A grid-forming battery energy storage system and control method thereof

Abstract

The application discloses a network-constructing type battery energy storage system and a control method thereof, relates to the technical field of battery energy storage, and generates a characteristic vector by preprocessing electrical parameters and operating state parameters; analyzes offset characteristics based on the characteristic vector and carries out characteristic drift marking; analyzes spacing characteristics of each battery energy storage device, and carries out clustering analysis according to the spacing characteristics; analyzes the equivalent capacity of each cluster in the clustering result; acquires a historical regulation and control record set, and analyzes the correlation degree between any two time points; analyzes the composite energy fluctuation value of the current time point, analyzes the dynamic regulation and control threshold in combination with the correlation degree of the real time and the historical time, analyzes the matching degree index of each cluster in combination with the equivalent capacity and the total regulation and control instruction, generates an ordered cluster index sequence after sorting, and carries out hierarchical regulation and control instruction analysis on the ordered cluster index sequence; the distribution proportion is dynamically adjusted, resource distribution is optimized, and regulation and control risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage, and in particular to a grid-type battery energy storage system and a control method thereof. Background Art

[0002] With the increasing penetration of new energy and power electronics, power systems are experiencing a trend of decreasing inertia and weakening system strength, leading to increasingly severe stability issues. Energy storage systems play a role in power grids, including providing active and reactive power support, improving renewable energy grid integration, participating in peak and frequency regulation, and providing short-term power during faults. Using grid-connected control technology to control energy storage converters can improve the stability of new power systems.

[0003] In scenarios such as equipment impedance fluctuations and response time variations, static clustering can easily lead to overloaded or idle equipment, preventing balanced capacity distribution. The correlation between historical control experience and real-time operating conditions is not effectively explored, and control thresholds often rely on manual settings or fixed rules, making it difficult to cope with the randomness and suddenness of grid power fluctuations. Existing strategies lack dynamic quantitative analysis of cluster matching and priority, which can easily lead to control command conflicts or resource allocation mismatches, exacerbating equipment losses and system oscillations.

[0004] Therefore, the present invention discloses a grid-type battery energy storage system and a control method thereof to solve the above problems. Summary of the Invention

[0005] The object of the present invention is to provide a grid-connected battery energy storage system and a control method thereof to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a grid-type battery energy storage control method, the method comprising the following steps:

[0007] S1: Real-time collection of electrical parameters and operating status parameters of energy storage devices, pre-processing of these parameters to generate feature vectors; analysis of offset features based on the feature vectors, and marking of feature drift;

[0008] S2: Analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; analyze the equivalent capacity of each cluster in the clustering results;

[0009] S3: Obtain a set of historical control records and analyze the correlation between any two moments. Extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the correlation between the real-time moment and the historical moment.

[0010] S4: Analyze the matching index of each cluster based on the equivalent capacity and the total control instructions, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence.

[0011] According to the above scheme, S1 includes the following contents:

[0012] S101: Obtain electrical parameters and dynamic operating parameters of a grid-type battery energy storage device, wherein the electrical parameters include equivalent impedance and phase angle; the dynamic operating parameters include power change rate and response time; normalize the electrical parameters and dynamic operating parameters respectively to generate a characteristic vector of the battery energy storage device, and record the characteristic vector of the i-th device at time t as: V i (t) = [(log Z i (t)) / Z0,θ i (t) / π,tanh((dP i / dt) / △P max ), 1 / T i ]; among them, Z i (t) represents the equivalent impedance of the i-th battery energy storage device at time t; Z0 represents the impedance reference value; θ i (t) represents the phase angle of the i-th battery energy storage device at time t; the phase angle represents the phase difference between the voltage and current when the battery energy storage device is connected to the grid; dP i / dt represents the power change rate of the i-th battery energy storage device at time t; △P max Indicates the maximum power change rate; T i represents the average response time of the i-th battery energy storage device; the eigenvectors of all battery energy storage devices are concatenated to obtain the eigenvector matrix at time t;

[0013] This application linearizes the impedance change, limits the power change rate within a range, compresses the parameter range, suppresses outliers, and improves model stability.

[0014] S102: The system presets the time window length and analyzes the offset feature D(t) between the end feature matrix of the current window and the end matrix of the previous window based on the time window:

[0015] ;

[0016] Among them, V （i，l） (t) represents the lth feature of the i-th battery energy storage device at time t; V （i，l） (t-△t) represents the lth feature of the i-th battery energy storage device at time t-△t; i∈[1,I], I represents the total number of battery energy storage devices; l∈[1,L], L represents the total number of features; △t represents the length of the time window;

[0017] If the offset feature is greater than the offset feature threshold D th (t), marking feature drift F drift (t)=1; otherwise F drift (t)=0;

[0018] ;

[0019] ;

[0020] Among them, μ D (t) represents the first shift feature threshold at time t, υ D (t) represents the second shift feature threshold at time t; σ D (t) represents the third shift feature threshold at time t.

