A power distribution system for improving the efficiency of photovoltaic energy storage and charging

Abstract

The present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging, which relates to the technical field of data processing and includes: a data acquisition module: monitoring the operating status of the target photovoltaic power distribution system in real time to obtain key equipment operating parameter data; an abnormality processing and prediction module: performing operating status abnormality detection and processing on the target photovoltaic power distribution system based on the key equipment operating parameter data, and predicting the future operating status of the power distribution system; a scheduling analysis module: generating a target scheduling strategy based on the prediction result of the future operating status of the power distribution system and dynamically adjusting the energy configuration. By analyzing the key equipment operating parameter data obtained from monitoring the operating status of the target photovoltaic power distribution system in real time, accurate detection of abnormal operating conditions and effective prediction of the future operating status of the power distribution system are achieved, thereby generating a target scheduling strategy and dynamically adjusting the energy configuration, effectively improving the efficiency of photovoltaic energy storage and charging and reducing the operating cost.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a power distribution system for improving the efficiency of photovoltaic energy storage and charging. Background Art

[0002] Developing new energy supply methods that are efficient, environmentally friendly, and renewable has become the focus of global attention. The integrated photovoltaic energy storage and charging power distribution system was born based on this background. It organically combines solar power generation, energy storage technology, and charging technology. However, due to the vulnerability of the integrated photovoltaic energy storage and charging power distribution system to external factors, system safety problems occur frequently, and the power distribution efficiency of the system is low. Therefore, how to improve the efficiency of photovoltaic energy storage and charging has become one of the current research priorities.

[0003] Therefore, the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging. Summary of the Invention

[0004] The present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging, which is used to analyze the key equipment operation parameter data obtained by real-time monitoring of the operation state of the target photovoltaic power distribution system, realize accurate detection of abnormal operation conditions and effective prediction of the future operation state of the power distribution system, thereby generating a target scheduling strategy, dynamically adjusting the energy configuration, effectively improving the efficiency of photovoltaic energy storage and charging, and reducing the operation cost.

[0005] The present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging, including:

[0006] A data acquisition module: used to real-time monitor the operation state of the target photovoltaic power distribution system to obtain key equipment operation parameter data;

[0007] An abnormality processing and prediction module: used to perform operation state abnormality detection and processing on the target photovoltaic power distribution system based on the key equipment operation parameter data, and predict the future operation state of the power distribution system;

[0008] A scheduling analysis module: used to generate a target scheduling strategy based on the prediction result of the future operation state of the power distribution system and dynamically adjust the energy configuration.

[0009] Preferably, the data acquisition module includes:

[0010] Obtain the key equipment operation parameters of each power distribution subsystem in the target photovoltaic power distribution system;

[0011] Determine the specified monitoring tool for each power distribution subsystem according to the key equipment operation parameters;

[0012] Use the specified monitoring tool to real-time monitor the operation state of the key equipment of each power distribution subsystem to obtain key equipment operation parameter data.

[0013] Preferably, the power distribution subsystem includes a power generation subsystem, an energy storage subsystem, and a charging subsystem.

[0014] Preferably, the anomaly handling and prediction module includes:

[0015] The data analysis unit: When the operating parameter data of the key equipment in the power distribution subsystem is greater than the corresponding set high threshold of the operating parameter or less than the corresponding set low threshold of the operating parameter, mark the current operating parameter data of the key equipment as the first data;

[0016] Calibrate the operating parameter of the key equipment corresponding to the first data as an abnormal parameter;

[0017] Calculate the absolute difference in operating parameters between the first data and the corresponding set high threshold of the operating parameter and the set low threshold of the operating parameter respectively, and regard the smaller absolute difference in operating parameters as the first parameter difference;

[0018] When there is no operating parameter data of the key equipment in the power distribution subsystem that is greater than the corresponding set high threshold of the operating parameter or less than the corresponding set low threshold of the operating parameter, mark all the operating parameter data of the key equipment as the second data;

[0019] Calibrate the operating parameter of the key equipment corresponding to the second data as a normal parameter;

[0020] Calculate the absolute difference in operating parameters between the second data and the corresponding set high threshold of the operating parameter and the set low threshold of the operating parameter respectively, and regard the smaller absolute difference in operating parameters as the second parameter difference;

[0021] The anomaly analysis unit: When there are abnormal parameters in the power distribution subsystem, determine the first parameter adjustment strategy from the extracted list of anomaly adjustment strategies that matches the current power distribution subsystem to perform anomaly handling on the abnormal parameters;

[0022] The status prediction unit: When there are no abnormal parameters in the power distribution subsystem, extract and analyze the historical equipment operating parameter data of the key equipment operating parameters within a preset time period to achieve the status prediction of the current operating status of the power distribution subsystem and obtain the status prediction result.

[0023] Preferably, the anomaly analysis unit includes:

[0024] Extract the set parameter adjustment strategy for handling the abnormal parameters from the list of anomaly adjustment strategies;

[0025] If there are adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter, calculate the adjustment influence coefficient based on the first parameter difference corresponding to the first data;

[0026] After adjusting the adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter by using the adjustment influence coefficient, a first parameter adjustment strategy is generated;

[0027] If there are no adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter, the obtained set parameter adjustment strategy is regarded as the first parameter adjustment strategy;

[0028] Use the first parameter adjustment strategy to perform abnormal maintenance on the abnormal parameters of the current power distribution subsystem, and generate an abnormal maintenance report.

