Power distribution system for improving optical storage and charging efficiency

Abstract

The invention provides a power distribution system for improving optical storage and charging efficiency, and relates to the technical field of data processing, and the system comprises a data collection module which monitors the operation state of a target photovoltaic power distribution system in real time, and obtains key equipment operation parameter data; the exception handling and predicting module is used for carrying out operation state exception detection and processing on the target photovoltaic power distribution system based on the key equipment operation parameter data and predicting the future operation state of the power distribution system; and the scheduling analysis module is used for generating a target scheduling strategy based on the prediction result of the future operation state of the power distribution system and dynamically adjusting energy configuration. By analyzing key equipment operation parameter data obtained by monitoring the operation state of a target photovoltaic power distribution system in real time, accurate detection of an abnormal operation condition and effective prediction of the future operation state of the power distribution system are realized, so that a target scheduling strategy is generated, energy configuration is dynamically adjusted, the light storage and charging efficiency is effectively improved, and the energy consumption is reduced. The operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a power distribution system for improving light storage and charging efficiency. Background Art

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

[0003] Therefore, the present invention provides a power distribution system that improves the efficiency of light storage and charging. Summary of the invention

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

[0005] The present invention provides a power distribution system for improving light storage and charging efficiency, comprising: 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.

[0006] Preferably, 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.

[0007] Preferably, the power distribution subsystem includes a power generation subsystem, an energy storage subsystem and a charging subsystem.

[0008] Preferably, 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.

[0009] Preferably, 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.

[0010] Preferably, the calculation formula for adjusting the influence 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.

[0011] Preferably, 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.

[0012] Preferably, 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.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By analyzing the key equipment operating parameter data obtained by real-time monitoring of the operating status of the target photovoltaic distribution system, accurate detection of abnormal operating conditions and effective prediction of the future operating status of the distribution system can be achieved, thereby generating a target scheduling strategy, dynamically adjusting energy configuration, effectively improving the efficiency of photovoltaic storage and charging, and reducing operating costs.

[0014] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural diagram of a power distribution system for improving the efficiency of light storage and charging in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] The embodiment of the present invention provides a power distribution system for improving the efficiency of light storage and charging, such as Figure 1 As shown, including: 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.

[0019] In this embodiment, the photovoltaic distribution system is an integrated photovoltaic, storage and charging distribution system composed of a power generation subsystem, an energy storage subsystem and a charging subsystem. The power generation subsystem converts solar energy into electrical energy through solar panels, directly providing green and renewable energy for the system. The energy storage subsystem uses energy storage batteries to store excess electrical energy and releases it when needed. The charging subsystem provides fast and convenient charging services.

[0020] In this embodiment, key equipment refers to equipment in each 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, and the key equipment of the charging subsystem includes charging controllers; the equipment importance coefficient refers to the coefficient obtained by evaluating the equipment from three evaluation directions of functional importance, economic benefits and accident frequency based on pre-set evaluation standards, and then combining the comprehensive weights of each evaluation direction determined by calculating the subjective weight using the hierarchical analysis method and the objective weight using the entropy weight method to calculate the coefficient, which is used to quantify the importance of the equipment to the distribution subsystem; the set importance threshold is pre-set.

[0021] In this embodiment, the key equipment operating parameters refer to parameters used to describe and reflect key information such as the state, performance, efficiency, etc. of the key equipment during operation. For example, the key equipment operating parameters of the photovoltaic inverter equipment, which is a key equipment in the power generation subsystem, include rated output power, response time, output voltage regulation rate, load regulation rate, etc., and the key equipment operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key equipment operating parameters of the energy storage battery, which is a key equipment in the energy storage subsystem, include rated voltage, charge and discharge efficiency, etc., and the key equipment operating parameters of the bidirectional energy storage inverter include conversion efficiency, response time, voltage, etc.; the key equipment operating parameters of the charging controller, which is a key equipment in the charging subsystem, include rated voltage, rated current, etc.

