Photovoltaic energy storage allocation method and system

Abstract

The invention provides a photovoltaic energy storage deployment method and system, and the method achieves the efficient photovoltaic energy storage management through integrating a data collection module, a weather forecast module, a prediction model training module, a plan generation module, a configuration scheme selection module, an energy supply control module, and an efficiency evaluation and correction module. According to the technical scheme, an energy distribution plan is generated, operation data are collected in real time to evaluate and correct the plan, the system can select an optimal energy configuration scheme according to a prediction result and a preset condition, energy supply testing is carried out, and an energy supply execution scheme is finally determined, and the energy management efficiency and prediction accuracy of the photovoltaic energy storage system are improved through the technical scheme. Energy distribution is optimized, the self-adaptive capacity of the system is enhanced, and powerful support is provided for wide application of the photovoltaic energy storage technology.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and more specifically, to a photovoltaic energy storage allocation method and system. Background Art

[0002] In recent years, with the growing global demand for renewable energy, photovoltaic power generation has received widespread attention as a clean and renewable form of energy. Photovoltaic power generation systems provide electricity to homes, businesses and industries by converting sunlight into electrical energy. However, a major limitation of photovoltaic power generation is the intermittent and unpredictable nature of its output, which is mainly affected by weather conditions, geographical location and time. In order to overcome the intermittent problem of photovoltaic power generation, photovoltaic energy storage systems have been introduced to store excess electricity generated by photovoltaic panels when there is sufficient sunlight, and release electricity when there is insufficient sunlight or peak demand, which improves the self-sufficiency of photovoltaic systems and contributes to the stability of the power grid.

[0003] The existing photovoltaic energy storage deployment technology has low energy management efficiency and poor prediction accuracy, which increases operating costs. Summary of the invention

[0004] In order to overcome the problems of low energy management efficiency and poor prediction accuracy in the photovoltaic energy storage allocation of the prior art, the present invention designs a photovoltaic energy storage allocation method and system that can effectively solve the problems.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A photovoltaic energy storage deployment method comprises the following steps:

[0007] Obtain environmental data and historical power generation data of the photovoltaic energy storage system;

[0008] Obtain weather forecast data for different time periods through the established online weather service platform;

[0009] Based on the acquired data, train the photovoltaic power generation prediction model and the power load demand prediction model;

[0010] Generate energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand;

[0011] Select the best energy allocation plan from the energy allocation plan, and control the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected plan;

[0012] Collect the operating data of the energy storage unit in real time, evaluate the efficiency level of the energy storage unit, and revise the energy allocation plan based on the evaluation results;

[0013] The photovoltaic energy storage system is tested for energy supply through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution plan.

[0014] Preferably, the training of the photovoltaic power generation prediction model and the power load demand prediction model based on the acquired data further includes:

[0015] Obtain multi-dimensional data samples that integrate weather forecast data, environmental data of photovoltaic energy storage systems, and historical power generation data;

[0016] Performing deep feature extraction on the multidimensional data sample to generate a first feature vector representing meteorological variation and a second feature vector describing the performance of the photovoltaic energy storage system;

[0017] Deeply encoding the first feature vector and the second feature vector respectively to obtain a first fine-grained feature vector related to meteorological parameters and a second fine-grained feature vector related to equipment performance;

[0018] Dynamically integrating the first fine-grained feature vector and the second fine-grained feature vector to generate a comprehensive target feature vector, and calculating a dynamic alignment loss of a photovoltaic power generation prediction model based on the target feature vector;

[0019] Based on the initial loss and dynamic alignment loss of the photovoltaic power generation prediction model, the model parameters of the photovoltaic power generation prediction model are dynamically adjusted and optimized.

[0020] Preferably, the generating of energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand also includes:

[0021] The predicted photovoltaic power generation and power load demand data are divided according to the time series to form a plurality of continuous time period sets, each of which contains the photovoltaic power generation prediction value and the power load demand prediction value in the corresponding time period;

[0022] For each time period set, different energy allocation strategies are set according to preset conditions;

[0023] Based on the set energy allocation strategy, combined with the predicted value of photovoltaic power generation and the predicted value of power load demand, the allocation ratio and specific value of each energy type in each time period are calculated to generate a preliminary energy allocation plan;

[0024] The generated preliminary energy allocation plan is verified to ensure that it meets the requirements for safe and stable operation of the power grid. At the same time, the charging and discharging efficiency and life management of the photovoltaic energy storage system are considered, and necessary adjustments and optimizations are made to the plan.

