Method and terminal for evaluating adjustability of power distribution network

Abstract

The invention discloses a power distribution network adjustable capability evaluation method and a terminal, and the method comprises the steps: employing a clustering algorithm to carry out the cluster division of a distributed power supply, distributed energy storage, and an electric vehicle based on wind power related data, photovoltaic related data, energy storage related data, and electric vehicle related data; a distributed power supply cluster, a distributed energy storage cluster and an electric vehicle cluster are obtained, and a distributed power supply cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model are established for the distributed power supply cluster, the distributed energy storage cluster and the electric vehicle cluster; the three types of adjustable power calculation models are aggregated to obtain an adjustable range and adjustable upper and lower limit constraints of the flexibility resources of the regional power distribution network, so that the adjustable capability of the power distribution network is accurately evaluated, a basis and guidance are provided for the participation of the flexibility resources in peak regulation and frequency modulation, the peak-valley difference is effectively reduced, the load fluctuation is stabilized, and the stability of the power distribution network is improved. Stable operation of the power grid is maintained.

Description

Technical Field

[0001] The present invention relates to the field of distribution network regulation, and in particular to a method and a terminal for evaluating the adjustable capability of a distribution network. Background Art

[0002] The increasing penetration of renewable energy in the power system has greatly increased the uncertainty in the power grid. These resources are affected by meteorological conditions and time changes, and their output fluctuates greatly, making them difficult to accurately predict and control, resulting in instability in the power grid. Flexible resources are needed to balance the power system. The load side also often fluctuates, and the peak-to-valley difference can cause the power grid to be overloaded or inefficient. Flexible resources can be used to adjust power supply to meet load demands at different times, thereby improving the reliability and efficiency of the power grid.

[0003] Although there are multiple potential flexibility resources, traditional power system assessment methods are usually unable to fully quantitatively evaluate their participation and effects, that is, existing methods are difficult to effectively and comprehensively consider multiple flexibility resources such as distributed power sources, distributed energy storage and electric vehicles to accurately evaluate the adjustable capacity of the distribution network. Therefore, a new method is urgently needed to quantitatively evaluate the potential of these resources to better manage the dispatchability and stability of the power system. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and a terminal for evaluating the adjustable capacity of a distribution network, which can accurately evaluate the adjustable capacity of the distribution network.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0006] A method for evaluating the adjustable capacity of a distribution network comprises the following steps:

[0007] Obtain wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data in the distribution network;

[0008] Using a clustering algorithm to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively, to obtain a distributed power source cluster, a distributed energy storage cluster and an electric vehicle cluster;

[0009] Based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively establish a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster;

[0010] The distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A terminal for evaluating adjustable capacity of a distribution network comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0013] Obtain wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data in the distribution network;

[0014] Using a clustering algorithm to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively, to obtain a distributed power source cluster, a distributed energy storage cluster and an electric vehicle cluster;

[0015] Based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively establish a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster;

[0016] The distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0017] The beneficial effects of the present invention are as follows: a clustering algorithm is used to cluster distributed power sources, distributed energy storage and electric vehicles based on wind power related data, photovoltaic related data, energy storage related data and electric vehicle related data, to obtain distributed power source clusters, distributed energy storage clusters and electric vehicle clusters; based on wind power related data, photovoltaic related data, energy storage related data and electric vehicle related data, a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model are established for the distributed power source clusters, distributed energy storage clusters and electric vehicle clusters respectively; the distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the flexibility resources of the regional distribution network; the present invention takes into account a variety of adjustable resources such as new energy power generation, distributed energy storage and electric vehicles, and uses a clustering algorithm combined with an adjustable power calculation model to evaluate the adjustable capacity of the regional distribution network, thereby accurately evaluating the adjustable capacity of the distribution network, which is helpful to provide a basis and guidance for the participation of flexibility resources in peak and frequency regulation, effectively reduce the peak-to-valley difference, and smooth out load fluctuations to maintain stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for evaluating the adjustable capability of a distribution network according to an embodiment of the present invention;

