Energy storage power station local power distribution method and system

Abstract

This invention discloses a local power allocation method for an energy storage power station, comprising: acquiring and preprocessing data information of battery modules of the target energy storage power station; performing cluster analysis on the battery modules of the target energy storage power station based on the DBSCAN algorithm; constructing a decision matrix based on the state of charge, state of health, and remaining capacity of the battery modules according to the cluster analysis results, and calculating the priority of each clustered battery module cluster using the entropy weight method; constructing and solving a local power allocation model for the target energy storage power station with the objectives of loss, aging cost, state of charge balance, and power allocation optimization based on battery module priority; and completing the local power allocation of the target energy storage power station based on the solution results. This invention also discloses a system for implementing the local power allocation method for the energy storage power station. This invention not only enables local power allocation of energy storage power stations but also considers the inherent characteristics of battery modules, resulting in higher reliability and better safety.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a local power distribution method and system for energy storage power stations. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, an increasing number of new energy power generation systems are being integrated into the power grid on a large scale. As an important means of regulating the intermittency and volatility of new energy sources, the safe and reliable operation of energy storage power stations is crucial for the power system.

[0004] Energy storage power stations consist of numerous battery modules. Over long-term operation, due to subtle differences in manufacturing processes and the influence of environmental factors such as temperature, humidity, and charging / discharging frequency, these modules gradually exhibit significant differences in state of charge (SOC), state of health (SOH), and internal resistance. Currently, energy storage power station power allocation schemes employ a traditional "one-size-fits-all" approach. This approach ignores the individual differences in SOC, SOH, and internal resistance among battery modules, applying a uniform power allocation strategy to all modules and completely disregarding individual performance variations. This lack of differentiated control for modules in different states can lead to overuse of high-health modules due to their performance advantage, accelerating their degradation. Furthermore, the failure to consider the coupling relationship between SOC and internal resistance can introduce safety hazards. Moreover, existing solutions often overlook the close coupling relationship between SOC and internal resistance. When the internal resistance of the battery module increases, if it is still charged and discharged at the normal power, it is very easy to cause dangerous overcharging and over-discharging of the battery. This not only seriously affects the battery's service life, but may also bring safety hazards. Summary of the Invention

[0005] One of the objectives of this invention is to provide a local power allocation method for energy storage power stations that is highly reliable and safe.

[0006] The second objective of this invention is to provide a system for implementing the local power allocation method of the energy storage power station.

[0007] The local power allocation method for energy storage power stations provided by this invention includes the following steps:

[0008] S1. Obtain data information of the battery modules of the target energy storage power station;

[0009] S2. Preprocess the data obtained in step S1;

[0010] S3. Based on the preprocessed data information obtained in step S2, perform cluster analysis on the battery modules of the target energy storage power station using the DBSCAN algorithm;

[0011] S4. Based on the clustering analysis results obtained in step S3, construct a decision matrix using the state of charge, state of health, and remaining capacity of the battery modules, and calculate the priority of each clustered battery module cluster using the entropy weight method.

[0012] S5. With the goals of optimizing power allocation based on battery module priority, construct a local power allocation model for the target energy storage power station;

[0013] S6. Solve the local power allocation model constructed in step S5, and complete the local power allocation of the target energy storage power station based on the solution results.

[0014] Step S1 specifically includes the following steps:

[0015] Acquire data information from the battery modules of the target energy storage power station; the data information includes state of charge (SOC), state of health (SOH), and internal resistance. .

[0016] Step S2 specifically includes the following steps:

[0017] The preprocessing process includes data cleaning, data interpolation, and data normalization;

[0018] The data normalization mentioned above is specifically performed using the following formula: In the formula X represents the normalized data; X represents the data before normalization. The minimum value of the data; This represents the maximum value of the data.

[0019] Step S3 includes the following steps:

[0020] The preprocessed state of charge, state of health, and internal resistance are used as three-dimensional feature vectors to construct a feature space;

[0021] The DBSCAN algorithm is used to perform cluster analysis on the battery modules of the target energy storage power station:

[0022] During cluster analysis, weighted Euclidean distance is used to represent the similarity of each sample in the feature space; the neighborhood radius during clustering is adjusted according to the performance degradation effect of temperature and the stability of the battery module's health status; and the minimum number of samples for each cluster is set according to the total number of battery modules.