[0029] Preferably, the calculation formula of the adjustment influence coefficient is as follows:

[0030] ; where T represents the adjustment influence coefficient; represents the first parameter difference corresponding to the current first data; represents the set operating parameter threshold for obtaining the first parameter difference by comparing with the current first data; represents the first data; represents the weight of the key equipment operating parameters to which the current first data belongs on the performance of the power distribution subsystem; represents the data loss compensation factor.

[0031] Preferably, the state prediction unit includes:

[0032] Extract the historical key equipment operating parameter data of the key equipment within a preset time period from the corresponding power distribution-equipment database of the power distribution subsystem;

[0033] After performing data cleaning and normalization processing on the obtained historical key equipment operating parameter data, historical operating data is obtained;

[0034] Use the historical operating data as training data to train a neural network to obtain the first state prediction model of the current power distribution subsystem;

[0035] Based on the set association screening principle, screen out the scheduling association parameters from the key equipment operating parameters, and regard the remaining parameters as non-scheduling association parameters;

[0036] Extract the scheduling association parameter data from the key equipment operating parameter data of the power distribution subsystem and input it into the first state prediction model to obtain the predicted state result.

[0037] Preferably, the scheduling analysis module includes:

[0038] Prediction analysis unit: used to analyze the corresponding prediction data of non-scheduled associated parameters by using a non-scheduled prediction analysis block; analyze the corresponding prediction data of scheduled associated parameters by using a scheduled prediction analysis block; match the dispatching strategy of the distribution sub-system by using a policy matching block;

[0039] Non-scheduled prediction analysis block: used to extract the non-scheduled prediction data of non-scheduled associated parameters from the prediction results and compare and analyze them with the corresponding set operating parameter thresholds;

[0040] If there is non-scheduled prediction data of non-scheduled associated parameters greater than the corresponding set high threshold of operating parameters or less than the set low threshold of operating parameters, mark the current non-scheduled associated parameter as a pre-abnormal non-scheduled associated parameter;

[0041] Otherwise, mark the current non-scheduled associated parameter as a pre-normal non-scheduled associated parameter;

[0042] Scheduled prediction analysis block: used to compare and analyze the scheduled prediction data of each scheduled associated parameter with the set demand threshold;

[0043] If there is scheduled prediction data of a scheduled associated parameter not greater than the corresponding set high demand threshold and not less than the set low demand threshold, mark the current scheduled associated parameter as a pre-normal scheduled associated parameter;

[0044] If there is scheduled prediction data of a scheduled associated parameter greater than the corresponding set high demand threshold, mark the current scheduled associated parameter as a high-abnormal scheduled associated parameter and output it as the scheduled associated prediction result;

[0045] Calculate the absolute difference between the scheduled prediction data of the high-abnormal scheduled associated parameter and the corresponding set high demand threshold to obtain the first high demand absolute difference and output it as the scheduled associated prediction result;

[0046] If there is scheduled prediction data of a scheduled associated parameter less than the corresponding set low demand threshold, mark the current scheduled associated parameter as a low-abnormal scheduled associated parameter and output it as the scheduled associated prediction result;

[0047] Calculate the absolute difference between the scheduled prediction data of the low-abnormal scheduled associated parameter and the corresponding set low demand threshold to obtain the first low demand absolute difference and output it as the scheduled associated prediction result;

[0048] Policy matching block: used to calculate the scheduled associated demand coefficient based on the scheduled associated prediction result;

[0049] Determine the scheduled associated demand level of the distribution sub-system according to the scheduled associated demand coefficient;

[0050] According to the described scheduling association requirement level, match the corresponding energy scheduling plan from the demand level - scheduling plan mapping table as the first scheduling strategy output of the distribution subsystem;

[0051] Policy analysis unit: used to combine and calculate and analyze the prediction status result of the non - scheduling associated parameter generated by the non - scheduling prediction analysis block with the set non - scheduling - scheduling association influence coefficient to obtain the first optimization coefficient;

[0052] Analyze the association degree between the current distribution subsystem and other distribution subsystems according to the preset system association evaluation rule to obtain the first association coefficient;

[0053] Perform a weighted average calculation on the first association coefficient and the first optimization coefficient to obtain the target adjustment coefficient;

[0054] Use the target adjustment coefficient to adjust the adjustable parameters in the first scheduling strategy of the current distribution subsystem to obtain the adjusted parameter values;

[0055] Replace the parameter values of the adjustable parameters in the current first scheduling strategy with the adjusted parameter values, and then output them as the target scheduling strategy.

[0056] Compared with the prior art, the beneficial effects of the present application are as follows:

[0057] By analyzing the key equipment operation parameter data obtained from real - time monitoring of the operation status of the target photovoltaic power distribution system, accurate detection of abnormal operation conditions and effective prediction of the future operation status of the power distribution system are realized, so as to generate the target scheduling strategy, dynamically adjust the energy configuration, effectively improve the efficiency of photovoltaic energy storage and charging, and reduce the operation cost.

[0058] Other features and advantages of the present invention will be described in the following description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written description and the drawings.