[0019] Figure 2 The present invention is a schematic diagram of the structure of a terminal for evaluating the adjustable capability of a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0021] Please refer to Figure 1 , a method for evaluating the adjustable capacity of a distribution network, comprising the steps of:

[0022] Obtain wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data in the distribution network;

[0023] Using a clustering algorithm to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively, to obtain a distributed power source cluster, a distributed energy storage cluster and an electric vehicle cluster;

[0024] Based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively establish a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster;

[0025] The distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0026] From the above description, it can be seen that the beneficial effects of the present invention are: a clustering algorithm is used to cluster distributed power sources, distributed energy storage and electric vehicles based on wind power related data, photovoltaic related data, energy storage related data and electric vehicle related data, to obtain distributed power source clusters, distributed energy storage clusters and electric vehicle clusters; based on wind power related data, photovoltaic related data, energy storage related data and electric vehicle related data, a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model are established for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster respectively; the distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the flexibility resources of the regional distribution network; the present invention takes into account a variety of adjustable resources such as new energy power generation, distributed energy storage and electric vehicles, and uses a clustering algorithm combined with an adjustable power calculation model to evaluate the adjustable capacity of the regional distribution network, thereby accurately evaluating the adjustable capacity of the distribution network, which is helpful to provide a basis and guidance for the participation of flexibility resources in peak and frequency regulation, effectively reduce the peak-to-valley difference, and smooth out load fluctuations to maintain stable operation of the power grid.

[0027] Furthermore, the clustering algorithm is used to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, and the distributed power source cluster, distributed energy storage cluster and electric vehicle cluster are obtained, including:

[0028] The Fast Unfolding clustering algorithm is used to cluster the distributed power sources based on the wind power related data and the photovoltaic related data to obtain a distributed power source cluster;

[0029] Using a fuzzy C-means clustering algorithm to cluster the distributed energy storage based on the energy storage related data to obtain a distributed energy storage cluster;

[0030] An improved k-means clustering algorithm is used to cluster the electric vehicles based on the electric vehicle related data to obtain electric vehicle clusters.

[0031] From the above description, it can be seen that different flexible resources use different clustering algorithms for cluster division, which is more in line with the characteristics of flexible resources and realizes reliable and accurate cluster division, so as to realize accurate adjustable capacity evaluation later.

[0032] Furthermore, the Fast Unfolding clustering algorithm is used to cluster the distributed power sources based on the wind power related data and the photovoltaic related data, and the obtained distributed power source cluster includes:

[0033] Calculating the net load of the distributed power source node according to the distributed power output and load of the distributed power source node of the distribution network, and using the net load of the distributed power source node as the first grouping indicator;

[0034] The reactive power regulation capacity and active power regulation capacity of distributed power sources are used as the second grouping index;

[0035] Normalizing the first grouping index and the second grouping index respectively to obtain a normalized first grouping index and a normalized second grouping index;

[0036] Calculating the similarity between distributed power supply nodes based on the normalized first grouping index and the normalized second grouping index;

[0037] Randomly select the cluster where any distributed power source node is located and merge it with other clusters to obtain a new cluster;

[0038] Calculating an incremental value of a modularity function corresponding to the new cluster based on the similarity between the distributed power supply nodes;

[0039] If the maximum increment value of the current modularity function is greater than the first preset value, merging the cluster where any distributed power source node is located with the cluster corresponding to the maximum increment value of the modularity function to obtain a new cluster;

[0040] If the maximum increment value of the current modularity function is not greater than the first preset value, the cluster where any distributed power source node is located is not merged with the cluster corresponding to the maximum increment value of the modularity function;

[0041] The cluster is taken as a new distributed power node, and the step of calculating the similarity between distributed power nodes based on the normalized first clustering index and the normalized second clustering index is returned to execute until the maximum incremental value of the modularity function no longer changes, thereby obtaining a distributed power cluster.

[0042] From the above description, it can be seen that the FastUnfolding clustering algorithm is used to divide the distributed power generation clusters. When this algorithm performs cluster division, there is no need to give the number of clusters in advance. The number of clusters is the optimal number obtained by optimizing and solving the modularity function value. Under different operating scenarios, the modularity function value of the cluster division of the clustering algorithm is slightly different, and it has strong adaptability, which ensures the effect of cluster division of distributed power generation.