[0021] The offset feature calculation based on the time window can capture the dynamic changes of the system state in real time. The trigger mechanism provides a reliable basis for subsequent clustering updates, ensuring the system's rapid response to sudden changes.

[0022] According to the above scheme, S2 includes the following contents:

[0023] S201: When the characteristic drift F is detected drift When (t)=1, the clustering update is triggered; otherwise, the clustering result of the previous moment is used; the first spacing feature of each battery energy storage device is analyzed, and the first spacing feature of the i-th battery energy storage device is recorded as ρ i :

[0024] ;

[0025] Where, exp() represents the exponential function with natural numbers as the base; ||V i -V j ||2 represents the eigenvector V i and the eigenvector V j The spacing, ||·||2 represents the L2 norm calculation function; A represents the spacing characteristic coefficient; the spacing characteristic coefficient is equal to the characteristic vector V i and the eigenvector V j The product of the standard deviation of the spacing and a preset constant;

[0026] Analyze the second spacing feature of each battery energy storage device and record the second spacing feature of the i-th battery energy storage device as δ i ;

[0027] If the first spacing feature of the i-th battery energy storage device is not a maximum value, then all battery energy storage devices whose first spacing feature is greater than the i-th battery energy storage device are extracted to form a spacing analysis set of the i-th battery energy storage device; the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of the battery energy storage device in the corresponding spacing analysis set; if the first spacing feature of the i-th battery energy storage device is a maximum value, then the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of any battery energy storage device;

[0028] Recording battery energy storage devices that simultaneously meet the first spacing feature and the second spacing feature greater than the corresponding threshold as central battery energy storage devices; dividing non-central battery energy storage devices into corresponding central battery energy storage devices according to the minimum value of the feature vector spacing; traversing all non-central battery energy storage devices to generate the final clustering results;

[0029] This application uses exponential function weighted distance to amplify local density differences, highlight high-density areas, and enhance the accuracy of cluster center identification; combined with maximum spacing analysis, it effectively distinguishes edge devices from isolated points and avoids noise interference.

[0030] S202: Analyze the centroid vector of each cluster and record the centroid vector of the mth cluster as V centroid m ;

[0031] ;

[0032] Among them, C m represents the mth cluster; |C m | represents the number of battery energy storage devices in the mth cluster, V （m，n） The feature vector representing the nth battery energy storage device in the mth cluster;

[0033] The equivalent capacity of each cluster is analyzed by weight decay accumulation based on the centroid vector.

[0034] ;

[0035] Among them, C m ceff represents the equivalent capacity of the mth cluster; C （m，n） represents the rated capacity of the nth battery energy storage device in the mth cluster, B represents the attenuation coefficient, and the attenuation coefficient is equal to the product of the spacing characteristic coefficient and a preset constant; the equivalent capacities of all clusters are normalized.

[0036] The capacity weight is dynamically adjusted based on the distance between the device and the center of mass. The farther the device is, the smaller its contribution is, reflecting the principle of "proximity balance". The normalization of equivalent capacity simplifies subsequent regulation and allocation, ensuring a reasonable capacity ratio for each cluster.

[0037] According to the above solution, S3 contains the following:

[0038] S301: Obtain a set of historical control records from the database, denoted as {H r =(t r , △P r , △Q r , C m (t r ))|r∈[1,R]}, R represents the total number of historical control records; where t r represents the rth historical regulation moment, △P r Indicates the rth active power control amount, △Q r Represents the rth reactive power control amount, C m (t r ) represents all clustering grouping sets at the corresponding moment; get the clustering grouping set at the current moment, denoted as {C m (t)|m∈[1,M]}, M represents the total number of clusters at the current moment; analyze the correlation value between any two moments t and t';

[0039] ;

[0040] Wherein, λ represents the correlation coefficient, which is a system preset constant; K() represents the indicator function; if C m (t)∩C m (t') there is at least one identical battery energy storage device, then K(C m (t)∩C m (t')) = 1; if C m (t)∩C m (t') is an empty set, then K(C m (t)∩C m (t')) = 0; the historical moment sequence {t r |r∈[1, R]} corresponding to the incidence matrix is ​​recorded as K(t)={K(t, t r )|r∈[1,R]};

[0041] S302: Extract real-time power fluctuations and analyze the composite energy fluctuation value at the current moment, where the composite energy fluctuation value is equal to the product of the arithmetic square root of the sum of the squares of the real-time active power and the real-time reactive power and the maximum correlation value; analyze the dynamic control threshold based on the composite energy fluctuation at the current moment, where the dynamic control threshold is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; the first smoothing coefficient and the second smoothing coefficient are system preset constants.

