoltaic-storage integrated adaptive resource scheduling control system includes:

[0108] The data monitoring and collection module 901 is used to monitor the photovoltaic power generation performance, energy storage status, load consumption and environmental variables in real time by installing sensors and intelligent metering equipment;

[0109] The precision prediction module 902 is used to establish a high-precision prediction model for photovoltaic power generation and load demand to form an operation plan for the future;

[0110] Adaptive scheduling module 903, used to make online adjustments according to the current photovoltaic and energy storage operation plans;

[0111] The optimization integrated module 904 is used to obtain the power generation efficiency and time period electricity price at the current moment and adjust the proportion of photovoltaic power;

[0112] A coordination control module 905 is used to manage the interaction between the photovoltaic array, the energy storage unit and the load through a coordination controller to achieve a balance between supply and demand;

[0113] The monitoring and adjustment module 906 is used to continuously monitor the efficiency of the current photovoltaic and energy storage execution process.

[0114] In this embodiment, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0115] For example, this embodiment relates to an integrated photovoltaic and energy storage adaptive resource scheduling control method and system. The solution includes real-time monitoring of photovoltaic power generation performance, energy storage status, load consumption and environmental variables by installing sensors and intelligent metering equipment; establishing a high-precision prediction model for photovoltaic power generation and load demand to form an operation plan for future moments; making online adjustments based on the photovoltaic and energy storage operation plans at the current moment; obtaining the power generation efficiency and time period electricity price at the current moment, and adjusting the proportion of photovoltaic electricity; managing the interaction between photovoltaic arrays, energy storage units and loads through a coordination controller to achieve a balance between supply and demand; and continuously monitoring the efficiency of the current photovoltaic and energy storage execution process. The solution optimizes energy use, reduces energy costs, and improves the system's ability to accept unstable renewable energy by dynamically adjusting the operation of photovoltaic power generation and energy storage equipment.

[0116] In addition, this embodiment includes the following beneficial effects: by comprehensively monitoring the real-time data of the photovoltaic system, energy storage equipment and loads, data support is provided for accurate resource scheduling, and the system's ability to respond to environmental changes and overall operating efficiency are enhanced; and efficient collaboration between photovoltaics, energy storage and loads is achieved, improving system stability, ensuring a balance between energy supply and demand, and optimizing overall system performance.

[0117] In summary, the processing method of the system provided in the embodiment of the present application obtains the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device are input into the target prediction model for processing, and the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period are predicted; based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, an initial operation plan is generated, wherein the initial operation plan is used to control the operating state of the photovoltaic power generation system and the energy storage system; based on the initial operation plan, a target operation plan is determined, and the operating state of the photovoltaic power generation system and the energy storage system is adjusted according to the target operation plan, thereby solving the problem in the related art that the operating state of the photovoltaic and energy storage systems is adjusted based on a fixed operation strategy, resulting in low operating efficiency of the photovoltaic power generation system and the energy storage system. By comprehensively monitoring the real-time data of the photovoltaic system, energy storage equipment and load, and inputting the real-time data of the photovoltaic system, energy storage equipment and load into the prediction model for prediction, the photovoltaic power generation and load demand in the future period are output, and then a detailed operation plan (i.e., the initial operation plan) is formed based on the prediction results. The initial operation plan is then adjusted online to obtain the final operation plan (i.e., the target operation plan), and the operating status of the photovoltaic power generation system and the energy storage system is adjusted according to the final operation plan (i.e., the target operation plan), so that the operation of the photovoltaic power generation and energy storage equipment can be dynamically adjusted, thereby achieving the effect of improving the operating efficiency of the photovoltaic power generation system and the energy storage system.

[0118] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0119] The embodiment of the present application also provides a system processing device. It should be noted that the system processing device of the embodiment of the present application can be used to execute the system processing method provided by the embodiment of the present application. The system processing device provided by the embodiment of the present application is introduced below.

[0120] Fig.10 Schematic diagram of a processing device of a system provided in an embodiment of the present application. Fig.10 As shown, the device includes: a first acquisition unit 1001, a first prediction unit 1002, a first generation unit 1003 and a first processing unit 1004.