[0023] Step S3 specifically includes the following steps:

[0024] The similarity between any two samples x and y in the feature space is calculated using the following formula: In the formula The similarity between sample x and sample y; Let x be the SOC value of sample x; Let y be the SOC value of the sample y; Here is the SOH value of sample x; Here is the SOH value of sample y; Let x be the internal resistance value of sample x; Let y be the internal resistance value of the sample y; The weights are set for the SOC value. The weighting of the set SOH value, The internal resistance value is set as the weight, and ;

[0025] Based on the performance degradation effect of temperature and the stability of the battery module's health status, the neighborhood radius during clustering is adjusted in real time using the following formula. : In the formula The set baseline radius value; The set weight value for health fluctuations; This serves as a stability indicator of the battery module's health status, and , This is the function for calculating standard deviation. This is historical data on the State of Health (SOH) of the battery module over the past 3 days; The set temperature correction weight value; This is the temperature correction factor for the battery module, and , These are the settings for temperature correction. This is the highest temperature of the battery module.

[0026] Based on the total number of battery modules, the minimum number of samples for each cluster is calculated using the following formula: In the formula The minimum number of samples for each cluster; This represents the total number of battery modules; This is a rounding up operation;

[0027] During clustering, for each sample point p, if the neighborhood radius of p... The number of samples within the range shall not be less than the minimum sample size. If the sample point p is selected as the cluster core, then the neighborhood radius of the cluster core p is considered as follows: If there are other clustering cores q, then clustering core q is added to the cluster corresponding to clustering core p, and clustering continues with clustering core q as the center;

[0028] Finally, the clustering is completed.

[0029] Step S4 includes the following steps:

[0030] A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the battery module as indicators.

[0031] The constructed decision matrix is ​​standardized.

[0032] Based on the standardized decision matrix, the entropy value and corresponding weight value of each indicator are calculated using the entropy weight method.

[0033] Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated.

[0034] Step S4 specifically includes the following steps:

[0035] A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the clustered battery module clusters as indicators.

[0036] Among them, the State of Charge (SOC) of the battery module cluster is the arithmetic mean of the SOCs of all battery modules in the cluster; the State of Health (SOH) of the battery module cluster is the arithmetic mean of the SOHs of all battery modules in the cluster; the Remaining Capacity of the battery module cluster is the sum of the remaining capacities of all battery modules in the cluster; and the Internal Resistance of the battery module cluster is the sum of the internal resistances of all battery modules in the cluster.

[0037] The constructed decision matrix is ​​standardized to obtain the standardized decision matrix Q;

[0038] Based on the entropy weight method, the entropy value and corresponding weight value of each indicator are calculated using the following formula: , In the formula Let be the entropy value of the j-th index; m is the total number of battery module clusters. Let be the element in the i-th row and j-th column of the standardized decision matrix Q; Let be the weight value of the j-th indicator;

[0039] Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated using the following formula: In the formula This represents the priority of the i-th battery module cluster.

[0040] Step S5 includes the following steps:

[0041] The priority weight factor of each battery module cluster is calculated based on the priority of each battery module cluster, and the power allocation optimization of the priority of each battery module cluster is calculated based on the priority weight factor of each battery module cluster.

[0042] Using battery module cluster losses, aging costs, state of charge balance, and priority power allocation optimization as objective functions, and total power balance, power limits, SOC safety range, and SOH safety range as constraints, a local power allocation model for the target energy storage power station is constructed.

[0043] Step S5 specifically includes the following steps:

[0044] The following formula is used as the objective function of the local power allocation model for the target energy storage power station: In the formula Let be the power value of the i-th battery module cluster; Let be the equivalent internal resistance value of the i-th battery module cluster; Let SOH be the SOH value of the i-th battery module cluster; is the standard deviation of the SOC values ​​of the battery modules contained in the i-th battery module cluster; Let be the priority weight factor for the i-th battery module cluster, and , This represents the maximum priority of the battery module cluster. The set aging cost weight value; The set weight value for the state of charge balance; Optimize the weight values ​​for power allocation based on the set priorities;

[0045] The following formula is used as a constraint condition for the local power allocation model of the target energy storage power station:

[0046] The following formula is used as the total power balance constraint: In the formula The power regulation command value for the target energy storage power station;

[0047] The following formula is used as the power limitation constraint: In the formula The maximum allowable power of the i-th battery module cluster is set.