[0042] Through the correlation matrix of historical control records and current clusters, the control experience under similar working conditions is mined to provide a reference for the dynamic threshold; the influence weight of historical data on current decision-making is adjusted to balance real-time and experience; the real-time power fluctuation and the maximum correlation value are combined to highlight the impact of key historical events; the current fluctuation and long-term trend are integrated through the smoothing coefficient, so that the threshold can quickly respond to sudden changes while avoiding short-term noise interference.

[0043] According to the above scheme, S4 includes the following contents:

[0044] S401: Obtain the total control instruction at time t, which includes the total control amount of active power and reactive power; analyze the matching index of each cluster, and mark the matching index of the mth cluster as M m ;

[0045] ;

[0046] Among them, △P req Indicates the total active power control quantity, △Q req Represents the total reactive power control quantity, P total Represents the total active rated capacity, which is equal to the sum of the rated capacities of all battery energy storage devices. total Represents the total reactive rated capacity, which is equal to the total active rated capacity; C m * (t) represents the normalized value of the equivalent capacity of the mth cluster; all matching indexes are arranged in descending order to obtain an ordered cluster index sequence;

[0047] S402: If the arithmetic square root of the sum of the squares of the total active and reactive control quantities is greater than the corresponding dynamic control threshold, hierarchical control is triggered, and hierarchical control instruction analysis is performed based on the ordered cluster index sequence, and cluster m is k The hierarchical control instructions are recorded as ; where k represents the sequence number of the cluster in the ordered cluster index sequence, Represents cluster m k The active power control amount, Represents cluster m k Reactive power control amount;

[0048] ;

[0049] ;

[0050] Among them, R P Indicates the remaining active power control demand, R Q Indicates the remaining reactive power control demand, Represents cluster m k The maximum available active capacity, Represents cluster m k The maximum available reactive power capacity, Represents cluster m k The variance of the capacity distribution; sign() represents the directional function. If the directional function content is greater than 0, the output is +1; if the directional function content is less than 0, the output is -1; if the directional function content is greater than 0, the output is 0; the maximum available active capacity is equal to the product of the normalized value of the corresponding equivalent capacity and the total active rated capacity; the maximum available reactive capacity is equal to the product of the normalized value of the corresponding equivalent capacity and the total reactive rated capacity.

[0051] Matching control requirements with cluster capacity ensures that large-capacity clusters take priority in control tasks and optimizes resource allocation. Dynamically adjust the allocation ratio based on remaining demand, available capacity, and variance, reducing allocation to clusters with large variance and reducing control risks.

[0052] In another aspect of the present application, a grid-type battery energy storage system is provided, which is applied to the above-mentioned grid-type battery energy storage control method. The system includes an energy storage device offset analysis module, a device clustering capacity analysis module, a correlation threshold analysis module, and a regulation analysis module.

[0053] The energy storage device offset analysis module is used to collect electrical parameters and operating status parameters of the energy storage device in real time, pre-process the electrical parameters and operating status parameters, and generate feature vectors; analyze the offset characteristics based on the feature vectors, and perform feature drift marking;

[0054] The device cluster capacity analysis module is used to analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; analyze the equivalent capacity of each cluster in the clustering results;

[0055] The correlation threshold analysis module is used to obtain a set of historical control records and analyze the degree of correlation between any two moments; extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the degree of correlation between the real-time moment and the historical moment;

[0056] The control analysis module is used to analyze the matching index of each cluster in combination with the equivalent capacity and the total control instruction, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence.

[0057] According to the above solution, the energy storage device offset analysis module includes a feature vector analysis unit and an offset analysis unit;

[0058] The eigenvector analysis unit is used to obtain electrical parameters and dynamic operating parameters of the grid-type battery energy storage device, normalize the electrical parameters and dynamic operating parameters respectively, generate eigenvectors of the battery energy storage device, and concatenate the eigenvectors of all battery energy storage devices to obtain a eigenvector matrix;

[0059] The offset analysis unit is used to analyze the offset feature between the current window end feature matrix and the previous window end matrix based on the time window, and if the offset feature is greater than the offset feature threshold, mark the feature drift.

[0060] According to the above solution, the device clustering capacity analysis module includes a device clustering unit and an equivalent capacity analysis unit;

[0061] The device clustering unit is used to analyze the first spacing feature and the second spacing feature of the battery energy storage device; record the battery energy storage device that satisfies both the first spacing feature and the second spacing feature greater than the corresponding threshold as the central battery energy storage device; divide the non-central battery energy storage devices into corresponding central battery energy storage devices according to the minimum value of the feature vector spacing; traverse all non-central battery energy storage devices to generate a final clustering result;

[0062] The equivalent capacity analysis unit is used to analyze the centroid vector of each cluster, analyze the equivalent capacity of each cluster based on the centroid vector by using weighted decay accumulation, and normalize the equivalent capacities of all clusters.