[0043] Furthermore, the electric vehicles are clustered based on the electric vehicle related data using the improved k-means clustering algorithm, and the electric vehicle clusters obtained include:

[0044] Randomly select K electric vehicle nodes from the distribution network as the initial clustering centers;

[0045] Calculate the distances between the remaining electric vehicle nodes and the K cluster centers;

[0046] Determine the evaluation index of clustering effect according to the universal gravitation model;

[0047] Assigning the remaining electric vehicle nodes corresponding to the maximum value of the evaluation index to the cluster center, and updating the cluster center;

[0048] Determine whether the evaluation index reaches a second preset value or the current iteration number reaches a preset number. If so, stop the iteration to obtain the electric vehicle cluster. If not, return to execute the step of calculating the distance between the remaining electric vehicle nodes and the K cluster centers.

[0049] From the above description, it can be seen that when clustering electric vehicles, the k-means clustering algorithm is simple in principle and easy to implement. It can cluster data according to multiple features or dimensions, and the gravitational model can simulate the mutual attraction and repulsion between data points, which helps the algorithm to remain stable in the face of noise and outliers, thereby improving the robustness of clustering. The traditional k-means algorithm assumes that clusters are convex and of similar scales, while the gravitational model can simulate more complex interactions, making it possible to discover clusters of arbitrary shapes and sizes. The gravitational model adjusts the cluster center by simulating the interaction between data points, which can reduce the dependence on the initial cluster center selection, thereby reducing the risk of falling into a local optimal solution. Therefore, the gravitational model is used to improve the k-means clustering algorithm, and the improved k-means clustering algorithm is used to perform clustering to obtain electric vehicle clusters, thereby improving the reliability and accuracy of electric vehicle clustering.

[0050] Further, the distributed power supply cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are established for the distributed power supply cluster, the distributed energy storage cluster and the electric vehicle cluster respectively based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, including:

[0051] Establishing a wind power cluster adjustable power calculation model based on the distributed power source cluster according to the wind power related data;

[0052] Establishing a photovoltaic cluster adjustable power calculation model based on the distributed power supply cluster according to the photovoltaic related data;

[0053] Establishing an adjustable power calculation model of a distributed energy storage cluster based on the distributed energy storage cluster according to the energy storage related data;

[0054] An electric vehicle cluster adjustable power calculation model is established based on the electric vehicle cluster according to the electric vehicle related data.

[0055] From the above description, it can be seen that by establishing the adjustable power calculation model of the wind power cluster, the adjustable power calculation model of the photovoltaic cluster, the adjustable power calculation model of the distributed energy storage cluster and the adjustable power calculation model of the electric vehicle cluster, the different adjustable power calculation models can comprehensively and accurately reflect the adjustable capacity of the distribution network.

[0056] Furthermore, establishing a wind power cluster adjustable power calculation model based on the distributed power source cluster according to the wind power related data includes:

[0057] Determine the output power of the wind turbine generator set according to the wind power related data;

[0058] Determining the adjustable potential of a single wind power according to the output power of the wind turbine group;

[0059] Determine a wind power cluster adjustable power calculation model according to the adjustable potential of the single wind power;

[0060] The step of establishing a photovoltaic cluster adjustable power calculation model based on the distributed power supply cluster according to the photovoltaic related data includes:

[0061] Determine the output power of the photovoltaic unit according to the photovoltaic related data;

[0062] Determining the adjustable potential of a single photovoltaic generator according to the output power of the photovoltaic group;

[0063] A photovoltaic cluster adjustable power calculation model is determined according to the adjustable potential of the single photovoltaic generator.

[0064] From the above description, it can be seen that the calculation model of the adjustable power of the wind power cluster is determined according to the adjustable potential of a single wind power, and the calculation model of the adjustable power of the photovoltaic cluster is determined according to the adjustable potential of a single photovoltaic generator, thereby realizing accurate evaluation of the adjustable power of the wind power cluster and the photovoltaic cluster.