[0048] The following formula is used as the SOC safety range constraint: In the formula The minimum SOC value for the i-th battery module cluster is set. This is the maximum SOC value set for the i-th battery module cluster; To control the duration of the cycle; Let be the total capacity of the battery modules in the i-th battery module cluster;

[0049] The following formula is used as the SOH safety range constraint: In the formula This is the set critical health threshold for the battery module cluster.

[0050] The aging cost weighting value is set using the following formula. : In the formula The base value for setting the aging cost weighting value; This represents the rate of decay of the SOH value of the i-th battery module cluster; The maximum SOH decay rate of the battery module cluster.

[0051] This invention also provides a system for implementing the local power allocation method of the energy storage power station, comprising a data acquisition module, a data processing module, a battery clustering module, a priority calculation module, an allocation modeling module, and a power allocation module; the data acquisition module, data processing module, battery clustering module, priority calculation module, allocation modeling module, and power allocation module are connected in series; the data acquisition module is used to acquire data information of the battery modules of the target energy storage power station and upload the data information to the data processing module; the data processing module is used to preprocess the acquired data information according to the received data information and upload the data information to the battery clustering module; the battery clustering module is used to perform [further processing] on the battery modules of the target energy storage power station based on the received data information and the preprocessed data information, using the DBSCAN algorithm. Cluster analysis is performed, and the data is uploaded to the priority calculation module. The priority calculation module constructs a decision matrix based on the received data and the cluster analysis results, using the state of charge, state of health, and remaining capacity of the battery modules. It then calculates the priority of each clustered battery module cluster using the entropy weight method and uploads the data to the allocation modeling module. The allocation modeling module constructs a local power allocation model for the target energy storage power station based on the received data, with the objectives of loss, aging cost, state of charge balance, and power allocation optimization based on battery module priority. The data is then uploaded to the power allocation module. The power allocation module solves the constructed local power allocation model based on the received data and completes the local power allocation for the target energy storage power station based on the solution results.

[0052] The local power allocation method and system for energy storage power stations provided by this invention clusters the battery modules by acquiring data information of the battery modules in the energy storage power station, calculates the priority of the battery modules based on the clustering results, and constructs and solves a local power allocation model for the target energy storage power station with the objectives of loss, aging cost, state of charge balance, and power allocation optimization based on the priority of battery modules. This not only realizes the local power allocation of the energy storage power station, but also takes into account the characteristics of the battery modules themselves, resulting in higher reliability and better safety. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0054] Figure 2 This is a schematic diagram comparing the charge and discharge efficiency curves of an embodiment of the method of the present invention.

[0055] Figure 3 This is a schematic diagram comparing the SOC standard deviation curves of embodiments of the method of the present invention.

[0056] Figure 4 This is a schematic diagram comparing the SOH decay curves of an embodiment of the method of the present invention.

[0057] Figure 5 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0058] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The local power allocation method for energy storage power stations disclosed in this invention includes the following steps:

[0059] S1. Obtain data information of the battery modules of the target energy storage power station; specifically including the following steps:

[0060] Acquire data information from the battery modules of the target energy storage power station; the data information includes state of charge (SOC), state of health (SOH), and internal resistance. .

[0061] S2. Preprocess the data obtained in step S1; specifically, this includes the following steps:

[0062] The preprocessing process includes data cleaning, data interpolation, and data normalization;

[0063] Since the acquired data have different dimensions, the following formula is used for normalization to eliminate the impact of dimensional differences on subsequent analysis: In the formula X represents the normalized data; X represents the data before normalization. The minimum value of the data; The maximum value of the data;

[0064] This processing step maps data of different dimensions to a unified system. This interval provides a good foundation for subsequent data analysis and processing.

[0065] S3. Based on the preprocessed data obtained in step S2, perform cluster analysis on the battery modules of the target energy storage power station using the DBSCAN algorithm; including the following steps:

[0066] Using the pre-processed state of charge, state of health, and internal resistance as three-dimensional feature vectors, a feature space is constructed to comprehensively reflect the state of each battery module.