[0025] Preferably, the selecting the best energy allocation scheme from the energy allocation scheme and controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected scheme further includes:

[0026] According to the preset evaluation indicators, a comprehensive evaluation is conducted on the generated multiple energy allocation plans;

[0027] Select the energy configuration plan with the best comprehensive evaluation as the optimal plan for the current time period and preset conditions;

[0028] Based on the selected optimal solution, the energy supply of the energy storage unit of the photovoltaic energy storage system is managed.

[0029] Preferably, the real-time collection of the operating data of the energy storage unit, the evaluation of the efficiency level of the energy storage unit, and the modification of the energy allocation plan according to the evaluation result also include:

[0030] Collect the operation data of the photovoltaic energy storage system in real time through sensors;

[0031] Use data analysis technology to process and analyze the collected operating data to evaluate the efficiency level of the photovoltaic energy storage system;

[0032] Based on the evaluation results, the energy allocation plan is revised.

[0033] Preferably, the energy supply test of the photovoltaic energy storage system is performed respectively through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution scheme, which also includes:

[0034] Apply each energy configuration scheme in the revised energy allocation plan to the photovoltaic energy storage system for energy supply testing;

[0035] During the test, the operating status and performance indicators of the photovoltaic energy storage system are monitored in real time;

[0036] According to the monitoring results, the energy configuration plan with the highest priority weight is selected as the final energy supply execution plan.

[0037] A photovoltaic energy storage dispatching system, comprising:

[0038] Data acquisition module, used to collect environmental data and historical power generation data of the photovoltaic energy storage system;

[0039] The meteorological data acquisition module obtains meteorological forecast data for different time periods in real time or regularly through the established online meteorological service platform;

[0040] The prediction model module trains the photovoltaic power generation prediction model and the power load demand prediction model based on the collected environmental data, historical power generation data and weather forecast data;

[0041] A plan generation module is used to generate energy allocation plans for different time periods and under different conditions based on the predicted photovoltaic power generation and power load demand and in combination with different preset conditions;

[0042] A configuration scheme selection module is used to select the optimal energy configuration scheme from the energy allocation plan;

[0043] An energy supply control module, used for controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected energy configuration scheme;

[0044] The efficiency evaluation and correction module is used to collect the operating data of the photovoltaic energy storage system in real time, evaluate the efficiency level of energy storage, and correct the energy distribution plan based on the evaluation results;

[0045] The test and solution determination module is used to perform energy supply tests on the photovoltaic energy storage system through each energy configuration solution in the revised energy allocation plan, and select the energy configuration solution with the highest priority weight as the final energy supply execution solution.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting environmental data and historical power generation data of the photovoltaic energy storage system, and combining with the meteorological forecast data provided by the network meteorological service platform, the technical solution can make full use of multi-dimensional data resources to build an accurate photovoltaic power generation prediction model and a power load demand prediction model. This method of data fusion and deep feature extraction significantly improves the accuracy of the prediction, makes the energy allocation plan closer to reality, and reduces the problem of low energy management efficiency caused by prediction errors. Secondly, through time series division and setting of preset conditions, energy allocation plans for different time periods and different conditions can be generated to ensure the diversity and practicality of the plans. At the same time, the generated plans are verified and adjusted to ensure that the plans meet the requirements of safe and stable operation of the power grid, and the charging and discharging efficiency and life management of the photovoltaic energy storage system are considered, which further improves the efficiency of energy management. The operation data of the photovoltaic energy storage system is collected in real time by sensors, and the data is processed and analyzed by data analysis technology, which can accurately evaluate the efficiency level of the energy storage unit, and modify the energy allocation plan according to the evaluation results. This dynamic adjustment and optimization process makes the energy allocation plan more in line with the actual situation and improves the flexibility and efficiency of energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying any creative work.

[0048] Figure 1 A step diagram of a photovoltaic energy storage deployment method;

[0049] Figure 2 This is a structural diagram of a photovoltaic energy storage deployment system. DETAILED DESCRIPTION

[0050] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0051] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0052] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0053] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0054] Example

[0055] A photovoltaic energy storage deployment method, such as Figure 1 As shown, the following steps are included:

[0056] Obtain environmental data and historical power generation data of the photovoltaic energy storage system;

[0057] Through the established online meteorological service platform, weather forecast data for different time periods can be obtained; including but not limited to light intensity, wind speed, precipitation probability, etc. These data are crucial for the prediction of photovoltaic power generation.