[0063] According to the above solution, the correlation threshold analysis module includes a correlation analysis unit and a dynamic control threshold analysis unit;

[0064] The association analysis unit is used to obtain a set of historical control records from a database and analyze the association value based on the cluster grouping set at two moments;

[0065] The dynamic control threshold analysis unit is used to extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the composite energy fluctuation at the current moment. The dynamic control threshold is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; the first smoothing coefficient and the second smoothing coefficient are system preset constants.

[0066] According to the above solution, the control analysis module includes a control sequence analysis unit and a hierarchical control instruction analysis unit;

[0067] The control sequence analysis unit is used to obtain the total control instruction and analyze the matching index of each cluster in combination with the equivalent capacity normalized value;

[0068] The hierarchical control instruction analysis unit is used to trigger hierarchical control if the arithmetic square root of the sum of the squares of the total active and reactive control quantities is greater than the corresponding dynamic control threshold, and perform hierarchical control instruction analysis based on the ordered cluster index sequence.

[0069] Compared with the prior art, the present invention has the following beneficial effects: the present invention linearizes impedance changes, limits the power change rate within a certain range, compresses the parameter range, suppresses outliers, and improves model stability; the offset feature calculation based on the time window can capture the dynamic changes of the system state in real time, and the trigger mechanism provides a reliable basis for subsequent clustering updates, ensuring the system's rapid response to sudden changes; the present invention uses exponential function weighted distance to amplify local density differences, highlight high-density areas, and enhance the accuracy of cluster center identification; combined with maximum spacing analysis, it effectively distinguishes edge devices from isolated points and avoids noise interference; the capacity weight is dynamically adjusted according to the distance between the device and the centroid, and the farther the device is, the smaller the contribution, reflecting the principle of "proximity balance"; Normalization of equivalent capacity simplifies subsequent control allocation and ensures a reasonable capacity ratio for each cluster. Through the correlation matrix of historical control records and current clusters, control experience under similar working conditions is mined to provide a reference for dynamic thresholds. The influence weight of historical data on current decisions is adjusted to balance real-time and experience. The impact of key historical events is highlighted by combining real-time power fluctuations with the maximum correlation value. The current fluctuations and long-term trends are integrated through the smoothing coefficient, so that the threshold can respond quickly to sudden changes while avoiding short-term noise interference. The control demand is matched with the cluster capacity to ensure that large-capacity clusters take priority in control tasks and optimize resource allocation. The allocation ratio is dynamically adjusted based on the remaining demand, available capacity and variance, and the allocation of clusters with large variance is reduced to reduce the control risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] 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:

[0071] Figure 1 A schematic flow chart of a grid-type battery energy storage control method according to the present invention;

[0072] Figure 2 This is a structural diagram of a grid-type battery energy storage system of the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] See also Figure 1 The present invention provides a technical solution: a grid-type battery energy storage control method, the method comprising the following steps:

[0075] S1: Real-time collection of electrical parameters and operating status parameters of energy storage devices, pre-processing of these parameters to generate feature vectors; analysis of offset features based on the feature vectors, and marking of feature drift;

[0076] S2: Analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; analyze the equivalent capacity of each cluster in the clustering results;

[0077] S3: Obtain a set of historical control records and analyze the correlation between any two moments. Extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the correlation between the real-time moment and the historical moment.

[0078] S4: Analyze the matching index of each cluster based on the equivalent capacity and the total control instructions, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence.

[0079] S1 contains the following:

[0080] S101: Obtain electrical parameters and dynamic operating parameters of the grid-type battery energy storage device. The electrical parameters include equivalent impedance and phase angle; the dynamic operating parameters include power change rate and response time. Normalize the electrical parameters and dynamic operating parameters to generate a feature vector of the battery energy storage device. The feature vector of the i-th device at time t is recorded as: V i (t) = [(log Z i (t)) / Z0,θ i (t) / π,tanh((dP i / dt) / △P max ), 1 / T i ]; among them, Z i (t) represents the equivalent impedance of the i-th battery energy storage device at time t; Z0 represents the impedance reference value; θ i (t) represents the phase angle of the i-th battery energy storage device at time t; the phase angle represents the phase difference between the voltage and current when the battery energy storage device is connected to the grid; dPi / dt represents the power change rate of the i-th battery energy storage device at time t; △P max Indicates the maximum power change rate; T i represents the average response time of the i-th battery energy storage device; the eigenvectors of all battery energy storage devices are concatenated to obtain the eigenvector matrix at time t;

[0081] S102: The system presets the time window length and analyzes the offset feature D(t) between the end feature matrix of the current window and the end matrix of the previous window based on the time window:

[0082] ;

[0083] Among them, V （i，l） (t) represents the lth feature of the i-th battery energy storage device at time t; V （i，l） (t-△t) represents the lth feature of the i-th battery energy storage device at time t-△t; i∈[1,I], I represents the total number of battery energy storage devices; l∈[1,L], L represents the total number of features; △t represents the length of the time window;

[0084] If the offset feature is greater than the offset feature threshold D th (t), marking feature drift F drift (t)=1; otherwise F drift (t)=0;

[0085] ;

[0086] ;

[0087] Among them, μ D (t) represents the first shift feature threshold at time t, υ D (t) represents the second shift feature threshold at time t; σ D (t) represents the third shift feature threshold at time t.