[0067] The DBSCAN algorithm is used to perform cluster analysis on the battery modules of the target energy storage power station:

[0068] During cluster analysis, weighted Euclidean distance is used to represent the similarity of each sample in the feature space; the neighborhood radius during clustering is adjusted according to the performance degradation effect of temperature and the stability of the battery module's health status; and the minimum number of samples for each cluster is set according to the total number of battery modules.

[0069] In practice, the following steps can be taken:

[0070] The similarity between any two samples x and y in the feature space is calculated using the following formula: In the formula The similarity between sample x and sample y; Let x be the SOC value of sample x; Let y be the SOC value of the sample y; Here is the SOH value of sample x; Here is the SOH value of sample y; Let x be the internal resistance value of sample x; Let y be the internal resistance value of the sample y; The weights are set for the SOC value. The weighting of the set SOH value, The internal resistance value is set as the weight, and In specific implementation, , and The value can be set by the researchers themselves;

[0071] Based on the performance degradation effect of temperature and the stability of the battery module's health status, the neighborhood radius during clustering is adjusted in real time using the following formula. : In the formula The set baseline radius value; The set weight value for health fluctuations; This serves as a stability indicator of the battery module's health status, and , This is the function for calculating standard deviation. This is historical data on the State of Health (SOH) of the battery module over the past 3 days; The set temperature correction weight value; This is the temperature correction factor for the battery module, and , These are the settings for temperature correction. This is the highest temperature of the battery module.

[0072] Based on the total number of battery modules, the minimum number of samples for each cluster is calculated using the following formula: In the formula The minimum number of samples for each cluster; This represents the total number of battery modules; This is a rounding up operation;

[0073] During clustering, for each sample point p, if the neighborhood radius of p... The number of samples within the range shall not be less than the minimum sample size. If the sample point p is selected as the cluster core, then the neighborhood radius of the cluster core p is considered as follows: If there are other clustering cores q, then clustering core q is added to the cluster corresponding to clustering core p, and clustering continues with clustering core q as the center;

[0074] Finally, the clustering is completed.

[0075] After clustering is completed, there may be some points in the feature space that are not assigned to any cluster. The battery modules corresponding to these points can be identified as abnormal modules, such as those with poor health, significantly different state of charge from other battery modules, or abnormal internal resistance. These battery modules can be marked as abnormal and then further testing or replacement can be performed.

[0076] Through the improved clustering process described above, noise points in the data can be automatically identified, and the battery modules can be accurately divided into several groups, such as high health high SOC group, high health low SOC group, low health high SOC group, and low health low SOC group. For each cluster, a targeted control strategy can be formulated.

[0077] The core significance of clustering lies in:

[0078] Simplify control complexity: By grouping modules with similar states into a cluster, the control object is reduced from a "single module" to a "cluster", reducing the number of optimization variables and ensuring that the power allocation model can be solved efficiently;

[0079] Achieve differentiated regulation: After clustering, precise strategies can be formulated for the characteristics of different clusters (such as high-health, high-SOC clusters and low-health, low-SOC clusters) to avoid overuse of high-health modules, while reducing the "barrel effect" through intra-cluster balancing.

[0080] Anomaly detection and management: Clustering can automatically identify abnormal modules (such as modules with SOH < 60% or abnormal internal resistance), which facilitates individual marking and repair, and improves system safety.

[0081] S4. Based on the clustering analysis results obtained in step S3, construct a decision matrix using the state of charge, state of health, and remaining capacity of the battery modules, and calculate the priority of each clustered battery module cluster using the entropy weight method; including the following steps:

[0082] A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the battery module as indicators.

[0083] The constructed decision matrix is ​​standardized.

[0084] Based on the standardized decision matrix, the entropy value and corresponding weight value of each indicator are calculated using the entropy weight method.

[0085] Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated.

[0086] In practice, the following steps can be taken:

[0087] A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the clustered battery module clusters as indicators.

[0088] Among them, the State of Charge (SOC) of the battery module cluster is the arithmetic mean of the SOCs of all battery modules in the cluster, reflecting the overall charge level of the cluster; the State of Health (SOH) of the battery module cluster is the arithmetic mean of the SOHs of all battery modules in the cluster, representing the overall health of the cluster; the Remaining Capacity of the battery module cluster is the sum of the remaining capacities of all battery modules in the cluster, used to measure the total energy storage capacity of the cluster; and the Internal Resistance of the battery module cluster is the sum of the internal resistances of all battery modules in the cluster, reflecting the overall resistance characteristics of the cluster.