[0065] Furthermore, establishing a distributed energy storage cluster adjustable power calculation model based on the distributed energy storage cluster according to the energy storage related data includes:

[0066] Establish energy storage charging and discharging constraints;

[0067] Determine the power of the energy storage load group according to the energy storage related data;

[0068] A distributed energy storage cluster adjustable power calculation model is obtained based on the energy storage load group power and the energy storage charging and discharging constraints.

[0069] From the above description, it can be seen that the distributed energy storage cluster adjustable power calculation model is obtained based on the energy storage load group power and energy storage charging and discharging constraints, which can reasonably and effectively calculate the adjustable power of distributed energy storage.

[0070] Furthermore, the step of establishing an adjustable power calculation model for an electric vehicle cluster based on the electric vehicle cluster according to the electric vehicle related data includes:

[0071] Establish battery safety capacity constraints and charge and discharge power constraints for electric vehicles;

[0072] Determining a total response potential based on the charge and discharge response potentials of users of the electric vehicle;

[0073] An adjustable power calculation model for an electric vehicle cluster is obtained according to the total response potential, the battery safety power constraint and the charge and discharge power constraint.

[0074] From the above description, it can be seen that the electric vehicle cluster adjustable power calculation model is obtained according to the total response potential, battery safety power constraint and charging and discharging power constraint. It can calculate the adjustable power of the electric vehicle cluster while ensuring the service life of the electric vehicle battery, thereby ensuring the reliable operation of the distribution network.

[0075] Furthermore, the distributed power cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources, including:

[0076] The Minkowski method is used to aggregate the adjustable power calculation model of the distributed power source cluster, the adjustable power calculation model of the distributed energy storage cluster and the adjustable power calculation model of the electric vehicle cluster to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0077] From the above description, it can be seen that the Minkowski method is used to aggregate the distributed power generation cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model with high precision, which improves the accuracy of the evaluation of the adjustable capacity of the distribution network. The adjustable range of the flexibility resources of the regional distribution network and the adjustable upper and lower limit constraints effectively reflect the adjustable capacity of the distribution network, enabling it to participate in the interactive operation of the power market more efficiently and facilitate the unified management and scheduling of the power grid.

[0078] Please refer to Figure 2 Another embodiment of the present invention provides an evaluation terminal for the adjustable capability of a distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the above-mentioned method for evaluating the adjustable capability of a distribution network when executing the computer program.

[0079] The above-mentioned method and terminal for evaluating the adjustable capacity of a distribution network of the present invention can be applied to a distribution network that needs to evaluate the adjustable capacity, and are described below through specific implementation methods:

[0080] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0081] A method for evaluating the adjustable capacity of a distribution network comprises the following steps:

[0082] S1. Obtain wind power related data, photovoltaic related data, energy storage related data and electric vehicle related data in the distribution network.

[0083] In an optional embodiment, the wind power related data includes real-time wind speed, the photovoltaic related data includes light intensity and ambient temperature, the energy storage related data includes the rated capacity of the energy storage system, and the electric vehicle related data includes the SOC status of the user of the electric vehicle.

[0084] S2. Clustering the distributed power source, distributed energy storage and electric vehicle based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data respectively to obtain a distributed power source cluster, a distributed energy storage cluster and an electric vehicle cluster, specifically including S21-S23:

[0085] S21, using the FastUnfolding clustering algorithm to cluster the distributed power sources based on the wind power related data and the photovoltaic related data to obtain a distributed power source cluster, specifically including S211-S219:

[0086] When the Fast Unfolding clustering algorithm performs cluster division, there is no need to give the number of clusters in advance. The number of clusters is the optimal number obtained by optimizing the modularity function value. In different operating scenarios, the modularity function value of cluster division based on this method has little difference and has strong adaptability.

[0087] S211. Calculate the net load of the distributed power source node according to the distributed power source output and load of the distributed power source node of the distribution network, and use the net load of the distributed power source node as the first grouping indicator.