[0022] In this embodiment, the target scheduling strategy is used to adjust the charging and discharging strategy to achieve intelligent interaction and collaborative work among photovoltaic power generation, energy storage and charging, thereby improving energy utilization efficiency and system stability.

[0023] The beneficial effect of the above technical solution is: by analyzing the key equipment operating parameter data obtained by real-time monitoring of the operating status of the target photovoltaic distribution system, accurate detection of abnormal operating conditions and effective prediction of the future operating status of the distribution system can be achieved, thereby generating a target scheduling strategy, dynamically adjusting energy configuration, effectively improving the photovoltaic storage and charging efficiency, and reducing operating costs.

[0024] An embodiment of the present invention provides a power distribution system for improving light storage and charging efficiency, wherein the data acquisition module comprises: 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.

[0025] In this embodiment, the photovoltaic power distribution system is an integrated photovoltaic 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.

[0026] In this embodiment, key equipment refers to equipment in each 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, and the key equipment of the charging subsystem includes charging controllers; the equipment importance coefficient refers to the coefficient obtained by evaluating the equipment from three evaluation directions of functional importance, economic benefits and accident frequency based on pre-set evaluation standards, and then combining the comprehensive weights of each evaluation direction determined by calculating the subjective weight using the hierarchical analysis method and the objective weight using the entropy weight method to calculate the coefficient, which is used to quantify the importance of the equipment to the distribution subsystem; the set importance threshold is pre-set.

[0027] In this embodiment, the key equipment operating parameters refer to parameters used to describe and reflect key information such as the state, performance, efficiency, etc. of the key equipment during operation. For example, the key equipment operating parameters of the photovoltaic inverter equipment, which is a key equipment in the power generation subsystem, include rated output power, response time, output voltage regulation rate, load regulation rate, etc., and the key equipment operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key equipment operating parameters of the energy storage battery, which is a key equipment in the energy storage subsystem, include rated voltage, charge and discharge efficiency, etc., and the key equipment operating parameters of the bidirectional energy storage inverter include conversion efficiency, response time, voltage, etc.; the key equipment operating parameters of the charging controller, which is a key equipment in the charging subsystem, include rated voltage, rated current, etc.; the designated monitoring tool is predetermined and used to monitor the operating status of the key equipment of the distribution subsystem.

[0028] The beneficial effect of the above technical solution is: by installing designated monitoring tools on the key equipment of the distribution subsystem to monitor the equipment operation and collect equipment operation data in real time, effective data support can be laid for subsequent status anomaly detection and future status prediction.

[0029] An embodiment of the present invention provides a power distribution system for improving the efficiency of light storage and charging, wherein the abnormality processing 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.

[0030] In this embodiment, the high threshold value of the operating parameter is set in advance; the low threshold value of the operating parameter is set in advance; the first data refers to the key equipment operating parameter data that is greater than the corresponding high threshold value of the operating parameter or less than the corresponding low threshold value of the operating parameter; the abnormal parameter refers to the key equipment operating parameter corresponding to the first data; the absolute difference of the operating parameter refers to the absolute difference between the first data and the high threshold value of the operating parameter or the low threshold value of the operating parameter.

[0031] In this embodiment, the abnormal adjustment strategy list is a list composed of the set parameter adjustment strategies for each key device operating parameter set in the distribution subsystem; the key device operating parameters refer to the parameters used to describe and reflect the key information such as the state, performance, efficiency, etc. of the key equipment in the operation process of the key equipment. For example, the key equipment operating parameters of the photovoltaic inverter equipment, which is the key equipment in the power generation subsystem, include rated output power, response time, output voltage regulation rate, load regulation rate, etc., and the key equipment operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key equipment operating parameters of the energy storage battery, which is the key equipment in the energy storage subsystem, include rated voltage, charge and discharge efficiency, etc., and the key equipment operating parameters of the bidirectional energy storage inverter include conversion efficiency, response time, voltage, etc.; the key equipment operating parameters of the charging controller, which is the key equipment in the charging subsystem, include rated voltage, rated current, etc.