[0089] The constructed decision matrix is ​​standardized to obtain the standardized decision matrix Q;

[0090] Based on the entropy weight method, the entropy value and corresponding weight value of each indicator are calculated using the following formula: , In the formula Let be the entropy value of the j-th index; m is the total number of battery module clusters. Let be the element in the i-th row and j-th column of the standardized decision matrix Q; Let be the weight value of the j-th indicator;

[0091] Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated using the following formula: In the formula This represents the priority of the i-th battery module cluster.

[0092] By calculating the above priorities, the control priorities of each module cluster can be objectively quantified based on battery state data, enabling dynamic and differentiated power allocation strategies. Its core value lies in: ① Integrating the objective weighting method of entropy weighting to adapt to the dynamic changes in battery state and avoid subjective decision-making bias; ② Coordinating multiple objectives such as lifespan, efficiency, safety, and balance, optimizing energy allocation through priority ranking, which improves system performance and delays battery aging; ③ Combining clustering to simplify control complexity, ensuring real-time performance and operability in large-scale energy storage scenarios, providing quantitative decision support for the refined management of energy storage systems, and ultimately significantly enhancing the system's reliability, economy, and full life cycle operating efficiency.

[0093] S5. Construct a local power allocation model for the target energy storage power station, with the objectives of optimizing power allocation based on battery module priority, considering losses, aging costs, state-of-charge balance, and priority-based power allocation. This includes the following steps:

[0094] The priority weight factor of each battery module cluster is calculated based on the priority of each battery module cluster, and the power allocation optimization of the priority of each battery module cluster is calculated based on the priority weight factor of each battery module cluster.

[0095] Using battery module cluster losses, aging costs, state of charge balance, and priority power allocation optimization as objective functions, and total power balance, power limits, SOC safety range, and SOH safety range as constraints, a local power allocation model for the target energy storage power station is constructed.

[0096] In practice, the following steps can be taken:

[0097] The following formula is used as the objective function of the local power allocation model for the target energy storage power station: In the formula Let be the power value of the i-th battery module cluster; Let be the equivalent internal resistance value of the i-th battery module cluster; Let SOH be the SOH value of the i-th battery module cluster; is the standard deviation of the SOC values ​​of the battery modules contained in the i-th battery module cluster; Let be the priority weight factor for the i-th battery module cluster, and , This represents the maximum priority of the battery module cluster. The set aging cost weight value (preferably 0.7, which can be set by yourself or by adopting the subsequent scheme); The set weight value for the state of charge balance (preferably 0.3); Optimize the weight values ​​for power allocation based on the set priorities;

[0098] In the objective function, This is a loss item for the battery module cluster, which reflects the loss of electrical energy converted into heat energy; This is an aging cost item that reflects the aging status of the battery module cluster; This is the state of charge balance term, which reflects the difference in SOC values ​​within the cluster. The purpose of adding this term is to prevent capacity waste caused by the "barrel effect". This is a priority power allocation optimization term. The purpose of adding this term is to allow high-priority battery module clusters to take on more power allocation results.

[0099] The following formula is used as a constraint condition for the local power allocation model of the target energy storage power station:

[0100] The following formula is used as the total power balance constraint: In the formula The power regulation command value for the target energy storage power station;

[0101] The following formula is used as the power limitation constraint: In the formula The maximum allowable power of the i-th battery module cluster is set.

[0102] The following formula is used as the SOC safety range constraint: In the formula The minimum SOC value for the i-th battery module cluster is set (preferably 5%). The maximum SOC value of the i-th battery module cluster is set (preferably 95%). To control the duration of the cycle; Let be the total capacity of the battery modules in the i-th battery module cluster;

[0103] The following formula is used as the SOH safety range constraint: In the formula The critical health threshold (preferably 60%) is set for the battery module cluster; this constraint is used to ensure that aging battery module clusters are not overused.

[0104] In addition, regarding the set aging cost weight value The following formula can be used to calculate it: In the formula The base value for setting the aging cost weighting value; This represents the rate of decay of the SOH value of the i-th battery module cluster; The maximum SOH decay rate of the battery module cluster.