[0088] Among them, the net load of the distributed power source node can reflect the relationship between the output of the distributed power source connected to the node and the load size. The net load of the distributed power source node is calculated according to the distributed power source output and load of the distributed power source node of the distribution network, specifically:

[0089] P net_i =P N_i -P L_i ;

[0090] Where P net_i represents the net load of distributed generation node i, P N_i represents the distributed power output of distributed power node i, P L_i Represents the load of distributed generation node i.

[0091] The output range of distributed power generation is:

[0092] P N_i ∈[P N_i,min ,P N_i,max ];

[0093] Where P N_i,min represents the lower limit of the distributed power output of distributed power node i, P N_i,max Represents the upper limit of the distributed generation output of distributed generation node i.

[0094] S212. Use the reactive power regulation capacity and active power regulation capacity of the distributed power source as the second grouping indicator.

[0095] Among them, the reactive power regulation capacity and active power regulation capacity of the distributed power source are:

[0096]

[0097] In the formula, Qc Represents the reactive power regulation capacity of distributed generation, P c Represents the active regulation capacity of distributed generation, Q min Indicates the minimum reactive power regulation capacity, Q max Indicates the maximum reactive power regulation capacity, P max Indicates the maximum value of active regulation capacity.

[0098] The second grouping index can be used to group distributed power sources with similar active and reactive regulation capacities into the same cluster.

[0099] S213: Normalize the first grouping index and the second grouping index respectively to obtain a normalized first grouping index and a normalized second grouping index, specifically:

[0100]

[0101] In the formula, x i ' ,m represents the mth clustering index of the ith node after normalization, x i,m represents the mth clustering index of the ith node, x i,m,min represents the minimum value among the M clustering indices of the i-th node, x i,m,max Represents the maximum value among the M clustering indices of the i-th node.

[0102] S214, calculating the similarity between the distributed power supply nodes based on the normalized first clustering index and the normalized second clustering index, so that the distributed power supply nodes in the same cluster have similar operating characteristics, specifically:

[0103]

[0104] In the formula, s ij represents the similarity between distributed power node i and distributed power node j, R represents the maximum value of the weighted product of the indicators of any two nodes, M represents the number of clustering indicators, α m represents the weight of the mth clustering index, x′ j,m Represents the mth clustering index of the jth node after normalization.

[0105] S215. Randomly select a cluster where any distributed power source node is located and merge it with other clusters to obtain a new cluster.

[0106] S216: Calculate an incremental value of a modularity function corresponding to the new cluster based on the similarity between the distributed power supply nodes.

[0107] The modularity function measures the rationality of the cluster division, specifically:

[0108]

[0109] In the formula, Q represents the modularity, s represents the sum of similarities between two nodes, and s i represents the sum of similarities between distributed power generation node i and other distributed power generation nodes, s j represents the sum of similarities between distributed power node j and other distributed power nodes, Indicates the relationship between distributed power generation node i and distributed power generation node j. If they are in the same cluster, it is 1; if they are not in the same cluster, it is 0.

[0110] The incremental value ΔQ of the modularity function corresponding to the new cluster is the incremental value between the modularity function corresponding to the cluster of the current iteration and the modularity function corresponding to the cluster of the previous iteration. The incremental value of the modularity function of each iteration is recorded, and a maximum incremental value will appear in each iteration.

[0111] S217, if the maximum increment value of the current modularity function ΔQ max If the maximum value of the modularity function is greater than a first preset value, the cluster where any distributed power source node is located is merged with the cluster corresponding to the maximum incremental value of the modularity function to obtain a new cluster.

[0112] In an optional implementation, the first preset value is 0.

[0113] S218: If the maximum increment value of the current modularity function is not greater than the first preset value, the cluster where any distributed power source node is located is not merged with the cluster corresponding to the maximum increment value of the modularity function.

[0114] S219, taking the cluster as a new distributed power node, returning to execute S214, until the maximum increment value of the modularity function no longer changes, thereby obtaining a distributed power cluster.

[0115] The cluster is regarded as a new distributed power supply node, and the process returns to execute S214. Then S214 calculates the similarity between the two clusters.