[0032] In this embodiment, the first parameter adjustment strategy is used to adjust abnormal parameters, wherein the abnormal parameters refer to key equipment operating parameters whose parameter data is greater than the corresponding set operating parameter high threshold or less than the corresponding set operating parameter low threshold.

[0033] In this embodiment, for example, there are key equipment operating parameters of photovoltaic inverter a1, a key equipment of the power generation subsystem, with a rated output power of 150kW and an output voltage adjustment rate of ; Among them, 150KW is less than the corresponding set operating parameter low threshold, is greater than the corresponding high threshold of the set operating parameters; at this time, 150kW, All are marked as the first data, and the rated output power and output voltage regulation rate of the photovoltaic inverter a1 are calibrated as abnormal parameters.

[0034] In this embodiment, the preset time period is preset; the historical equipment operation parameter data refers to the historical parameter data of the key equipment operation parameters of the distribution subsystem within the preset time period.

[0035] The beneficial effects of the above technical solution are: by performing abnormal detection on the distribution subsystem, problems can be discovered and handled in a timely manner, and the future state of the distribution subsystem can be predicted, which can provide data support for the optimization of the scheduling strategy, and can effectively improve the photovoltaic storage and charging efficiency while ensuring the safety of the system.

[0036] An embodiment of the present invention provides a power distribution system for improving light storage and charging efficiency, wherein 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.

[0037] In this embodiment, the abnormal adjustment strategy list is a list composed of the set parameter adjustment strategies for each key device operating parameter set in the distribution subsystem; the key device operating parameters refer to the parameters used to describe and reflect the key information such as the state, performance, efficiency, etc. of the key equipment in the operation process of the key equipment. For example, the key equipment operating parameters of the photovoltaic inverter equipment, which is the key equipment in the power generation subsystem, include rated output power, response time, output voltage regulation rate, load regulation rate, etc., and the key equipment operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key equipment operating parameters of the energy storage battery, which is the key equipment in the energy storage subsystem, include rated voltage, charge and discharge efficiency, etc., and the key equipment operating parameters of the bidirectional energy storage inverter include conversion efficiency, response time, voltage, etc.; the key equipment operating parameters of the charging controller, which is the key equipment in the charging subsystem, include rated voltage, rated current, etc.

[0038] In this embodiment, the parameter adjustment strategy is set to adjust the key equipment operating parameters of the key equipment. For example, the parameter adjustment strategy for the output voltage regulation 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 regulation rate, wherein the adjustable parameters that affect the output voltage regulation rate include voltage regulation rate threshold, voltage setting value, PID control parameters or DC bus voltage control parameters, etc.; adjustable parameters refer to parameters that can be custom adjusted based on the equipment technical manual of the key equipment in the corresponding parameter adjustment strategy content.

[0039] In this embodiment, the first parameter adjustment strategy is used to adjust abnormal parameters, wherein abnormal parameters refer to key equipment operating parameters whose parameter data is greater than the corresponding set operating parameter high threshold or less than the corresponding set operating parameter low threshold; the adjustment influence coefficient is used to characterize the degree of influence of abnormal parameters on the normal operation and system efficiency of the distribution subsystem; the set parameter adjustment strategy is pre-set; the abnormal maintenance report includes maintenance time, abnormal location, abnormal parameters, abnormal parameter values ​​and parameter values ​​after maintenance.

[0040] In this embodiment, for example, if the adjustable parameter s1 is 5.8 and the adjustment influence coefficient is 0.02, then the adjusted adjustable parameter s1 is 5.8+ .

[0041] The beneficial effect of the above technical solution is: by matching the abnormal parameters with the set parameter adjustment strategy and fine-tuning the adjustable parameters existing in the set parameter adjustment strategy, the flexibility and scientificity of the adjustment strategy can be improved, the abnormality handling can be ensured to be timely and accurate, and the system security can be improved.