[0121] Specifically, the first acquisition unit 1001 is used to acquire historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system;

[0122] The first prediction unit 1002 is used to input the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into the target prediction model for processing, and predict the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period;

[0123] The first generating unit 1003 is used to generate an initial operation plan according to the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operation status of the photovoltaic power generation system and the energy storage system;

[0124] The first processing unit 1004 is used to determine a target operation plan based on the initial operation plan, and adjust the operation states of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0125] In summary, the processing device of the system provided in the embodiment of the present application obtains the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device through the first acquisition unit 1001, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; the first prediction unit 1002 inputs the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into the target prediction model for processing, and predicts the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period; the first generation unit 1003 generates an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating state of the photovoltaic power generation system and the energy storage system; the first processing unit 1004 determines the target operation plan based on the initial operation plan, and adjusts the operating state of the photovoltaic power generation system and the energy storage system according to the target operation plan, which solves the problem that the operating state of the photovoltaic and energy storage systems is adjusted based on a fixed operation strategy in the related art, resulting in low operating efficiency of the photovoltaic power generation system and the energy storage system. By comprehensively monitoring the real-time data of the photovoltaic system, energy storage equipment and load, and inputting the real-time data of the photovoltaic system, energy storage equipment and load into the prediction model for prediction, the photovoltaic power generation and load demand in the future period are output, and then a detailed operation plan (i.e., the initial operation plan) is formed based on the prediction results. The initial operation plan is then adjusted online to obtain the final operation plan (i.e., the target operation plan), and the operating status of the photovoltaic power generation system and the energy storage system is adjusted according to the final operation plan (i.e., the target operation plan), so that the operation of the photovoltaic power generation and energy storage equipment can be dynamically adjusted, thereby achieving the effect of improving the operating efficiency of the photovoltaic power generation system and the energy storage system.

[0126] Optionally, in the processing device of the system provided in the embodiment of the present application, the first generating unit includes: a first acquisition module, used to acquire current status information of the energy storage system; a first determination module, used to determine the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system based on the current status information of the energy storage system, the power generation data of the photovoltaic power generation system within a first preset time period, and the load demand data of the load device within the first preset time period; the first generating module, used to generate an initial operation plan based on the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system.

[0127] Optionally, in the processing device of the system provided in the embodiment of the present application, the first processing unit includes: a first calculation module, used to calculate the power generation data of the photovoltaic power generation system within a second preset time period using a preset formula, wherein the second preset time period is later than the first preset time period; a second determination module, used to determine the target operation plan based on the power generation data of the photovoltaic power generation system within the second preset time period and the initial operation plan.

[0128] Optionally, in the processing device of the system provided in the embodiment of the present application, the second determination module includes: a first judgment submodule, used to judge whether the power generation data of the photovoltaic power generation system within the second preset time period is less than the preset power generation data; a first determination submodule, used to prohibit adjustment of the initial operation plan if the power generation data of the photovoltaic power generation system within the second preset time period is not less than the preset power generation data, and use the initial operation plan as the target operation plan; a first adjustment submodule, used to adjust the initial operation plan if the power generation data of the photovoltaic power generation system within the second preset time period is less than the preset power generation data to obtain an adjusted initial operation plan; and a second determination submodule, used to use the adjusted initial operation plan as the target operation plan.

[0129] Optionally, in the processing device of the system provided in the embodiment of the present application, the target prediction model is obtained through the following units: a second acquisition unit, used to acquire a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load equipment; a first training unit, used to use the sample data set to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

[0130] Optionally, in the processing device of the system provided in the embodiment of the present application, the first training unit includes: a first training module, which is used to use the sample data set to learn and train the original prediction model to obtain a trained prediction model; a first judgment module, which is used to judge whether the trained prediction model meets the preset requirements; a third determination module, which is used to use the trained prediction model as the target prediction model if the trained prediction model meets the preset requirements; and a first adjustment module, which is used to adjust the model structure of the trained prediction model if the trained prediction model does not meet the preset requirements to obtain the target prediction model.

[0131] Optionally, in the processing device of the system provided in the embodiment of the present application, the first training module includes: a first processing sub-module, used to clean the sample data in the sample data set to obtain processed sample data, wherein the cleaning processing method includes at least one of the following: filling missing values, removing outliers and data standardization; a second processing sub-module, used to perform feature extraction on the processed sample data to obtain a feature vector; and a first training sub-module, used to use the feature vector to learn and train the original prediction model to obtain a trained prediction model.