[0088] S2 contains the following:

[0089] S201: When the characteristic drift F is detected drift When (t)=1, the clustering update is triggered; otherwise, the clustering result of the previous moment is used; the first spacing feature of each battery energy storage device is analyzed, and the first spacing feature of the i-th battery energy storage device is recorded as ρ i :

[0090] Where, exp() represents the exponential function with natural numbers as the base; ||V i -V j ||2 represents the eigenvector Vi and the eigenvector V j The spacing, ||·||2 represents the L2 norm calculation function; A represents the spacing characteristic coefficient; the spacing characteristic coefficient is equal to the characteristic vector V i and the eigenvector V j The product of the standard deviation of the spacing and a preset constant;

[0091] Analyze the second spacing feature of each battery energy storage device and record the second spacing feature of the i-th battery energy storage device as δ i ;

[0092] If the first spacing feature of the i-th battery energy storage device is not a maximum value, then all battery energy storage devices whose first spacing feature is greater than the i-th battery energy storage device are extracted to form a spacing analysis set of the i-th battery energy storage device; the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of the battery energy storage device in the corresponding spacing analysis set; if the first spacing feature of the i-th battery energy storage device is a maximum value, then the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of any battery energy storage device;

[0093] Recording battery energy storage devices that simultaneously meet the first spacing feature and the second spacing feature greater than the corresponding threshold as central battery energy storage devices; dividing non-central battery energy storage devices into corresponding central battery energy storage devices according to the minimum value of the feature vector spacing; traversing all non-central battery energy storage devices to generate the final clustering results;

[0094] S202: Analyze the centroid vector of each cluster and record the centroid vector of the mth cluster as V centroid m ;

[0095] ;

[0096] Among them, C m represents the mth cluster; |C m | represents the number of battery energy storage devices in the mth cluster, V （m，n） The feature vector representing the nth battery energy storage device in the mth cluster;

[0097] The equivalent capacity of each cluster is analyzed by weight decay accumulation based on the centroid vector.

[0098] ;

[0099] Among them, C m ceff represents the equivalent capacity of the mth cluster; C （m，n）represents the rated capacity of the nth battery energy storage device in the mth cluster, B represents the attenuation coefficient, which is equal to the product of the spacing characteristic coefficient and a preset constant; the equivalent capacities of all clusters are normalized.

[0100] In S3, it contains the following:

[0101] S301: Obtain a set of historical control records from the database, denoted as {H r =(t r , △P r , △Q r , C m (t r ))|r∈[1,R]}, R represents the total number of historical control records; where t r represents the rth historical regulation moment, △P r Indicates the rth active power control amount, △Q r Represents the rth reactive power control amount, C m (t r ) represents all clustering grouping sets at the corresponding moment; get the clustering grouping set at the current moment, denoted as {C m (t)|m∈[1,M]}, M represents the total number of clusters at the current moment; analyze the correlation value between any two moments t and t';

[0102] ;

[0103] Among them, λ represents the correlation coefficient, which is a system preset constant; K() represents the indicator function; if C m (t)∩C m (t') there is at least one identical battery energy storage device, then K(C m (t)∩C m (t')) = 1; if C m (t)∩C m (t') is an empty set, then K(C m (t)∩C m (t')) = 0; the historical moment sequence {t r |r∈[1, R]} corresponding to the incidence matrix is ​​recorded as K(t)={K(t, t r )|r∈[1,R]};

[0104] S302: Extract real-time power fluctuations and analyze the composite energy fluctuation value at the current moment. The composite energy fluctuation value is equal to the product of the arithmetic square root of the sum of the squares of the real-time active power and the real-time reactive power and the maximum correlation value; analyze the dynamic control threshold based on the composite energy fluctuation at the current moment. The dynamic control threshold is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; the first smoothing coefficient and the second smoothing coefficient are system preset constants.