[0059] The following will further describe the technical solutions of the present invention in detail through the drawings and embodiments. Description of the Drawings

[0060] The drawings are used to provide further understanding of the present invention, and constitute a part of the description. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0061] Figure 1 It is a structural diagram of a power distribution system for improving the efficiency of photovoltaic energy storage and charging in an embodiment of the present invention. Detailed Embodiments

[0062] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0063] An embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging, as Figure 1 shown, including:

[0064] A data acquisition module: used to monitor the operating status of the target photovoltaic power distribution system in real time and obtain key equipment operating parameter data;

[0065] An anomaly processing and prediction module: used to detect and process the abnormal operating status of the target photovoltaic power distribution system based on the key equipment operating parameter data, and predict the future operating status of the power distribution system;

[0066] A scheduling analysis module: used to generate a target scheduling strategy based on the prediction result of the future operating status of the power distribution system and dynamically adjust the energy configuration.

[0067] In this embodiment, the photovoltaic power distribution system is an integrated photovoltaic energy storage and charging power distribution system composed of a power generation subsystem, an energy storage subsystem, and a charging subsystem. Among them, the power generation subsystem converts solar energy into electrical energy through solar panels and directly provides green and renewable energy for the system. The energy storage subsystem stores the excess electrical energy using energy storage batteries and releases it when needed. The charging subsystem provides fast and convenient charging services.

[0068] In this embodiment, the key equipment refers to the equipment in each power distribution subsystem whose equipment importance coefficient is greater than the set importance threshold. For example, the key equipment of the power generation subsystem includes photovoltaic inverters and photovoltaic modules. The key equipment of the energy storage subsystem includes energy storage batteries and bidirectional energy storage converters. The key equipment of the charging subsystem includes charging controllers. The equipment importance coefficient is a coefficient obtained by combining the evaluation of the equipment from three evaluation directions of functional importance, economic benefits, and risk occurrence frequency based on a preset evaluation standard, and then combining with the comprehensive weights of each evaluation direction determined by using the analytic hierarchy process to calculate the subjective weight and the entropy weight method to calculate the objective weight, and is used to quantify the importance of the equipment to the power distribution subsystem. The set importance threshold is preset.

[0069] In this embodiment, the key device operation parameters refer to the parameters used to describe and reflect the key information such as the state, performance, and efficiency of the key device during operation. For example, the key device operation parameters of the key device, i.e., the photovoltaic inverter device, in the power generation subsystem include the rated output power, response time, output voltage regulation rate, load regulation rate, etc. The key device operation parameters of the photovoltaic module include the short-circuit current, open-circuit voltage, peak power, etc. The key device operation parameters of the key device, i.e., the energy storage battery, in the energy storage subsystem include the rated voltage, charge-discharge efficiency, etc. The key device operation parameters of the bidirectional energy storage converter include the conversion efficiency, response time, voltage, etc. The key device operation parameters of the key device, i.e., the charging controller, in the charging subsystem include the rated voltage, rated current, etc.

[0070] In this embodiment, the target scheduling strategy is used to adjust the charge-discharge strategy, realize the intelligent interaction and collaborative work among photovoltaic power generation, energy storage, and charging, and improve the energy utilization efficiency and system stability.

[0071] The beneficial effects of the above technical solution are as follows: By analyzing the key device operation parameter data obtained from the real-time monitoring of the operation state of the target photovoltaic power distribution system, accurate detection of abnormal operation conditions and effective prediction of the future operation state of the power distribution system are realized, so as to generate the target scheduling strategy, dynamically adjust the energy configuration, effectively improve the efficiency of photovoltaic energy storage and charging, and reduce the operation cost.

[0072] An embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging. The data acquisition module includes:

[0073] Obtain the key device operation parameters of each power distribution subsystem in the target photovoltaic power distribution system;

[0074] Determine the specified monitoring tool for each power distribution subsystem according to the key device operation parameters;

[0075] Use the specified monitoring tool to monitor the operation state of the key devices of each power distribution subsystem in real time, and obtain the key device operation parameter data.

[0076] In this embodiment, the photovoltaic power distribution system is an integrated photovoltaic energy storage and charging power distribution system composed of a power generation subsystem, an energy storage subsystem, and a charging subsystem; the power distribution subsystem includes a power generation subsystem, an energy storage subsystem, and a charging subsystem.

[0077] In this embodiment, the key device refers to a device in each power distribution subsystem whose device importance coefficient is greater than a set importance threshold. For example, the key devices in the power generation subsystem are photovoltaic inverters and photovoltaic modules, the key devices in the energy storage subsystem are energy storage batteries and bidirectional energy storage converters, and the key device in the charging subsystem is a charging controller; the device importance coefficient refers to a coefficient obtained by evaluating the device based on a preset evaluation criterion from three evaluation directions of functional importance, economic benefits, and risk occurrence frequency, and then combining it with the comprehensive weight of each evaluation direction determined by using the analytic hierarchy process to calculate the subjective weight and the entropy weight method to calculate the objective weight, which is used to quantify the importance of the device to the power distribution subsystem; the set importance threshold is preset.

[0078] In this embodiment, the key device operation parameters refer to the parameters used to describe and reflect the key information such as the state, performance, and efficiency of the key device during operation. For example, the key device operation parameters of the key device photovoltaic inverter in the power generation subsystem include rated output power, response time, output voltage adjustment rate, load adjustment rate, etc., and the key device operation parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key device operation parameters of the key device energy storage battery in the energy storage subsystem include rated voltage, charge and discharge efficiency, etc., and the key device operation parameters of the bidirectional energy storage converter include conversion efficiency, response time, voltage, etc.; the key device operation parameters of the key device charging controller in the charging subsystem include rated voltage, rated current, etc.; the specified monitoring tool is predetermined and used to monitor the operation status of the key devices in the power distribution subsystem.