[0058] Based on the acquired data, train the photovoltaic power generation prediction model and the power load demand prediction model;

[0059] Generate energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand;

[0060] Select the best energy allocation plan from the energy allocation plan, and control the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected plan;

[0061] Collect the operating data of the energy storage unit in real time, evaluate the efficiency level of the energy storage unit, and revise the energy allocation plan based on the evaluation results;

[0062] The photovoltaic energy storage system is tested for energy supply through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution plan.

[0063] The training of the photovoltaic power generation prediction model and the power load demand prediction model based on the acquired data further includes:

[0064] Obtain multi-dimensional data samples that integrate weather forecast data, environmental data of photovoltaic energy storage systems, and historical power generation data;

[0065] Performing deep feature extraction on the multidimensional data sample to generate a first feature vector representing meteorological variation and a second feature vector describing the performance of the photovoltaic energy storage system;

[0066] Deeply encoding the first feature vector and the second feature vector respectively to obtain a first fine-grained feature vector related to meteorological parameters and a second fine-grained feature vector related to equipment performance;

[0067] Dynamically integrating the first fine-grained feature vector and the second fine-grained feature vector to generate a comprehensive target feature vector, and calculating a dynamic alignment loss of a photovoltaic power generation prediction model based on the target feature vector;

[0068] Based on the initial loss and dynamic alignment loss of the photovoltaic power generation prediction model, the model parameters of the photovoltaic power generation prediction model are dynamically adjusted and optimized.

[0069] Choose appropriate machine learning algorithms such as support vector machines, time series analysis, random forests, neural networks, etc. as the basis of the prediction model.

[0070] The generation of energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand also includes:

[0071] The predicted photovoltaic power generation and power load demand data are divided according to the time series to form a plurality of continuous time period sets, each of which contains the photovoltaic power generation prediction value and the power load demand prediction value in the corresponding time period;

[0072] For each time period set, different energy allocation strategies are set according to preset conditions, such as energy costs, environmental constraints, system stability requirements, etc. These strategies include but are not limited to giving priority to photovoltaic power generation, enabling photovoltaic energy storage systems to supplement power, and calling on backup power supplies.

[0073] Based on the set energy allocation strategy, combined with the predicted value of photovoltaic power generation and the predicted value of power load demand, the allocation ratio and specific value of each energy type in each time period are calculated to generate a preliminary energy allocation plan;

[0074] The generated preliminary energy allocation plan is verified to ensure that it meets the requirements for safe and stable operation of the power grid. At the same time, the charging and discharging efficiency and life management of the photovoltaic energy storage system are considered, and necessary adjustments and optimizations are made to the plan.

[0075] The step of selecting the optimal energy allocation scheme from the energy allocation plan and controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected plan also includes:

[0076] Comprehensively evaluate the generated multiple energy allocation plans based on preset evaluation indicators, such as lowest energy cost, lowest environmental impact, highest system efficiency, etc.;

[0077] Select the energy configuration plan with the best comprehensive evaluation as the optimal plan for the current time period and preset conditions;

[0078] Based on the selected optimal solution, the energy supply management of the energy storage unit of the photovoltaic energy storage system is carried out, including setting the charging and discharging strategy of the photovoltaic energy storage system, controlling the input and output power of the photovoltaic energy storage system, etc.

[0079] The real-time collection of the operating data of the energy storage unit, the evaluation of the efficiency level of the energy storage unit, and the modification of the energy allocation plan according to the evaluation results also include:

[0080] The operating data of the photovoltaic energy storage system is collected in real time through sensors, including key parameters such as power, temperature, voltage, and current of the photovoltaic energy storage system.

[0081] Data analysis technology is used to process and analyze the collected operating data to evaluate the efficiency level of the photovoltaic energy storage system, including energy storage efficiency, cycle life, etc.

[0082] According to the evaluation results, the energy allocation plan is revised, such as adjusting the charging and discharging strategy of the photovoltaic energy storage system, optimizing the energy allocation ratio, etc., to improve the efficiency of the photovoltaic energy storage system and extend its service life.

[0083] The energy supply test of the photovoltaic energy storage system is performed respectively through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution scheme, which also includes:

[0084] Apply each energy configuration scheme in the revised energy allocation plan to the photovoltaic energy storage system for energy supply testing;

[0085] During the test, the operating status and performance indicators of the photovoltaic energy storage system are monitored in real time, such as system stability and energy utilization efficiency.