[0105] In S4, the following contents are included:

[0106] S401: Obtain the total control instruction at time t, which includes the total control amount of active power and reactive power; analyze the matching index of each cluster, and mark the matching index of the mth cluster as M m ;

[0107] ;

[0108] Among them, △P req Indicates the total active power control quantity, △Q req Represents the total reactive power control quantity, P total Represents the total active rated capacity, which is equal to the sum of the rated capacities of all battery energy storage devices. total Indicates the total reactive power rated capacity, which is equal to the total active power rated capacity; C m * (t) represents the normalized value of the equivalent capacity of the mth cluster; all matching indexes are arranged in descending order to obtain an ordered cluster index sequence;

[0109] Example 1: S402: In this example, the actual value P actual =950kW,Q actual =300kVar, reference value: P ref =1000kW, Q ref =250kVar;

[0110] Therefore, ΔP = 950-1000 = -50kW, ΔQ = 300-250 = +50kVar;

[0111] [(-50) 2 +(+50) 2 ] 1 / 2 ≈70.7kVA; in this embodiment, the dynamic control threshold is 60kVA; then hierarchical control is triggered;

[0112] Based on the ordered cluster index sequence, hierarchical control instruction analysis is performed to divide cluster m k The hierarchical control instructions are recorded as ; where k represents the sequence number of the cluster in the ordered cluster index sequence, Represents cluster m k The active power control amount, Represents cluster m k Reactive power control amount;

[0113] ;

[0114] ;

[0115] Among them, R P Indicates the remaining active power control demand, R Q Indicates the remaining reactive power control demand, Represents cluster m k The maximum available active capacity, Represents cluster m k The maximum available reactive power capacity, Represents cluster m k The variance of the capacity distribution; sign() represents the directional function. If the directional function content is greater than 0, the output is +1; if the directional function content is less than 0, the output is -1; if the directional function content is greater than 0, the output is 0; the maximum available active capacity is equal to the product of the normalized value of the corresponding equivalent capacity and the total active rated capacity; the maximum available reactive capacity is equal to the product of the normalized value of the corresponding equivalent capacity and the total reactive rated capacity.

[0116] See also Figure 2 , the present invention provides a technical solution: a grid-type battery energy storage system, the system includes an energy storage device offset analysis module, a device clustering capacity analysis module, a correlation threshold analysis module and a control analysis module;

[0117] The energy storage device offset analysis module is used to collect the electrical parameters and operating status parameters of the energy storage device in real time, pre-process the electrical parameters and operating status parameters, and generate feature vectors; analyze the offset characteristics based on the feature vectors and mark the feature drift;

[0118] The device cluster capacity analysis module is used to analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; it also analyzes the equivalent capacity of each cluster in the clustering results;

[0119] The correlation threshold analysis module is used to obtain a set of historical control records and analyze the degree of correlation between any two moments; extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold by combining the degree of correlation between the real-time moment and the historical moment;

[0120] The control analysis module is used to analyze the matching index of each cluster in combination with the equivalent capacity and the total control instructions, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence.

[0121] The energy storage device offset analysis module includes a feature vector analysis unit and an offset analysis unit;

[0122] The eigenvector analysis unit is used to obtain the electrical parameters and dynamic operating parameters of the grid-type battery energy storage device, normalize the electrical parameters and dynamic operating parameters respectively, generate the eigenvector of the battery energy storage device, and splice the eigenvectors of all battery energy storage devices to obtain the eigenvector matrix;

[0123] The offset analysis unit is used to analyze the offset feature between the end feature matrix of the current window and the end feature matrix of the previous window based on the time window. If the offset feature is greater than the offset feature threshold, the feature drift is marked.

[0124] The equipment clustering capacity analysis module includes an equipment clustering unit and an equivalent capacity analysis unit;

[0125] The device clustering unit is used to analyze the first spacing feature and the second spacing feature of the battery energy storage device; record the battery energy storage device that satisfies both the first spacing feature and the second spacing feature greater than the corresponding threshold as the central battery energy storage device; classify the non-central battery energy storage devices into the corresponding central battery energy storage device according to the minimum value of the feature vector spacing; traverse all non-central battery energy storage devices to generate the final clustering result;

[0126] The equivalent capacity analysis unit is used to analyze the centroid vector of each cluster, and based on the centroid vector, the equivalent capacity of each cluster is analyzed by weighted decay accumulation, and the equivalent capacity of all clusters is normalized.

[0127] The correlation threshold analysis module includes a correlation analysis unit and a dynamic control threshold analysis unit;

[0128] The association analysis unit is used to obtain a set of historical control records from the database and analyze the association value based on the cluster grouping set at two moments;

[0129] The dynamic control threshold analysis unit is used to extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the composite energy fluctuation at the current moment. The dynamic control threshold is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; the first smoothing coefficient and the second smoothing coefficient are system preset constants.

[0130] The regulation analysis module includes a regulation sequence analysis unit and a hierarchical regulation instruction analysis unit;

[0131] The control sequence analysis unit is used to obtain the total control instructions and analyze the matching index of each cluster in combination with the normalized value of equivalent capacity;

[0132] The hierarchical control instruction analysis unit is used to trigger hierarchical control if the arithmetic square root of the sum of the squares of the total active and reactive control quantities is greater than the corresponding dynamic control threshold, and perform hierarchical control instruction analysis based on the ordered cluster index sequence.