[0079] The beneficial effects of the above technical solution are: By installing a specified monitoring tool on the key devices of the power distribution subsystem to monitor the device operation and collecting the device operation data in real time, it can lay an effective data support for subsequent abnormal state detection and future state prediction.

[0080] The embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging. The anomaly processing and prediction module includes:

[0081] Data analysis unit: When the key device operation parameter data in the power distribution subsystem is greater than the corresponding set operation parameter high threshold or less than the corresponding set operation parameter low threshold, mark the current key device operation parameter data as the first data;

[0082] Calibrate the key device operation parameter corresponding to the first data as an abnormal parameter;

[0083] Calculate the absolute operation parameter differences between the first data and the corresponding set operation parameter high threshold and set operation parameter low threshold respectively, and regard the smaller absolute operation parameter difference as the first parameter difference;

[0084] When there is no key device operation parameter data in the power distribution subsystem that is greater than the corresponding set operation parameter high threshold or less than the corresponding set operation parameter low threshold, mark all key device operation parameter data as second data;

[0085] Calibrate the key device operation parameters corresponding to the second data as normal parameters;

[0086] Calculate the absolute operation parameter differences between the second data and the corresponding set operation parameter high threshold and set operation parameter low threshold respectively, and regard the smaller absolute operation parameter difference as the second parameter difference;

[0087] Abnormality analysis unit: When there are abnormal parameters in the power distribution subsystem, determine the first parameter adjustment strategy from the extracted list of abnormal adjustment strategies that match the current power distribution subsystem to perform abnormality processing on the abnormal parameters;

[0088] Status prediction unit: When there are no abnormal parameters in the power distribution subsystem, extract and analyze the historical device operation parameter data of the key device operation parameters within a preset time period to achieve the status prediction of the current power distribution subsystem operation state and obtain the status prediction result.

[0089] In this embodiment, the set operation parameter high threshold is preset; the set operation parameter low threshold is preset; the first data refers to the key device operation parameter data that is greater than the corresponding set operation parameter high threshold or less than the corresponding set operation parameter low threshold; the abnormal parameter refers to the key device operation parameter corresponding to the first data; the absolute operation parameter difference refers to the absolute data difference between the first data and the set operation parameter high threshold or the set operation parameter low threshold.

[0090] In this embodiment, the list of abnormal adjustment strategies is a list composed of set parameter adjustment strategies set for each key device operation parameter in the power distribution subsystem; the key device operation parameter refers to the parameter used to describe and reflect the key information such as the state, performance, and efficiency of the key device during operation. For example, the key device operation parameters of the key device photovoltaic inverter device in the power generation subsystem include rated output power, response time, output voltage adjustment rate, load adjustment rate, etc., and the key device operation parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key device operation parameters of the key device energy storage battery in the energy storage subsystem include rated voltage, charge and discharge efficiency, etc., and the key device operation parameters of the bidirectional energy storage converter include conversion efficiency, response time, voltage, etc.; the key device operation parameters of the key device charging controller in the charging subsystem include rated voltage, rated current, etc.

[0091] In this embodiment, the first parameter adjustment strategy is used to adjust abnormal parameters, where the abnormal parameters refer to the key equipment operation parameters whose parameter data is greater than the corresponding set operation parameter high threshold or less than the corresponding set operation parameter low threshold.

[0092] In this embodiment, for example, the key equipment operation parameter rated output power of the key equipment photovoltaic inverter a1 in the power generation subsystem is 150 kW, and the output voltage adjustment rate is ; among them, 150 KW is less than the corresponding set operation parameter low threshold, is greater than the corresponding set operation parameter high threshold; at this time, 150 kW, are all marked as the first data, and the rated output power and output voltage adjustment rate of the photovoltaic inverter a1 are calibrated as abnormal parameters.

[0093] In this embodiment, the preset time period is preset in advance; the historical equipment operation parameter data refers to the historical parameter data of the key equipment operation parameters of the power distribution subsystem within the preset time period.

[0094] The beneficial effects of the above technical solution are: by performing abnormal detection on the power distribution subsystem, problems can be discovered and processed in a timely manner, the future state of the power distribution subsystem can be predicted, data support can be provided for optimizing the dispatching strategy, and the efficiency of the photovoltaic energy storage charging system can be effectively improved while ensuring the system security.

[0095] An embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage charging. The abnormal analysis unit includes:

[0096] Extract the set parameter adjustment strategy for processing the abnormal parameters from the abnormal adjustment strategy list;

[0097] If there are adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter, calculate the adjustment influence coefficient based on the first parameter difference corresponding to the first data;

[0098] After adjusting the adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter by using the adjustment influence coefficient, generate the first parameter adjustment strategy;

[0099] If there are no adjustable parameters in the set parameter adjustment strategy of the current abnormal parameter, regard the obtained set parameter adjustment strategy as the first parameter adjustment strategy;

[0100] Use the first parameter adjustment strategy to perform abnormal maintenance on the abnormal parameters of the current power distribution subsystem and generate an abnormal maintenance report.