[0116] S22, using a fuzzy C-means clustering algorithm to cluster the distributed energy storage based on the energy storage related data to obtain a distributed energy storage cluster, specifically including S221-S225:

[0117] The k-means clustering algorithm only rigidly assigns data points to the nearest cluster center, while the fuzzy C-means clustering algorithm can assign a fuzziness value to each data point, which can better cope with the noise and ambiguity in the data set. The fuzzy C-means clustering algorithm is relatively insensitive to the selection of the initial cluster center, so it can obtain better clustering results.

[0118] The objective function of the fuzzy C-means clustering algorithm is specifically:

[0119]

[0120] In the formula, J m (U,P) represents the weighted sum of squares, m represents the weighted index, U represents the membership matrix, P represents the cluster center, N represents the number of nodes connected to the power system, C represents the number of clusters, μ i,k represents the membership function, that is, the membership relationship between the sample and the subset, and μ i,k ∈[0,1], membership matrix U=[μ i,k ] c×k , Represents the distance from the cluster center to the i-th class sample.

[0121] S221, initializing the membership matrix U, the scheduling period T and the number of clusters C;

[0122] S222, take L iterations to calculate the cluster center, specifically:

[0123]

[0124] In the formula, represents the cluster center of L+1 iterations, represents the membership function of L iterations, x k represents the kth sample.

[0125] S223, modifying the membership matrix, specifically:

[0126]

[0127] In the formula, Represents the distance from the cluster center to the j-th class sample.

[0128] S224, calculate the objective function, specifically:

[0129]

[0130] S225. If the above calculation satisfies |μ i,k (L+1) -μ i,k (L) |≤ε, μ i,k =max{μ i,k}, where ε represents the error threshold, a very small constant, then the samples are grouped into a subgroup and a distributed energy storage cluster is obtained. If it is not satisfied, L=L+1, and return to S222.

[0131] S23, using an improved k-means clustering algorithm to cluster the electric vehicles based on the electric vehicle related data to obtain electric vehicle clusters, specifically including S231-S235:

[0132] Since the k-means clustering algorithm is simple in principle and easy to implement, and can cluster data according to multiple features or dimensions, the present invention uses the k-means clustering algorithm to divide the group based on the EV's (electric vehicle) access time, off-grid time, and initial remaining power when entering the station. The k-means algorithm uses k as a parameter to divide n samples into k clusters. Its goal is to make the samples within the cluster have a higher similarity, while the samples between clusters have a lower similarity. The k-means algorithm uses distance as the evaluation index of similarity. The goal of clustering is to minimize the distance from each cluster center to the observation value in the cluster.

[0133] Gravitation is a kind of interaction characteristic widely existing in nature, and this model is introduced into the k-means clustering model. The gravitational model can simulate the mutual attraction and repulsion between data points, which helps the algorithm remain stable in the face of noise and outliers, thereby improving the robustness of clustering; the traditional k-means algorithm assumes that clusters are convex and of similar scale, while the gravitational model can simulate more complex interactions, making it possible to discover clusters of arbitrary shapes and sizes. The gravitational model adjusts the cluster center by simulating the interaction between data points, which can reduce the dependence on the initial cluster center selection, thereby reducing the risk of falling into a local optimal solution. Therefore, the k-means clustering algorithm is improved using the gravitational model.

[0134] S231. Randomly select K electric vehicle nodes from the distribution network as initial clustering centers.

[0135] S232, calculating the distances between the remaining electric vehicle nodes and the K cluster centers, specifically:

[0136] r i =||x i -c j ||;

[0137] In the formula, r i represents the distance between electric vehicle node i and K cluster centers, x i represents electric vehicle node i, c j represents the cluster center j.

[0138] S233. Determine the evaluation index of clustering effect according to the universal gravitation model, specifically:

[0139]

[0140] Where, EI i represents the evaluation index of clustering effect, V represents the evaluation adjustment coefficient, P represents the load capacity of two points, and p i represents the load capacity of electric vehicle node i, r i Represents the distance from electric vehicle node i to the cluster center.

[0141] S234, assigning the remaining electric vehicle nodes corresponding to the maximum value of the evaluation index to the cluster center, and updating the cluster center.