[0133] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0134] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A grid-type battery energy storage control method, characterized in that: The method comprises the following steps: S1: Real-time collection of electrical parameters and operating status parameters of energy storage equipment, pre-processing of the electrical parameters and operating status parameters, and generation of feature vectors; Analyze the offset features based on the feature vector and mark the feature drift; S2: Analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; analyze the equivalent capacity of each cluster in the clustering results; S3: Obtain a set of historical control records and analyze the degree of correlation between any two moments; Extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold by combining the correlation between the real-time moment and the historical moment; S4: Analyze the matching index of each cluster based on the equivalent capacity and total control instructions, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence; In S3, include the following: S301: Obtain a set of historical control records from the database, denoted as {H r =(t r , △P r , △Q r , C m (t r ))|r∈[1,R]}, R represents the total number of historical control records; where t r represents the rth historical regulation moment, △P r Indicates the rth active power control amount, △Q r Represents the rth reactive power control amount, C m (t r ) represents all clustering grouping sets at the corresponding moment; get the clustering grouping set at the current moment, denoted as {C m (t)|m∈[1,M]}, M represents the total number of clusters at the current moment; analyze the correlation value between any two moments t and t'; convert the historical moment sequence {t r |r∈[1, R]} corresponding to the incidence matrix is ​​recorded as K(t)={K(t, t r )|r∈[1,R]}; S302: extracting real-time power fluctuations and analyzing a composite energy fluctuation value at the current moment, where the composite energy fluctuation value is equal to the product of the arithmetic square root of the sum of the squares of the real-time active power and the real-time reactive power and the maximum correlation value; A dynamic control threshold value based on the composite energy fluctuation analysis at the current moment, wherein the dynamic control threshold value is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; The first smoothing coefficient and the second smoothing coefficient are system preset constants; In S4, include the following: S401: Obtain the total control instruction at time t, which includes the total control amount of active power and reactive power; analyze the matching index of each cluster, and mark the matching index of the mth cluster as M m ; ； Among them, △P req Indicates the total active power control quantity, △Q req Represents the total reactive power control quantity, P total Represents the total active rated capacity, which is equal to the sum of the rated capacities of all battery energy storage devices, Q total Represents the total reactive rated capacity, which is equal to the total active rated capacity; C m * (t) represents the normalized value of the equivalent capacity of the mth cluster; all matching indexes are arranged in descending order to obtain an ordered cluster index sequence; S402: If the arithmetic square root of the sum of the squares of the total active and reactive control quantities is greater than the corresponding dynamic control threshold, hierarchical control is triggered, and hierarchical control instruction analysis is performed based on the ordered cluster index sequence, and cluster m is k The hierarchical control instructions are recorded as ; where k represents the sequence number of the cluster in the ordered cluster index sequence, Represents cluster m k The active power control amount, Represents cluster m k The reactive power control amount.

2. A grid-type battery energy storage control method according to claim 1, characterized in that: S1 contains the following: S101: Obtain electrical parameters and dynamic operating parameters of a grid-type battery energy storage device, wherein the electrical parameters include equivalent impedance and phase angle; the dynamic operating parameters include power change rate and response time; normalize the electrical parameters and dynamic operating parameters respectively to generate a characteristic vector of the battery energy storage device, and record the characteristic vector of the i-th device at time t as: V i (t) = [(log Z i (t)) / Z0,θ i (t) / π,tanh((dP i / dt) / △P max ), 1 / T i ]; among them, Z i (t) represents the equivalent impedance of the i-th battery energy storage device at time t; Z0 represents the impedance reference value; θ i (t) represents the phase angle of the i-th battery energy storage device at time t; the phase angle represents the phase difference between the voltage and current when the battery energy storage device is connected to the grid; dP i / dt represents the power change rate of the i-th battery energy storage device at time t; △P max Indicates the maximum power change rate; T i represents the average response time of the i-th battery energy storage device; the eigenvectors of all battery energy storage devices are concatenated to obtain the eigenvector matrix at time t; S102: The system presets the time window length and analyzes the offset feature D(t) between the feature matrix at the end of the current window and the matrix at the end of the previous window based on the time window; if the offset feature is greater than the offset feature threshold D th (t), marking feature drift F drift (t)=1; otherwise F drift (t)=0.