[0042] An embodiment of the present invention provides a power distribution system for improving light storage and charging efficiency, and the calculation formula of the adjustment influence 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 represents a set operating parameter threshold value which is compared with the current first data to obtain a 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.

[0043] In this embodiment, the performance impact weight is the impact weight of the key equipment operating parameters on the 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 discrete degree of distribution of historical key equipment operating parameter data. Its value range is .

[0044] The beneficial effect of the above technical solution is: by calculating the adjustment influence coefficient, the adjustable parameters in the set parameter adjustment strategy are fine-tuned, which helps to improve the flexibility and scientific nature of the adjustment strategy, thereby ensuring timely and accurate exception handling and improving system security.

[0045] An embodiment of the present invention provides a power distribution system for improving light storage and charging efficiency, wherein 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; The historical operation data is used 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.

[0046] In this embodiment, the distribution-equipment database is composed of operating parameter data of the distribution subsystem and the corresponding key equipment; the distribution subsystem includes a power generation subsystem, an energy storage subsystem and a charging subsystem; the preset time period is pre-set; the historical operating data is the data obtained after data cleaning and normalization processing of the historical key equipment operating parameter data, wherein the historical key equipment operating parameter data refers to the historical parameter data of the key equipment operating parameters of the distribution subsystem within the preset time period.

[0047] In this embodiment, the key equipment operating parameters refer to parameters used to describe and reflect key information such as the state, performance, efficiency, etc. of the key equipment during operation. For example, the key equipment operating parameters of the photovoltaic inverter equipment, which is a key equipment in the power generation subsystem, include rated output power, response time, output voltage regulation rate, load regulation rate, etc., and the key equipment operating parameters of the photovoltaic module include short-circuit current, open-circuit voltage, peak power, etc.; the key equipment operating parameters of the energy storage battery, which is a key equipment in the energy storage subsystem, include rated voltage, charge and discharge efficiency, etc., and the key equipment operating parameters of the bidirectional energy storage inverter include conversion efficiency, response time, voltage, etc.; the key equipment operating parameters of the charging controller, which is a key equipment in the charging subsystem, include rated voltage, rated current, etc.

[0048] In this embodiment, the first state prediction model is used to predict the future operating state of key equipment of each distribution subsystem; the scheduling associated parameters are parameters directly related to the scheduling strategy, which are screened out from the key equipment operating parameters using the set associated screening principle, such as output voltage, output current, output power, etc.; non-scheduling associated parameters are operating parameters of the key equipment operating parameters other than the scheduling associated parameters, such as rated voltage and equipment operating time; setting the associated screening principle specifically includes: treating the key equipment operating parameters that can directly affect the scheduling decision, energy task allocation or system operating efficiency of the distribution system as scheduling associated parameters.

[0049] The beneficial effect of the above technical solution is: by training the state prediction model to predict the future operating status of the key equipment of each distribution subsystem, it can lay the foundation for the generation of subsequent target scheduling strategies, which is beneficial to improving the system's solar storage and charging efficiency.

[0050] An embodiment of the present invention provides a power distribution system for improving the efficiency of photovoltaic storage and charging, wherein the scheduling and analysis module comprises: 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; Performing weighted average calculation on the first correlation coefficient and the first optimization coefficient to obtain a target adjustment 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.