[0105] S6. Solve the local power allocation model constructed in step S5, and complete the local power allocation of the target energy storage power station based on the solution results; in specific implementation, a quadratic programming scheme can be used to solve the model.

[0106] The method of the present invention will be further described below with reference to an embodiment:

[0107] Experimental setup:

[0108] Dataset: A 100kWh energy storage system containing 10 battery module clusters, simulating different SOH and SOC states; initial SOC range 30%-70%, SOH range 60%-95%, internal resistance 10-30 Ω. ;

[0109] The comparison is between the present invention and the traditional method; the traditional method is a uniform charging and discharging scheme for all battery modules, which does not distinguish between states and distributes power evenly, and ignores the individual differences of battery modules.

[0110] Evaluation metrics: charge / discharge efficiency, SOC standard deviation, SOH decay rate, and overcharge / over-discharge cycles.

[0111] Experimental results:

[0112] After 100 charge-discharge cycles, the final evaluation metrics are shown in Table 1:

[0113] Table 1. Schematic diagram of evaluation indicators

[0114]

[0115] As shown in Table 1, the method of this invention significantly outperforms the traditional method in all evaluation indicators: In terms of charge / discharge efficiency, the method of this invention achieves 92.5%, far exceeding the 85.3% of the traditional method, indicating higher energy conversion efficiency; in terms of SOC standard deviation, the method of this invention is only 3.2%, far less than the 8.7% of the traditional method, indicating that this invention can better achieve balanced state of charge of the battery module; in terms of SOH decay rate, the method of this invention is 1.2% / 100 cycles, significantly lower than the 3.5% / 100 cycles of the traditional method, demonstrating that this invention can effectively slow down the aging rate of the battery module; in terms of safety, the method of this invention did not exhibit overcharge or over-discharge phenomena, while the traditional method showed 12 instances of overcharge and over-discharge, fully demonstrating the superior safety of the method of this invention. Overall, the method of this invention, by considering the individual characteristics of the battery module and implementing differentiated control, demonstrates significant advantages in improving efficiency, balance, delaying aging, and ensuring safety.

[0116] Comparison curves as follows Figures 2-4 As shown:

[0117] pass Figure 2 As can be seen, the efficiency of the present invention is stable at 92%-93%, while the efficiency of the comparative scheme fluctuates greatly (85%-87%); this indicates that the high internal resistance clusters in the comparative scheme frequently participate, leading to increased losses.

[0118] pass Figure 3 As can be seen, the standard deviation of SOC in the present invention rapidly decreased to 3% and remained there, while the standard deviation of the comparative scheme was always >8%. This indicates that the comparative scheme did not perform differentiated control, resulting in over-expansion of high SOC clusters and under-expansion of low SOC clusters, forming a "barrel effect".

[0119] pass Figure 4 As can be seen, the SOH decay of the present invention is slow, while the SOH decay of the comparative scheme is faster; this indicates that the low SOH clusters are overused in the comparative scheme, which accelerates the aging of the battery module.

[0120] like Figure 5The diagram shows the functional modules of the system of this invention: The system for implementing the local power allocation method of the energy storage power station disclosed in this invention includes a data acquisition module, a data processing module, a battery clustering module, a priority calculation module, an allocation modeling module, and a power allocation module; these modules are connected in series. The data acquisition module acquires data information of the battery modules of the target energy storage power station and uploads the data information to the data processing module. The data processing module preprocesses the acquired data information based on the received data information and uploads the data information to the battery clustering module. The battery clustering module, based on the received data information and the preprocessed data information, performs priority calculation on the target energy storage power station using the DBSCAN algorithm. The battery modules of the power storage station undergo cluster analysis, and the data is uploaded to the priority calculation module. The priority calculation module, based on the received data and the cluster analysis results, constructs a decision matrix using the battery module's state of charge, state of health, and remaining capacity. It then calculates the priority of each clustered battery module using the entropy weight method and uploads this data to the allocation modeling module. The allocation modeling module, based on the received data, constructs a local power allocation model for the target power storage station, targeting losses, aging costs, state of charge balance, and power allocation optimization based on battery module priorities. This model is then uploaded to the power allocation module. Finally, the power allocation module solves the constructed local power allocation model based on the received data and completes the local power allocation for the target power storage station based on the solution results.