[0142] Specifically, using MAX(EI i ) i Attributed to c j This category, and update the cluster center, specifically:

[0143]

[0144] S235, judging whether the evaluation index reaches a second preset value or the current number of iterations reaches a preset number, if so, stopping the iteration to obtain an electric vehicle cluster, if not, returning to execute S232.

[0145] S3, based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively establish a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster, specifically including S31-S34:

[0146] S31, establishing a wind power cluster adjustable power calculation model based on the distributed power source cluster according to the wind power related data, specifically including S311-S313:

[0147] S311. Determine the output power of the wind turbine according to the wind power related data. The active power of the wind turbine is related to natural factors such as wind speed and wind direction and the control strategy. The biggest influencing factor is the real-time wind speed. To ensure the safe operation of the wind turbine, the wind speed on the blades cannot increase indefinitely. If necessary, the operation state needs to be cut out. The output power of the wind turbine changes according to the change of wind speed. Specifically:

[0148]

[0149] Where P v,i represents the output power of the wind turbine, v represents the real-time wind speed, and v in represents the cut-in wind speed, P N Indicates the rated power of the wind turbine, v N Indicates rated wind speed, vout Indicates the cut-out wind speed, when ν<ν in When ν in ≤ν≤ν N When ν N ≤ν<ν out The wind turbine still maintains rated power output. If the wind speed continues to increase and exceeds the cut-out wind speed, the wind turbine will exit operation and be disconnected from the grid to avoid accidents.

[0150] In order to achieve reasonable dispatch of wind farm output, the wind power output prediction system of the dispatching department should ensure a certain degree of accuracy, that is, the wind farm output prediction error is small enough, specifically:

[0151]

[0152] Where P wp represents the predicted output of the wind farm, P represents the actual output of the wind farm, and P wcap represents the capacity of the wind farm, and δ represents the maximum error in the wind farm output prediction.

[0153] S312, determining the adjustable potential of a single wind power according to the output power of the wind turbine generator set, specifically:

[0154]

[0155] P v,min,i ≤ΔP v,i ≤P v,max,i ;

[0156] In the formula, ΔP v,i represents the adjustable potential of a single wind power, P v,i,max (t+1) represents the predicted maximum power of wind turbine i in the next cycle, P v,i (t) represents the output power of wind turbine i at the current cycle wind speed, P N ,v,i represents the rated power of wind turbine i, P v,min,i represents the lower limit of the adjustable power of wind turbine i, P v,max,i Represents the upper limit of the adjustable power of wind turbine i.

[0157] S313, determining a wind power cluster adjustable power calculation model according to the adjustable potential of the single wind power, specifically:

[0158]

[0159] In the formula, ΔP W It represents the adjustable power of the wind power cluster, and n represents the number of wind turbines in the wind power cluster.

[0160] S32, establishing a photovoltaic cluster adjustable power calculation model based on the distributed power supply cluster according to the photovoltaic related data, specifically including S321-S323:

[0161] S321. Determine the output power of the photovoltaic unit according to the photovoltaic related data. The output of the solar photovoltaic generator set is mainly affected by the light intensity, the battery junction temperature and the ambient temperature, specifically:

[0162]

[0163] Where P p,i (t) represents the output power of the photovoltaic unit at time t, P SET represents the rated output power of the photovoltaic unit, S(t) represents the light intensity of the photovoltaic unit at time t, S SET Indicates the standard light intensity of the photovoltaic unit, k T represents the temperature coefficient, T(t) represents the ambient temperature at time t, T SET Indicates standard ambient temperature.

[0164] S322, determining the adjustable potential of a single photovoltaic generator according to the output power of the photovoltaic unit, specifically:

[0165]

[0166] P p,min,i ≤ΔP p,i ≤P p,max,i ;

[0167] Where ΔP p,i represents the adjustable potential of a single photovoltaic generator, P p,i,max (t+1) represents the predicted maximum power of PV unit i in the next cycle, P p,i (t) represents the output power of PV unit i under the current cycle conditions, P N,p,i Indicates the rated power of photovoltaic unit i, P p,min,i represents the lower limit of the adjustable power of PV unit i, P p,max,i Indicates the upper limit of the adjustable power of PV unit i.