[0051] In this embodiment, non-dispatch-related parameters refer to parameters that are not directly related to the energy dispatch strategy, such as rated voltage; non-dispatch prediction data refer to future prediction data of non-dispatch-related parameters; setting the operating parameter threshold is predetermined, including setting the operating parameter high threshold and setting the operating parameter low threshold; In this embodiment, the scheduling associated parameters are parameters directly related to the scheduling strategy, such as output voltage, output current, output power, etc., which are screened out from the key equipment operating parameters using the set associated screening principle; the demand level-scheduling scheme mapping table is pre-set; the scheduling prediction data refers to the future prediction data of the scheduling associated parameters; the set demand threshold is pre-set, including setting the high demand threshold and setting the low demand threshold; the first high demand absolute difference refers to the absolute difference between the scheduling prediction data of the high abnormal scheduling associated parameter and the corresponding set demand high threshold, wherein the high abnormal scheduling associated parameter refers to the scheduling associated parameter marked as high abnormality; the first low demand absolute difference refers to the absolute difference between the scheduling prediction data of the low abnormal scheduling associated parameter and the corresponding set demand low threshold, wherein the low abnormal scheduling associated parameter refers to the scheduling associated parameter marked as low abnormality.

[0052] In this embodiment, the scheduling association prediction results include high abnormal scheduling association parameters, the first high demand absolute difference, low abnormal scheduling association parameters and the first low demand absolute difference, wherein the pre-normal scheduling association parameters refer to the scheduling association parameters marked as pre-normal; the scheduling association demand coefficient is used to determine the scheduling association demand level; the scheduling association demand level is determined by comparing and analyzing the scheduling association demand coefficient with the set level high threshold and the set level low threshold, including level one, level two and level three, wherein the set level high threshold and the set level low threshold are set in advance.

[0053] In this embodiment, when the scheduling-related demand coefficient is greater than the set level high threshold, it is determined that the current scheduling-related demand level is level one; When the dispatch-related demand coefficient is less than the set level low threshold, the current dispatch-related demand level is determined to be level 2; When the scheduling-associated demand coefficient is not greater than the set level high threshold and is less than the set level low threshold, it is determined that the current scheduling-associated demand level is level three.

[0054] In this embodiment, the scheduling-related demand coefficient is a coefficient obtained by weightedly adding the calculation result of multiplying the total number of high-abnormal scheduling-related parameters by the average value of the first highest demand absolute difference of all high-abnormal scheduling-related parameters, and the calculation result of multiplying the total number of low-abnormal scheduling-related parameters by the average value of the first lowest demand absolute difference of all low-abnormal scheduling-related parameters.

[0055] In this embodiment, the predicted state result includes the number of parameters of pre-abnormal non-dispatch associated parameters and the number of pre-normal non-dispatch associated parameters; the non-dispatch-dispatch associated influence coefficient is set in advance and is used to express the degree of influence of the non-dispatch associated parameters on the dispatch associated parameters; the first optimization coefficient is obtained by combining the number of parameters of pre-abnormal non-dispatch associated parameters and the number of pre-normal non-dispatch associated parameters in the current distribution subsystem obtained in the non-dispatch prediction analysis block with the non-dispatch-dispatch associated influence coefficient for calculation and analysis; the first correlation coefficient is obtained by performing a correlation evaluation on the current distribution subsystem and other distribution subsystems according to a preset system correlation evaluation rule, wherein the preset system correlation evaluation rule refers to evaluating the correlation between distribution subsystems according to set system correlation indicators, and the set system correlation indicators include energy flow and conversion efficiency, system coordination, cost-effectiveness and distribution reliability; the target adjustment coefficient is used to adjust the adjustable parameters in the first dispatch strategy.

[0056] The beneficial effects of the above technical solution are: by analyzing the predicted state results, a target scheduling strategy is generated to dynamically adjust the energy configuration, and the output of photovoltaic power generation and energy storage systems is reasonably arranged, which is conducive to ensuring the stability and reliability of power supply.

[0057] 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 equivalents, the present invention is also intended to include these modifications and variations.

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.

2. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: The data acquisition module comprises: 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.

3. 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.

4. A power distribution system for improving light storage and charging efficiency according to claim 1, characterized in that: 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.

5. A power distribution system for improving light storage and charging efficiency according to claim 4, 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.

6. A power distribution system for improving light storage and charging efficiency according to claim 5, 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.

7. A power distribution system for improving light storage and charging efficiency according to claim 4, 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.

8. 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.

Images