3. A grid-type battery energy storage control method according to claim 2, characterized in that: S2 contains the following: S201: When the characteristic drift F is detected drift When (t)=1, cluster update is triggered; Otherwise, the clustering result of the previous moment is used; analyze the first spacing feature of each battery energy storage device, and record the first spacing feature of the i-th battery energy storage device as ρ i ; Analyze the second spacing feature of each battery energy storage device and record the second spacing feature of the i-th battery energy storage device as δ i ; If the first spacing feature of the i-th battery energy storage device is not a maximum value, then all battery energy storage devices whose first spacing feature is greater than the i-th battery energy storage device are extracted to form a spacing analysis set of the i-th battery energy storage device; the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of the battery energy storage device in the corresponding spacing analysis set; if the first spacing feature of the i-th battery energy storage device is a maximum value, then the second spacing feature of the i-th battery energy storage device is equal to the maximum value of the distance between the feature vector of the i-th battery energy storage device and the feature vector of any battery energy storage device; Recording battery energy storage devices that simultaneously meet the first spacing feature and the second spacing feature greater than the corresponding threshold as central battery energy storage devices; dividing non-central battery energy storage devices into corresponding central battery energy storage devices according to the minimum value of the feature vector spacing; traversing all non-central battery energy storage devices to generate the final clustering results; S202: Analyze the centroid vector of each cluster and record the centroid vector of the mth cluster as V centroid m ; The equivalent capacity of each cluster is analyzed by weight decay accumulation based on the centroid vector; the equivalent capacity of all clusters is normalized.

4. A grid-type battery energy storage system, wherein the system is applied to implement a grid-type battery energy storage control method according to any one of claims 1 to 3, characterized in that: The system includes an energy storage device offset analysis module, an equipment cluster capacity analysis module, an associated threshold analysis module, and a regulation analysis module; The energy storage device offset analysis module is used to collect electrical parameters and operating status parameters of the energy storage device in real time, pre-process the electrical parameters and operating status parameters, and generate feature vectors; based on Feature vector analysis offsets features and marks feature drift; The device cluster capacity analysis module is used to analyze the spacing characteristics of each battery energy storage device and perform cluster analysis based on the spacing characteristics; analyze the equivalent capacity of each cluster in the clustering results; The correlation threshold analysis module is used to obtain a set of historical control records and analyze the degree of correlation between any two moments; Extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold by combining the correlation between the real-time moment and the historical moment; The control analysis module is used to analyze the matching index of each cluster in combination with the equivalent capacity and the total control instruction, generate an ordered cluster index sequence after sorting, and perform hierarchical control instruction analysis on the ordered cluster index sequence.

5. A grid-type battery energy storage system according to claim 4, characterized in that: The energy storage device offset analysis module includes a feature vector analysis unit and an offset analysis unit; The eigenvector analysis unit is used to obtain electrical parameters and dynamic operating parameters of the grid-type battery energy storage device, normalize the electrical parameters and dynamic operating parameters respectively, generate eigenvectors of the battery energy storage device, and concatenate the eigenvectors of all battery energy storage devices to obtain a eigenvector matrix; The offset analysis unit is used to analyze the offset feature between the end feature matrix of the current window and the end feature matrix of the previous window based on the time window, and if the offset feature is greater than the offset feature threshold, mark the feature drift.

6. A grid-type battery energy storage system according to claim 4, characterized in that: The device clustering capacity analysis module includes a device clustering unit and an equivalent capacity analysis unit; The device clustering unit is used to analyze the first spacing feature and the second spacing feature of the battery energy storage device; The battery energy storage devices that simultaneously meet the first spacing feature and the second spacing feature greater than the corresponding threshold are recorded as central battery energy storage devices; the non-central battery energy storage devices are divided into corresponding central battery energy storage devices according to the minimum value of the feature vector spacing; all non-central battery energy storage devices are traversed to generate the final clustering results; The equivalent capacity analysis unit is used to analyze the centroid vector of each cluster, analyze the equivalent capacity of each cluster based on the centroid vector by using weighted decay accumulation, and normalize the equivalent capacities of all clusters.

7. A grid-type battery energy storage system according to claim 4, characterized in that: The correlation threshold analysis module includes a correlation analysis unit and a dynamic control threshold analysis unit; The association analysis unit is used to obtain a set of historical control records from a database and analyze the association value based on the cluster grouping set at two moments; The dynamic control threshold analysis unit is used to extract real-time power fluctuations, analyze the composite energy fluctuation value at the current moment, and analyze the dynamic control threshold based on the composite energy fluctuation at the current moment. The dynamic control threshold is equal to the product of the first smoothing coefficient and the composite energy fluctuation value plus the product of the second smoothing coefficient and the exponential moving average of the composite energy fluctuation value; the first smoothing coefficient and the second smoothing coefficient are system preset constants.

8. The grid-connected battery energy storage system according to claim 4, characterized in that: The control analysis module includes a control sequence analysis unit and a hierarchical control instruction analysis unit; The control sequence analysis unit is used to obtain the total control instruction and analyze the matching index of each cluster in combination with the equivalent capacity normalized value; The hierarchical control instruction analysis unit is used to trigger hierarchical control if the arithmetic square root of the sum of the squares of the total active and reactive control quantities is greater than the corresponding dynamic control threshold, and perform hierarchical control instruction analysis based on the ordered cluster index sequence.

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