[0132] The processing device of the system includes a processor and a memory. The above-mentioned first acquisition unit 1001, first prediction unit 1002, first generation unit 1003 and first processing unit 1004 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0133] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the operating efficiency of the photovoltaic power generation system and the energy storage system can be improved by adjusting the kernel parameters.

[0134] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0135] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the processing method of the system is implemented.

[0136] An embodiment of the present invention provides a processor, which is used to run a program, wherein the processing method of the system is executed when the program is run.

[0137] like Fig.11As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: obtaining historical power generation data of a photovoltaic power generation system, historical status information of an energy storage system and historical consumption data of a load device, wherein the load device is a device powered by a target system, and the target system is a system constructed based on a photovoltaic power generation system and an energy storage system; inputting the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into a target prediction model for processing, and predicting the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period; generating an initial operation plan based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating status of the photovoltaic power generation system and the energy storage system; determining a target operation plan based on the initial operation plan, and adjusting the operating status of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0138] When the processor executes the program, the following steps are also implemented: based on the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, an initial operation plan is generated, including: obtaining the current state information of the energy storage system; based on the current state information of the energy storage system, the power generation data of the photovoltaic power generation system in the first preset time period and the load demand data of the load device in the first preset time period, determining the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system; based on the power generation strategy of the photovoltaic power generation system and the charging and discharging strategy of the energy storage system, an initial operation plan is generated.

[0139] When the processor executes the program, the following steps are also implemented: based on the initial operation plan, determining the target operation plan includes: using a preset formula to calculate the power generation data of the photovoltaic power generation system in a second preset time period, wherein the second preset time period is later than the first preset time period; determining the target operation plan according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan.

[0140] When the processor executes the program, the following steps are also implemented: according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan, the target operation plan is determined, including: judging whether the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data; if the power generation data of the photovoltaic power generation system in the second preset time period is not less than the preset power generation data, it is prohibited to adjust the initial operation plan, and the initial operation plan is used as the target operation plan; if the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data, the initial operation plan is adjusted to obtain the adjusted initial operation plan; and the adjusted initial operation plan is used as the target operation plan.

[0141] When the processor executes the program, the following steps are also implemented: the target prediction model is obtained by: obtaining a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load equipment; using the sample data set to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

[0142] When the processor executes the program, the following steps are also implemented: using the sample data set to learn and train the original prediction model to obtain the target prediction model, including: using the sample data set to learn and train the original prediction model to obtain a trained prediction model; judging whether the trained prediction model meets the preset requirements; if the trained prediction model meets the preset requirements, the trained prediction model is used as the target prediction model; if the trained prediction model does not meet the preset requirements, the model structure of the trained prediction model is adjusted to obtain the target prediction model.

[0143] When the processor executes the program, the following steps are also implemented: using the sample data set to learn and train the original prediction model, and obtaining the trained prediction model includes: cleaning the sample data in the sample data set to obtain the processed sample data, wherein the cleaning method includes at least one of the following: filling missing values, removing outliers and data standardization; performing feature extraction on the processed sample data to obtain a feature vector; using the feature vector to learn and train the original prediction model to obtain the trained prediction model. The device in this article can be a server, PC, PAD, mobile phone, etc.

[0144] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: obtaining historical power generation data of a photovoltaic power generation system, historical status information of an energy storage system, and historical consumption data of a load device, wherein the load device is a device powered by a target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; inputting the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system, and the historical consumption data of the load device into a target prediction model for processing, and predicting the power generation data of the photovoltaic power generation system within a first preset time period and the load demand data of the load device within the first preset time period; generating an initial operation plan based on the power generation data of the photovoltaic power generation system within the first preset time period and the load demand data of the load device within the first preset time period, wherein the initial operation plan is used to control the operating status of the photovoltaic power generation system and the energy storage system; determining a target operation plan based on the initial operation plan, and adjusting the operating status of the photovoltaic power generation system and the energy storage system according to the target operation plan.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0151] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0154] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A system processing method, characterized in that: include: Acquire historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load device, wherein the load device is a device powered by the target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; Inputting historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system and historical consumption data of the load device into a target prediction model for processing, and predicting power generation data of the photovoltaic power generation system within a first preset time period and load demand data of the load device within the first preset time period; Generate an initial operation plan based on the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operating states of the photovoltaic power generation system and the energy storage system; Based on the initial operation plan, a target operation plan is determined, and the operation states of the photovoltaic power generation system and the energy storage system are adjusted according to the target operation plan.