[0086] According to the monitoring results, the energy configuration plan with the highest priority weight is selected as the final energy supply implementation plan. The priority weight can be determined based on multiple factors, such as energy cost, environmental impact, system efficiency, etc.

[0087] In the specific implementation, first, we use the sensors and network of the photovoltaic energy storage system itself to collect environmental data, such as temperature, humidity, light intensity, etc. and historical power generation data, such as power generation and power generation efficiency in the past period of time. At the same time, we use the established network meteorological service platform to obtain weather forecast data for the future period of time, including but not limited to sunshine time, cloud cover, wind speed, etc.

[0088] Next, the collected environmental data, historical power generation data and weather forecast data are used to train the photovoltaic power generation prediction model and the power load demand prediction model. This step includes obtaining multidimensional data samples that integrate weather forecast data and photovoltaic energy storage system data, performing deep feature extraction, and generating feature vectors that characterize meteorological variation and photovoltaic energy storage system performance. These feature vectors are then deep encoded to obtain fine-grained feature vectors. These fine-grained feature vectors are dynamically integrated to generate a comprehensive target feature vector, which is used to calculate the dynamic alignment loss of the photovoltaic power generation prediction model, thereby dynamically adjusting and optimizing the model parameters.

[0089] Based on the trained prediction model, we predict the photovoltaic power generation and power load demand in the future. Then, we divide the prediction data into multiple continuous time period sets according to the time series. Each set contains the predicted value of photovoltaic power generation and power load demand in the corresponding time period. For each time period set, we set different energy allocation strategies according to preset conditions, such as the safe and stable operation requirements of the power grid, the charging and discharging efficiency and life management of the photovoltaic energy storage system, as well as energy costs, environmental constraints, and system stability requirements. We also calculate the allocation ratio and specific values ​​of each energy type in each time period to generate a preliminary energy allocation plan. We verify and adjust the generated plan to ensure that it meets all preset conditions.

[0090] Then, based on preset evaluation indicators, such as lowest energy cost, smallest environmental impact, highest system efficiency, etc., we conduct a comprehensive evaluation of the multiple energy allocation plans generated, and select the energy configuration plan with the best comprehensive evaluation as the optimal plan for the current time period and preset conditions. Based on the selected optimal plan, we manage the energy supply of the energy storage unit of the photovoltaic energy storage system.

[0091] During the energy supply process, we use sensors to collect real-time operating data of the photovoltaic energy storage system, such as the power, temperature, and voltage of the energy storage unit. We use data analysis technology to process and analyze these data to evaluate the efficiency level of the photovoltaic energy storage system. Based on the evaluation results, we dynamically adjust the energy allocation plan to optimize the system's operating efficiency and stability.

[0092] Finally, each energy configuration scheme in the revised energy allocation plan is applied to the photovoltaic energy storage system for energy supply testing. During the test, we monitor the system's operating status and performance indicators in real time, such as power generation efficiency, energy storage efficiency, system stability, etc. According to the monitoring results, we select the energy configuration scheme with the highest priority weight as the final energy supply execution plan to achieve the optimal allocation of the photovoltaic energy storage system.

[0093] A computer-readable storage medium is also provided, the computer-readable storage medium includes a photovoltaic energy storage allocation method program, and when the photovoltaic energy storage allocation method program is executed by a processor, the steps of the photovoltaic energy storage allocation method as described above are implemented.

[0094] A photovoltaic energy storage dispatching system, such as Figure 2 As shown, including:

[0095] Data acquisition module, used to collect environmental data and historical power generation data of the photovoltaic energy storage system;

[0096] The meteorological data acquisition module obtains meteorological forecast data for different time periods in real time or regularly through the established online meteorological service platform;

[0097] The prediction model module trains the photovoltaic power generation prediction model and the power load demand prediction model based on the collected environmental data, historical power generation data and weather forecast data;

[0098] A plan generation module is used to generate energy allocation plans for different time periods and under different conditions based on the predicted photovoltaic power generation and power load demand and in combination with different preset conditions;

[0099] A configuration scheme selection module is used to select the optimal energy configuration scheme from the energy allocation plan;

[0100] An energy supply control module, used for controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected energy configuration scheme;

[0101] The efficiency evaluation and correction module is used to collect the operating data of the photovoltaic energy storage system in real time, evaluate the efficiency level of energy storage, and correct the energy distribution plan based on the evaluation results;

[0102] The test and solution determination module is used to perform energy supply tests on the photovoltaic energy storage system through each energy configuration solution in the revised energy allocation plan, and select the energy configuration solution with the highest priority weight as the final energy supply execution solution.