Claims

1. A method for local power allocation in an energy storage power station, characterized in that... Includes the following steps: S1. Obtain data information of the battery modules of the target energy storage power station; S2. Preprocess the data obtained in step S1; S3. Based on the preprocessed data information obtained in step S2, perform cluster analysis on the battery modules of the target energy storage power station using the DBSCAN algorithm; S4. Based on the clustering analysis results obtained in step S3, construct a decision matrix using the state of charge, state of health, and remaining capacity of the battery modules, and calculate the priority of each clustered battery module cluster using the entropy weight method. S5. With the goals of optimizing power allocation based on battery module priority, construct a local power allocation model for the target energy storage power station; S6. Solve the local power allocation model constructed in step S5, and complete the local power allocation of the target energy storage power station based on the solution results.

2. The local power allocation method for an energy storage power station according to claim 1, characterized in that... Step S1 specifically includes the following steps: Acquire data information from the battery modules of the target energy storage power station; the data information includes state of charge (SOC), state of health (SOH), and internal resistance. ; Step S2 specifically includes the following steps: The preprocessing process includes data cleaning, data interpolation, and data normalization; The data normalization mentioned above is specifically performed using the following formula: In the formula X represents the normalized data; X represents the data before normalization. The minimum value of the data; This represents the maximum value of the data.

3. The local power allocation method for an energy storage power station according to claim 2, characterized in that... Step S3 includes the following steps: The preprocessed state of charge, state of health, and internal resistance are used as three-dimensional feature vectors to construct a feature space; The DBSCAN algorithm is used to perform cluster analysis on the battery modules of the target energy storage power station: During cluster analysis, weighted Euclidean distance is used to represent the similarity of each sample in the feature space; the neighborhood radius during clustering is adjusted according to the performance degradation effect of temperature and the stability of the battery module's health status; and the minimum number of samples for each cluster is set according to the total number of battery modules.

4. The local power allocation method for an energy storage power station according to claim 3, characterized in that... Step S3 specifically includes the following steps: The similarity between any two samples x and y in the feature space is calculated using the following formula: In the formula The similarity between sample x and sample y; Let x be the SOC value of sample x; Let y be the SOC value of the sample y; Here is the SOH value of sample x; Here is the SOH value of sample y; Let x be the internal resistance value of sample x; Let y be the internal resistance value of the sample y; The weights are set for the SOC value. The weighting of the set SOH value, The internal resistance value is set as the weight, and ; Based on the performance degradation effect of temperature and the stability of the battery module's health status, the neighborhood radius during clustering is adjusted in real time using the following formula. : In the formula The set baseline radius value; The set weight value for health fluctuations; This serves as a stability indicator of the battery module's health status, and , This is the function for calculating standard deviation. This is historical data on the State of Health (SOH) of the battery module over the past 3 days; The set temperature correction weight value; This is the temperature correction coefficient for the battery module, and , These are the settings for temperature correction. This is the highest temperature of the battery module. Based on the total number of battery modules, the minimum number of samples for each cluster is calculated using the following formula: In the formula The minimum number of samples for each cluster; This represents the total number of battery modules; This is a rounding up operation; During clustering, for each sample point p, if the neighborhood radius of p... The number of samples within the range is no less than the minimum sample size. If p is a clustering point, then sample point p will be used as the clustering core. If the neighborhood radius of the cluster core p If there are other clustering cores q, then clustering core q is added to the cluster corresponding to clustering core p, and clustering continues with clustering core q as the center; Finally, the clustering is completed.

5. The local power allocation method for an energy storage power station according to claim 4, characterized in that... Step S4 includes the following steps: A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the battery module as indicators. The constructed decision matrix is ​​standardized. Based on the standardized decision matrix, the entropy value and corresponding weight value of each indicator are calculated using the entropy weight method. Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated.