[0101] In this embodiment, the abnormal adjustment strategy list is a list composed of setting parameter adjustment strategies for the operating parameters of each key device in the power distribution subsystem; the key device operating parameters refer to the parameters used to describe and reflect the key information such as the state, performance, and efficiency of the key device during operation. For example, the key device operating parameters of the key device photovoltaic inverter device in the power generation subsystem include rated output power, response time, output voltage adjustment rate, load adjustment rate, etc., and the key device operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key device operating parameters of the key device energy storage battery in the energy storage subsystem include rated voltage, charge and discharge efficiency, etc., and the key device operating parameters of the bidirectional energy storage converter include conversion efficiency, response time, voltage, etc.; the key device operating parameters of the key device charging controller in the charging subsystem include rated voltage, rated current, etc.

[0102] In this embodiment, the setting parameter adjustment strategy is used to adjust the key device operating parameters of the key device. For example, the setting parameter adjustment strategy for the output voltage adjustment rate of the photovoltaic inverter is to optimize the inverter settings. The specific strategy content is: adjust the adjustable parameters that affect the output voltage adjustment rate. Among them, the adjustable parameters that affect the output voltage adjustment rate include voltage adjustment rate threshold, voltage setting value, PID control parameter, or DC bus voltage control parameter, etc.; the adjustable parameter refers to the parameter that can be customized and adjusted as marked in the corresponding setting parameter adjustment strategy content based on the device technical manual of the key device.

[0103] In this embodiment, the first parameter adjustment strategy is used to adjust abnormal parameters. Among them, the abnormal parameter refers to the key device operating parameter whose parameter data is greater than the corresponding high threshold of the set operating parameter or less than the corresponding low threshold of the set operating parameter; the adjustment influence coefficient is used to characterize the influence degree of the abnormal parameter on the normal operation and system efficiency of the power distribution subsystem; the setting parameter adjustment strategy is preset in advance; the abnormal maintenance report includes maintenance time, abnormal location, abnormal parameter, abnormal parameter value, and parameter value after maintenance.

[0104] In this embodiment, for example, there is an adjustable parameter s1 of 5.8 and an adjustment influence coefficient of 0.02, then the adjusted adjustable parameter s1 is 5.8 + .

[0105] The beneficial effects of the above technical solution are: by matching the abnormal parameter with the setting parameter adjustment strategy and finely adjusting the adjustable parameters in the setting parameter adjustment strategy, the flexibility and scientificity of the adjustment strategy can be improved, ensuring timely and accurate abnormal handling, and improving system security.

[0106] The embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic energy storage and charging. The calculation formula of the adjustment influence coefficient is as follows:

[0107] ; wherein, T represents the adjustment influence coefficient; represents the first parameter difference corresponding to the current first data; represents the set operating parameter threshold for obtaining the first parameter difference by comparing with the current first data; represents the first data; represents the performance influence weight of the key equipment operating parameters to which the current first data belongs on the power distribution subsystem; represents the data loss compensation factor.

[0108] In this embodiment, the performance influence weight is the influence weight of the key equipment operating parameters on the power distribution subsystem calculated by using the statistical analysis method and the principal component analysis method based on the historical key equipment operating parameter data, and its value range is ; the data loss compensation factor is obtained by using the entropy method to analyze the dispersion degree of the distribution of historical key equipment operating parameter data, and its value range is .

[0109] The beneficial effect of the above technical solution is: by calculating the adjustment influence coefficient and finely adjusting the adjustable parameters in the set parameter adjustment strategy, it helps to improve the flexibility and scientificity of the adjustment strategy, thereby ensuring timely and accurate exception handling and improving system security.

[0110] An embodiment of the present invention provides a power distribution system for improving the efficiency of optical storage and charging, and the state prediction unit includes:

[0111] Extract the historical key equipment operating parameter data of the key equipment within a preset time period from the corresponding power distribution - equipment database of the power distribution subsystem;

[0112] Perform data cleaning and normalization processing on the obtained historical key equipment operating parameter data to obtain historical operation data;

[0113] Use the historical operation data as training data to train a neural network to obtain the first state prediction model of the current power distribution subsystem;

[0114] Based on the set association screening principle, screen out the scheduling - related parameters from the key equipment operating parameters, and regard the remaining parameters as non - scheduling - related parameters;

[0115] Extract the scheduling - related parameter data from the key equipment operating parameter data of the power distribution subsystem and input it into the first state prediction model to obtain the predicted state result.

[0116] In this embodiment, the power distribution - equipment database is composed of the operation parameter data of the power distribution subsystem and the corresponding key equipment; the power distribution subsystem includes a power generation subsystem, an energy storage subsystem, and a charging subsystem; the preset time period is set in advance; the historical operation data is the data obtained after data cleaning and normalization processing of the historical operation parameter data of the key equipment, where the historical operation parameter data of the key equipment refers to the historical parameter data of the operation parameters of the key equipment in the power distribution subsystem within the preset time period.

[0117] In this embodiment, the key equipment operation parameters refer to the parameters used to describe and reflect the key information such as the state, performance, and efficiency of the key equipment during operation. For example, the key equipment operation parameters of the photovoltaic inverter equipment, which is a key equipment in the power generation subsystem, include rated output power, response time, output voltage adjustment rate, load adjustment rate, etc.; the key equipment operation parameters of the photovoltaic modules include short - circuit current, open - circuit voltage, peak power, etc.; the key equipment operation parameters of the energy storage battery, which is a key equipment in the energy storage subsystem, include rated voltage, charge - discharge efficiency, etc.; the key equipment operation parameters of the bi - directional energy storage converter include conversion efficiency, response time, voltage, etc.; the key equipment operation parameters of the charging controller, which is a key equipment in the charging subsystem, include rated voltage, rated current, etc.