[0103] In the specific implementation, a photovoltaic energy storage dispatching system is constructed, which consists of a data acquisition module, a meteorological data acquisition module, a prediction model module, a plan generation module, a configuration scheme selection module, an energy supply control module, an efficiency evaluation and correction module, and a test and scheme determination module. The system is installed in the control center of the photovoltaic energy storage system to ensure smooth data interaction and command transmission among all modules.

[0104] After the system is built, initial settings are performed, including setting the frequency of data collection, the scheduled tasks of the meteorological data acquisition module, the training parameters of the prediction model, etc. At the same time, energy allocation plan templates under different conditions are defined and preset to provide a basis for the subsequent automatic generation of plans.

[0105] The data acquisition module regularly collects environmental data such as temperature, humidity, light intensity, etc. and historical power generation data such as power generation and power generation efficiency from the sensor network of the photovoltaic energy storage system. These data will be stored in the system's database for subsequent analysis and prediction.

[0106] The meteorological data acquisition module obtains the weather forecast data for the next week on a regular basis every day through the established online meteorological service platform, including light intensity, temperature, wind speed, etc. These data are also stored in the database and used together with environmental data and historical power generation data to train the prediction model.

[0107] The prediction model module uses the collected environmental data, historical power generation data and weather forecast data, and adopts machine learning algorithms such as time series analysis and neural networks to train photovoltaic power generation prediction models and power load demand prediction models. These models can predict photovoltaic power generation and power load demand in the future. In order to improve the accuracy of the prediction, the model needs to be updated and retrained regularly. Whenever new data is collected and stored, the system will automatically trigger the model update process.

[0108] The plan generation module automatically generates energy allocation plans for different time periods and under different conditions based on the predicted photovoltaic power generation and power load demand, combined with different preset conditions such as energy prices, grid stability, environmental protection policies, etc.

[0109] The configuration scheme selection module selects the optimal energy configuration scheme from the generated energy allocation plans. This process involves the weighing of multiple factors, such as cost-effectiveness, environmental impact, energy utilization, etc. The system can sort the schemes according to preset priority weights and select the optimal one.

[0110] The energy supply control module controls the energy supply of the energy storage unit of the photovoltaic energy storage system according to the selected energy configuration scheme, which includes adjusting the power generation power of the photovoltaic panels and the charging and discharging strategies of the energy storage batteries.

[0111] The efficiency evaluation and correction module collects the operating data of the photovoltaic energy storage system in real time and evaluates the efficiency level of energy storage, such as energy storage efficiency, energy loss, etc. If the efficiency level is found to be lower than expected, the system will revise the energy allocation plan to improve the efficiency of energy storage.

[0112] The test and solution determination module conducts energy supply tests on the photovoltaic energy storage system through each energy configuration scheme in the revised energy allocation plan. These tests are aimed at verifying the feasibility and effectiveness of the plan.

[0113] After completing the test, the system selects the energy configuration plan with the highest priority weight as the final energy supply execution plan based on the test results. This plan will be passed to the energy supply control module to guide the actual operation of the photovoltaic energy storage system.

[0114] The same or similar reference numerals correspond to the same or similar components;

[0115] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A photovoltaic energy storage deployment method, characterized in that: The following steps are involved: Obtain environmental data and historical power generation data of the photovoltaic energy storage system; Obtain weather forecast data for different time periods through the established online weather service platform; Based on the acquired data, train the photovoltaic power generation prediction model and the power load demand prediction model; Generate energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand; Select the best energy allocation plan from the energy allocation plan, and control the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected plan; Collect the operating data of the energy storage unit in real time, evaluate the efficiency level of the energy storage unit, and revise the energy allocation plan based on the evaluation results; The photovoltaic energy storage system is tested for energy supply through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution plan.