2. The method according to claim 1, characterized in that Generating an initial operation plan according to the power generation data of the photovoltaic power generation system in a first preset time period and the load demand data of the load device in the first preset time period includes: Obtaining current status information of the energy storage system; Determine the power generation strategy of the photovoltaic power generation system and the charge and discharge strategy of the energy storage system according to the current state information of the energy storage system, the power generation data of the photovoltaic power generation system in a first preset time period, and the load demand data of the load device in the first preset time period; The initial operation plan is generated based on the power generation strategy of the photovoltaic power generation system and the charge and discharge strategy of the energy storage system.

3. The method according to claim 1, characterized in that Based on the initial operation plan, determining the target operation plan includes: Calculating the power generation data of the photovoltaic power generation system in a second preset time period using a preset formula, wherein the second preset time period is later than the first preset time period; The target operation plan is determined according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan.

4. The method according to claim 3, characterized in that: Determining the target operation plan according to the power generation data of the photovoltaic power generation system in the second preset time period and the initial operation plan includes: Determining whether the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data; If the power generation data of the photovoltaic power generation system in the second preset time period is not less than the preset power generation data, it is prohibited to adjust the initial operation plan, and the initial operation plan is used as the target operation plan; If the power generation data of the photovoltaic power generation system in the second preset time period is less than the preset power generation data, adjusting the initial operation plan to obtain an adjusted initial operation plan; The adjusted initial operation plan is used as the target operation plan.

5. The method according to claim 1, characterized in that The target prediction model is obtained by: Acquire a sample data set, wherein the sample data set includes at least: historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load device; The sample data set is used to learn and train the original prediction model to obtain the target prediction model, wherein the original prediction model is a model constructed based on a random forest model or a long short-term memory network.

6. The method according to claim 5, characterized in that The sample data set is used to train the original prediction model to obtain the target prediction model, including: Using the sample data set to train the original prediction model to obtain a trained prediction model; Determine whether the trained prediction model meets the preset requirements; If the trained prediction model meets the preset requirement, the trained prediction model is used as the target prediction model; If the trained prediction model does not meet the preset requirement, the model structure of the trained prediction model is adjusted to obtain the target prediction model.

7. The method according to claim 6, characterized in that The sample data set is used to train the original prediction model, and the trained prediction model includes: Performing cleaning processing on the sample data in the sample data set to obtain processed sample data, wherein the cleaning processing includes at least one of the following methods: filling missing values, removing outliers, and data standardization; Performing feature extraction processing on the processed sample data to obtain a feature vector; The original prediction model is trained using the feature vector to obtain the trained prediction model.

8. A processing device of a system, characterized in that: include: A first acquisition unit is used to acquire historical power generation data of the photovoltaic power generation system, historical status information of the energy storage system, and historical consumption data of the load device, wherein the load device is a device powered by a target system, and the target system is a system constructed based on the photovoltaic power generation system and the energy storage system; A first prediction unit is used to input the historical power generation data of the photovoltaic power generation system, the historical status information of the energy storage system and the historical consumption data of the load device into a target prediction model for processing, and predict the power generation data of the photovoltaic power generation system within a first preset time period and the load demand data of the load device within the first preset time period; A first generating unit, configured to generate an initial operation plan according to power generation data of the photovoltaic power generation system in a first preset time period and load demand data of the load device in the first preset time period, wherein the initial operation plan is used to control the operation states of the photovoltaic power generation system and the energy storage system; The first processing unit is used to determine a target operation plan based on the initial operation plan, and adjust the operation states of the photovoltaic power generation system and the energy storage system according to the target operation plan.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, wherein the program executes the processing method of the system according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the processing method of the system described in any one of claims 1 to 7.

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