[0118] In this embodiment, the first - state prediction model is used to predict the future operation states of the key equipment in each power distribution subsystem; the scheduling - related parameters are the parameters directly related to the scheduling strategy screened from the key equipment operation parameters using the set correlation screening principle, such as output voltage, output current, output power, etc.; the non - scheduling - related parameters are the operation parameters other than the scheduling - related parameters in the key equipment operation parameters, such as rated voltage, equipment operation time; the set correlation screening principle specifically includes: regarding the key equipment operation parameters that can directly affect the scheduling decision, energy task allocation, or system operation efficiency of the power distribution system as scheduling - related parameters.

[0119] The beneficial effects of the above - mentioned technical solution are: By training the state prediction model to predict the future operation states of the key equipment in each power distribution subsystem, it can lay a foundation for the generation of subsequent target scheduling strategies, and thus is conducive to improving the efficiency of the photovoltaic - energy - storage - charging system.

[0120] An embodiment of the present invention provides a power distribution system for improving the photovoltaic - energy - storage - charging efficiency. The scheduling analysis module includes:

[0121] A prediction and analysis unit: used to analyze the corresponding prediction data of the non - scheduling - related parameters using the non - scheduling prediction and analysis block; analyze the corresponding prediction data of the scheduling - related parameters using the scheduling prediction and analysis block; match the scheduling strategy for the power distribution subsystem using the strategy matching block.

[0122] Unscheduled prediction analysis block: used to compare and analyze the unscheduled prediction data for extracting unscheduled correlation parameters from the prediction results with the corresponding set operating parameter thresholds;

[0123] If there is unscheduled prediction data of an unscheduled correlation parameter greater than the corresponding set high operating parameter threshold or less than the set low operating parameter threshold, then mark the current unscheduled correlation parameter as a pre-abnormal unscheduled correlation parameter;

[0124] Otherwise, mark the current unscheduled correlation parameter as a pre-normal unscheduled correlation parameter;

[0125] Scheduled prediction analysis block: used to compare and analyze the scheduled prediction data of each scheduled correlation parameter with the set demand threshold;

[0126] If there is scheduled prediction data of a scheduled correlation parameter not greater than the corresponding set high demand threshold and not less than the set low demand threshold, then mark the current scheduled correlation parameter as a pre-normal scheduled correlation parameter;

[0127] If there is scheduled prediction data of a scheduled correlation parameter greater than the corresponding set high demand threshold, then mark the current scheduled correlation parameter as a high-abnormal scheduled correlation parameter and output it as the scheduled correlation prediction result;

[0128] Calculate the absolute difference between the scheduled prediction data of the high-abnormal scheduled correlation parameter and the corresponding set high demand threshold to obtain the first high-demand absolute difference and output it as the scheduled correlation prediction result;

[0129] If there is scheduled prediction data of a scheduled correlation parameter less than the corresponding set low demand threshold, then mark the current scheduled correlation parameter as a low-abnormal scheduled correlation parameter and output it as the scheduled correlation prediction result;

[0130] Calculate the absolute difference between the scheduled prediction data of the low-abnormal scheduled correlation parameter and the corresponding set low demand threshold to obtain the first low-demand absolute difference and output it as the scheduled correlation prediction result;

[0131] Policy matching block: used to calculate the scheduled correlation demand coefficient based on the scheduled correlation prediction result;

[0132] Determine the scheduled correlation demand level of the distribution sub-system according to the scheduled correlation demand coefficient;

[0133] Match the corresponding energy scheduling scheme from the demand level - scheduling scheme mapping table as the first scheduling strategy output of the distribution sub-system according to the scheduled correlation demand level;

[0134] The policy analysis unit: used to combine and calculate and analyze the prediction status result of the unscheduled correlation parameter generated by using the unscheduled prediction analysis block with the set unscheduled-scheduled correlation influence coefficient to obtain the first optimization coefficient;

[0135] Analyze the correlation degree between the current power distribution subsystem and other power distribution subsystems according to the preset system correlation evaluation rule to obtain the first correlation coefficient;

[0136] Perform a weighted average calculation on the first correlation coefficient and the first optimization coefficient to obtain the target adjustment coefficient;

[0137] Use the target adjustment coefficient to adjust the adjustable parameters in the first scheduling policy of the current power distribution subsystem to obtain the adjusted parameter values;

[0138] Replace the parameter values of the adjustable parameters in the current first scheduling policy with the adjusted parameter values, and then output them as the target scheduling policy.

[0139] In this embodiment, the unscheduled correlation parameter refers to a parameter that has no direct correlation with the energy scheduling policy, such as the rated voltage; the unscheduled prediction data refers to the future prediction data of the unscheduled correlation parameter; the set operating parameter threshold is predetermined, including the set high operating parameter threshold and the set low operating parameter threshold;

[0140] In this embodiment, the scheduling correlation parameter is a parameter that is directly related to the scheduling policy and is screened from the key device operating parameters by using the set correlation screening principle, such as the output voltage, output current, output power, etc.; the demand level-scheduling scheme mapping table is preset; the scheduling prediction data refers to the future prediction data of the scheduling correlation parameter; the set demand threshold is preset, including the set high demand threshold and the set low demand threshold; the first high demand absolute difference refers to the absolute difference between the scheduling prediction data of the high abnormal scheduling correlation parameter and the corresponding set high demand threshold, where the high abnormal scheduling correlation parameter is a scheduling correlation parameter marked as high abnormal; the first low demand absolute difference refers to the absolute difference between the scheduling prediction data of the low abnormal scheduling correlation parameter and the corresponding set low demand threshold, where the low abnormal scheduling correlation parameter is a scheduling correlation parameter marked as low abnormal.