2. A photovoltaic energy storage deployment method according to claim 1, characterized in that: The training of the photovoltaic power generation prediction model and the power load demand prediction model based on the acquired data further includes: Obtain multi-dimensional data samples that integrate weather forecast data, environmental data of photovoltaic energy storage systems, and historical power generation data; Performing deep feature extraction on the multidimensional data sample to generate a first feature vector representing meteorological variation and a second feature vector describing the performance of the photovoltaic energy storage system; Deeply encoding the first feature vector and the second feature vector respectively to obtain a first fine-grained feature vector related to meteorological parameters and a second fine-grained feature vector related to equipment performance; Dynamically integrating the first fine-grained feature vector and the second fine-grained feature vector to generate a comprehensive target feature vector, and calculating a dynamic alignment loss of a photovoltaic power generation prediction model based on the target feature vector; Based on the initial loss and dynamic alignment loss of the photovoltaic power generation prediction model, the model parameters of the photovoltaic power generation prediction model are dynamically adjusted and optimized.

3. A photovoltaic energy storage deployment method according to claim 1, characterized in that: The generation of energy allocation plans for different time periods and under different preset conditions based on the predicted photovoltaic power generation and power load demand also includes: The predicted photovoltaic power generation and power load demand data are divided according to the time series to form a plurality of continuous time period sets, each of which contains the photovoltaic power generation prediction value and the power load demand prediction value in the corresponding time period; For each time period set, different energy allocation strategies are set according to preset conditions; Based on the set energy allocation strategy, combined with the predicted value of photovoltaic power generation and the predicted value of power load demand, the allocation ratio and specific value of each energy type in each time period are calculated to generate a preliminary energy allocation plan; The generated preliminary energy allocation plan is verified to ensure that it meets the requirements for safe and stable operation of the power grid. At the same time, the charging and discharging efficiency and life management of the photovoltaic energy storage system are considered, and necessary adjustments and optimizations are made to the plan.

4. A photovoltaic energy storage deployment method according to claim 1, characterized in that: The step of selecting the optimal energy allocation scheme from the energy allocation plan and controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected plan also includes: According to the preset evaluation indicators, a comprehensive evaluation is conducted on the generated multiple energy allocation plans; Select the energy configuration plan with the best comprehensive evaluation as the optimal plan for the current time period and preset conditions; Based on the selected optimal solution, the energy supply of the energy storage unit of the photovoltaic energy storage system is managed.

5. A photovoltaic energy storage deployment method according to claim 1, characterized in that: The real-time collection of the operating data of the energy storage unit, the evaluation of the efficiency level of the energy storage unit, and the modification of the energy allocation plan according to the evaluation results also include: Collect the operation data of the photovoltaic energy storage system in real time through sensors; Use data analysis technology to process and analyze the collected operating data to evaluate the efficiency level of the photovoltaic energy storage system; Based on the evaluation results, the energy allocation plan is revised.

6. A photovoltaic energy storage deployment method according to claim 1, characterized in that: The energy supply test of the photovoltaic energy storage system is performed respectively through each energy configuration scheme in the revised energy allocation plan, and the energy configuration scheme with the highest priority weight is selected as the final energy supply execution scheme, which also includes: Apply each energy configuration scheme in the revised energy allocation plan to the photovoltaic energy storage system for energy supply testing; During the test, the operating status and performance indicators of the photovoltaic energy storage system are monitored in real time; According to the monitoring results, the energy configuration plan with the highest priority weight is selected as the final energy supply execution plan.

7. A photovoltaic energy storage allocation system, used to implement a photovoltaic energy storage allocation method as claimed in any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to collect environmental data and historical power generation data of the photovoltaic energy storage system; The meteorological data acquisition module obtains meteorological forecast data for different time periods in real time or regularly through the established online meteorological service platform; The prediction model module trains the photovoltaic power generation prediction model and the power load demand prediction model based on the collected environmental data, historical power generation data and weather forecast data; A plan generation module is used to generate energy allocation plans for different time periods and under different conditions based on the predicted photovoltaic power generation and power load demand and in combination with different preset conditions; A configuration scheme selection module is used to select the optimal energy configuration scheme from the energy allocation plan; An energy supply control module, used for controlling the energy supply of the energy storage unit of the photovoltaic energy storage system based on the selected energy configuration scheme; The efficiency evaluation and correction module is used to collect the operating data of the photovoltaic energy storage system in real time, evaluate the efficiency level of energy storage, and correct the energy distribution plan based on the evaluation results; The test and solution determination module is used to perform energy supply tests on the photovoltaic energy storage system through each energy configuration solution in the revised energy allocation plan, and select the energy configuration solution with the highest priority weight as the final energy supply execution solution.

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