6. The local power allocation method for an energy storage power station according to claim 5, characterized in that... Step S4 specifically includes the following steps: A decision matrix is ​​constructed using the state of charge (SOC), state of health (SOH), and remaining capacity of the clustered battery module clusters as indicators. Among them, the State of Charge (SOC) of the battery module cluster is the arithmetic mean of the SOCs of all battery modules in the cluster; the State of Health (SOH) of the battery module cluster is the arithmetic mean of the SOHs of all battery modules in the cluster; the Remaining Capacity of the battery module cluster is the sum of the remaining capacities of all battery modules in the cluster; and the Internal Resistance of the battery module cluster is the sum of the internal resistances of all battery modules in the cluster. The constructed decision matrix is ​​standardized to obtain the standardized decision matrix Q; Based on the entropy weight method, the entropy value and corresponding weight value of each indicator are calculated using the following formula: , In the formula Let be the entropy value of the j-th index; m is the total number of battery module clusters. Let be the element in the i-th row and j-th column of the standardized decision matrix Q; Let be the weight value of the j-th indicator; Based on the weight values ​​of each indicator and the constructed decision matrix, the priority of each clustered battery module cluster is calculated using the following formula: In the formula This represents the priority of the i-th battery module cluster.

7. The local power allocation method for an energy storage power station according to claim 6, characterized in that... Step S5 includes the following steps: The priority weight factor of each battery module cluster is calculated based on the priority of each battery module cluster, and the power allocation optimization of the priority of each battery module cluster is calculated based on the priority weight factor of each battery module cluster. Using battery module cluster losses, aging costs, state of charge balance, and priority power allocation optimization as objective functions, and total power balance, power limits, SOC safety range, and SOH safety range as constraints, a local power allocation model for the target energy storage power station is constructed.

8. The local power allocation method for an energy storage power station according to claim 7, characterized in that... Step S5 specifically includes the following steps: The following formula is used as the objective function of the local power allocation model for the target energy storage power station: In the formula Let be the power value of the i-th battery module cluster; Let be the equivalent internal resistance value of the i-th battery module cluster; Let SOH be the SOH value of the i-th battery module cluster; is the standard deviation of the SOC values ​​of the battery modules contained in the i-th battery module cluster; Let be the priority weight factor for the i-th battery module cluster, and , This represents the maximum priority of the battery module cluster. The set aging cost weight value; The set weight value for the state of charge balance; Optimize the weight values ​​for power allocation based on the set priorities; The following formula is used as a constraint condition for the local power allocation model of the target energy storage power station: The following formula is used as the total power balance constraint: In the formula The power regulation command value for the target energy storage power station; The following formula is used as the power limitation constraint: In the formula The maximum allowable power of the i-th battery module cluster is set; The following formula is used as the SOC safety range constraint: In the formula The minimum SOC value for the i-th battery module cluster is set. This is the maximum SOC value set for the i-th battery module cluster; To control the duration of the cycle; Let be the total capacity of the battery modules in the i-th battery module cluster; The following formula is used as the SOH safety range constraint: In the formula This is the set critical health threshold for the battery module cluster.

9. The local power allocation method for an energy storage power station according to claim 8, characterized in that... The aging cost weighting value is set using the following formula. : In the formula The base value for setting the aging cost weighting value; This represents the rate of decay of the SOH value of the i-th battery module cluster; The maximum SOH decay rate of the battery module cluster.

10. A system for implementing the local power allocation method for an energy storage power station as described in any one of claims 1 to 9, characterized in that... It includes a data acquisition module, a data processing module, a battery clustering module, a priority calculation module, an allocation modeling module, and a power allocation module; the data acquisition module, data processing module, battery clustering module, priority calculation module, allocation modeling module, and power allocation module are connected in series; the data acquisition module is used to acquire data information of the battery modules of the target energy storage power station and upload the data information to the data processing module; The data processing module is used to preprocess the acquired data information based on the received data information and upload the data information to the battery clustering module; the battery clustering module is used to perform cluster analysis on the battery modules of the target energy storage power station based on the received data information and the preprocessed data information, and upload the data information to the priority calculation module. The priority calculation module is used to construct a decision matrix based on the received data information and the obtained clustering analysis results, using the state of charge, state of health and remaining capacity of the battery module, and calculate the priority of each clustered battery module cluster by combining the entropy weight method, and upload the data information to the allocation modeling module. The power allocation modeling module is used to construct a local power allocation model for the target energy storage power station based on the received data information, with the objectives of loss, aging cost, state of charge balance and power allocation optimization based on battery module priority, and upload the data information to the power allocation module; the power allocation module is used to solve the constructed local power allocation model based on the received data information, and complete the local power allocation of the target energy storage power station based on the solution results.

Images