[0141] In this embodiment, the scheduling correlation prediction result includes the high abnormal scheduling correlation parameter, the first high demand absolute difference, the low abnormal scheduling correlation parameter, and the first low demand absolute difference, where the pre-normal scheduling correlation parameter is a scheduling correlation parameter marked as pre-normal; the scheduling correlation demand coefficient is used to determine the scheduling correlation demand level; the scheduling correlation demand level is determined by comparing and analyzing the scheduling correlation demand coefficient with the set high level threshold and the set low level threshold, including level one, level two, and level three, where the set high level threshold and the set low level threshold are set in advance.

[0142] In this embodiment, when the scheduling associated demand coefficient is greater than the high threshold of the set level, it is determined that the current scheduling associated demand level is level one;

[0143] When the scheduling associated demand coefficient is less than the low threshold of the set level, it is determined that the current scheduling associated demand level is level two;

[0144] When the scheduling associated demand coefficient is not greater than the high threshold of the set level and less than the low threshold of the set level, it is determined that the current scheduling associated demand level is level three.

[0145] In this embodiment, the scheduling associated demand coefficient is obtained by weighted addition of the calculation result of multiplying the total number of parameters of the high-abnormality scheduling associated parameters by the average value of the first high-demand absolute differences of all high-abnormality scheduling associated parameters, and the calculation result of multiplying the total number of parameters of the low-abnormality scheduling associated parameters by the average value of the first low-demand absolute differences of all low-abnormality scheduling associated parameters.

[0146] In this embodiment, the predicted state result includes the number of parameters of the pre-abnormal non-scheduling associated parameters and the number of pre-normal non-scheduling associated parameters in the current distribution subsystem; the set non-scheduling - scheduling association influence coefficient is preset and is used to represent the influence degree of the non-scheduling associated parameters on the scheduling associated parameters; the first optimization coefficient is obtained by combining and calculating the number of parameters of the pre-abnormal non-scheduling associated parameters and the number of pre-normal non-scheduling associated parameters in the current distribution subsystem obtained from the non-scheduling prediction analysis block with the non-scheduling - scheduling association influence coefficient; the first correlation coefficient is obtained by evaluating the correlation between the current distribution subsystem and other distribution subsystems according to the preset system association evaluation rule, where the preset system association evaluation rule refers to evaluating the correlation between distribution subsystems according to the set system association indicators, and the set system association indicators include energy flow and conversion efficiency, system cooperation, cost-benefit, and distribution reliability; the target adjustment coefficient is used to adjust the adjustable parameters in the first scheduling strategy.

[0147] The beneficial effects of the above technical solution are: by analyzing the predicted state result to generate the target scheduling strategy to realize the dynamic adjustment of energy configuration, reasonably arranging the power output of the photovoltaic power generation and the energy storage system, which is beneficial to ensuring the stability and reliability of power supply.

[0148] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A power distribution system for improving light storage and charging efficiency, characterized in that: include: Data acquisition module: used to monitor the operating status of the target photovoltaic power distribution system in real time and obtain key equipment operating parameter data; Abnormal processing and prediction module: used to detect and process abnormal operation status of the target photovoltaic distribution system based on the key equipment operation parameter data, and predict the future operation status of the distribution system; Scheduling analysis module: used to generate target scheduling strategies based on the prediction results of the future operating status of the distribution system and dynamically adjust energy configuration; Wherein, the data acquisition module includes: Obtaining key equipment operating parameters of each distribution subsystem in the target photovoltaic distribution system; Determine the designated monitoring tools for each distribution subsystem based on key equipment operating parameters; Use designated monitoring tools to monitor the operating status of key equipment in each distribution subsystem in real time and obtain key equipment operating parameter data; Wherein, the exception handling and prediction module includes: A data analysis unit: used for marking the current key equipment operating parameter data as the first data when the key equipment operating parameter data of the power distribution subsystem is greater than the corresponding set operating parameter high threshold or less than the corresponding set operating parameter low threshold; Marking the key equipment operating parameters corresponding to the first data as abnormal parameters; Calculating the absolute differences between the first data and the corresponding set operating parameter high threshold and set operating parameter low threshold, and treating the smaller absolute difference as the first parameter difference; When there is no key equipment operating parameter data in the power distribution subsystem that is greater than the corresponding set operating parameter high threshold or less than the corresponding set operating parameter low threshold, marking all key equipment operating parameter data as second data; Calibrate the key equipment operating parameters corresponding to the second data as normal parameters; Calculate the absolute operating parameter differences between the second data and the corresponding set operating parameter high threshold and set operating parameter low threshold, and regard the smaller operating parameter absolute difference as the second parameter difference; An abnormality analysis unit: when there are abnormal parameters in the power distribution subsystem, determine a first parameter adjustment strategy from the extracted abnormal adjustment strategy list matching the current power distribution subsystem to perform abnormal processing on the abnormal parameters; State prediction unit: used to extract and analyze historical equipment operating parameter data of key equipment operating parameters within a preset time period when there are no abnormal parameters in the distribution subsystem, to achieve state prediction of the current distribution subsystem operating state and obtain state prediction results.

2. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: The power distribution subsystem includes a power generation subsystem, an energy storage subsystem and a charging subsystem.

3. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: The abnormality analysis unit comprises: Extracting a setting parameter adjustment strategy for processing the abnormal parameter from the abnormal adjustment strategy list; If there is an adjustable parameter in the setting parameter adjustment strategy of the current abnormal parameter, then the adjustment influence coefficient is calculated based on the first parameter difference corresponding to the first data; After adjusting the adjustable parameters in the setting parameter adjustment strategy of the current abnormal parameter by using the adjustment influence coefficient, a first parameter adjustment strategy is generated; If there is no adjustable parameter in the setting parameter adjustment strategy of the current abnormal parameter, the obtained setting parameter adjustment strategy is regarded as the first parameter adjustment strategy; The first parameter adjustment strategy is used to perform abnormal maintenance on abnormal parameters of the current power distribution subsystem, and an abnormal maintenance report is generated.

4. A power distribution system for improving light storage and charging efficiency according to claim 3, characterized in that: The calculation formula of the adjustment impact coefficient is as follows: ; In the formula, T represents the adjustment influence coefficient; It is represented as the first parameter difference corresponding to the current first data; It is represented by the set operating parameter threshold value which is compared with the current first data to obtain the first parameter difference; Represented as first data; Indicates the weight of the impact of the key equipment operating parameters to which the current first data belongs on the performance of the distribution subsystem; Expressed as a data loss compensation factor.

5. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: The state prediction unit comprises: Extracting historical key equipment operating parameter data of key equipment within a preset time period from a corresponding power distribution-equipment database of the power distribution subsystem; The historical operation data of key equipment operation parameters are cleaned and normalized to obtain historical operation data; Using the historical operation data as training data to train a neural network, to obtain a first state prediction model of the current power distribution subsystem; Based on the set association screening principle, the scheduling-related parameters are screened out from the key equipment operating parameters, and the remaining parameters are used as non-scheduling-related parameters; The dispatching-related parameter data are extracted from the key equipment operating parameter data of the power distribution subsystem and input into the first state prediction model to obtain the predicted state result.

6. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: The scheduling analysis module includes: Prediction and analysis unit: used to analyze the corresponding prediction data of non-scheduling related parameters using the non-scheduling prediction and analysis block; to analyze the corresponding prediction data of scheduling related parameters using the scheduling prediction and analysis block; to match the scheduling strategy to the distribution subsystem using the strategy matching block; Non-scheduling prediction analysis block: used to extract the non-scheduling prediction data of non-scheduling related parameters from the prediction results and compare and analyze them with the corresponding set operating parameter thresholds; If there is a non-scheduling prediction data of a non-scheduling associated parameter that is greater than the corresponding set operating parameter high threshold or less than the set operating parameter low threshold, the current non-scheduling associated parameter is marked as a pre-abnormal non-scheduling associated parameter; Otherwise, the current non-scheduling associated parameter is marked as a pre-normal non-scheduling associated parameter; Scheduling prediction analysis block: used to compare and analyze the scheduling prediction data of each scheduling-related parameter with the set demand threshold; If the scheduling prediction data of the scheduling-related parameters is not greater than the corresponding set demand high threshold and is not less than the set demand low threshold, the current scheduling-related parameters are marked as pre-normal scheduling-related parameters; If there is a scheduling prediction data of a scheduling-related parameter that is greater than the corresponding set high demand threshold, the current scheduling-related parameter is marked as a high abnormal scheduling-related parameter and then output as a scheduling-related prediction result; Calculate the absolute difference between the scheduling prediction data of the high abnormal scheduling associated parameter and the corresponding set demand high threshold value, obtain the first high demand absolute difference and output it as the scheduling associated prediction result; If there is a scheduling prediction data of a scheduling-related parameter that is less than the corresponding set demand low threshold, the current scheduling-related parameter is marked as a low abnormal scheduling-related parameter and then output as a scheduling-related prediction result; Calculate the absolute difference between the scheduling prediction data of the low abnormal scheduling associated parameter and the corresponding set demand low threshold, obtain a first low demand absolute difference and output it as a scheduling associated prediction result; Strategy matching block: used to calculate the scheduling association demand coefficient based on the scheduling association prediction result; Determining a dispatch-related demand level of the distribution subsystem according to the dispatch-related demand coefficient; According to the scheduling-associated demand level, matching a corresponding energy scheduling scheme from a demand level-scheduling scheme mapping table as a first scheduling strategy output of the distribution subsystem; A strategy analysis unit: used to combine the prediction state result of the non-scheduling associated parameter generated by the non-scheduling prediction analysis block with the set non-scheduling-scheduling associated influence coefficient to calculate and analyze, and obtain a first optimization coefficient; Perform correlation analysis on the current power distribution subsystem and other power distribution subsystems according to the preset system correlation evaluation rules to obtain a first correlation coefficient; Calculate the target adjustment coefficient by weighted average of the first correlation coefficient and the first optimization coefficient; Using the target adjustment coefficient, an adjustable parameter in the first dispatching strategy of the current power distribution subsystem is adjusted to obtain an adjusted parameter value; The parameter values ​​of the adjustable parameters in the current first scheduling strategy are replaced with the adjusted parameter values, which are then output as the target scheduling strategy.

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