[0168] S323, determining a photovoltaic cluster adjustable power calculation model according to the adjustable potential of the single photovoltaic generator, specifically:

[0169]

[0170] Where ΔP PV represents the adjustable power of the PV cluster, and n represents the number of PV units.

[0171] S33, establishing a distributed energy storage cluster adjustable power calculation model based on the distributed energy storage cluster according to the energy storage related data, specifically including S331-S333:

[0172] S331. Establish energy storage charging and discharging constraints, specifically:

[0173] The charging and discharging power P of the energy storage system ES Defined as charging positive and discharging negative, assuming that the charging efficiency of the energy storage system is η c and discharge efficiency η d Keep it unchanged during operation, and set:

[0174]

[0175] In the formula, α ES (t) represents the charging and discharging efficiency of the energy storage system at time t, represents the charging efficiency of the energy storage system, k(t) represents the operating state of the energy storage system. If it is in the charging state, it is 1; if it is in the discharging state, it is -1; if it is in the non-charging and non-discharging state, it is 0. Indicates the discharge efficiency of the energy storage system;

[0176] The energy storage SOC changes as follows:

[0177]

[0178] 0≤P ES ≤P ES,c,max ;

[0179] 0≤P ES ≤P ES,d,max ;

[0180] In the formula, SOC ES Indicates the state of charge of the energy storage system, P ES Represents the charging and discharging power of the energy storage system, C ES Represents the rated capacity of the energy storage system, P ES,c,max Represents the maximum charging power of the energy storage system, P ES,d,max Indicates the maximum discharge power of the energy storage system;

[0181] The energy storage charging and discharging constraints are established as:

[0182] P ESmin,i ≤SOC ES (t)≤P ESmax,i ;

[0183] Where P ESmin,i Represents the minimum state of charge of the energy storage system, P ESmax,i Indicates the maximum state of charge of the energy storage system, SOC ES(t) represents the charge state of the energy storage system at time t.

[0184] S332: Determine the power of the energy storage load group according to the energy storage related data, specifically:

[0185]

[0186] In the formula, represents the energy storage load group power at time t, N represents the energy storage quantity, k j (t) represents the operating state of energy storage system j, Represents the charging and discharging rated power of energy storage system j.

[0187] S333, obtaining a distributed energy storage cluster adjustable power calculation model based on the energy storage load group power and the energy storage charging and discharging constraints, including:

[0188]

[0189] Where ΔP ES (t) represents the adjustable power of the distributed energy storage cluster at time t, Indicates the power of the energy storage load group before regulation. Indicates the power of the energy storage load group after adjustment.

[0190] S34, establishing an electric vehicle cluster adjustable power calculation model based on the electric vehicle cluster according to the electric vehicle related data, specifically including S341-S343:

[0191] S341. Establish battery safety power constraints and charge and discharge power constraints for electric vehicles.

[0192] To ensure the service life of EV batteries, the battery safety power limit must be met. If the charging response is considered, the EV power must reach the initial expected power before leaving the station on the premise of meeting the travel needs. If the discharge response is considered, the battery power requirements must also be met. Too low power will also affect the use of EV. The battery safety power constraint is:

[0193]

[0194] S max ≥S i (t)+S DR,i (t)≥S ex,i ;

[0195] S i (t)+S DR,i (t)≥S min ;

[0196] In the formula, S minIndicates the lower limit of SOC of electric vehicle battery, S i (t) represents the SOC of electric vehicle user i at time t, S DR,i (t) represents the electric vehicle user i responding to the power P at time t i (t) SOC change caused by charging (discharging), S max Indicates the upper limit of SOC of electric vehicle battery, P d Indicates the rated discharge power of the electric vehicle, P c represents the rated charging power of the electric vehicle, t arrive represents the arrival time of electric vehicle user i, t leave represents the expected departure time of electric vehicle user i, η c represents the charging efficiency of electric vehicles, C0 represents the battery capacity of electric vehicles, S ex,i Indicates the initial expected power of the electric vehicle.

[0197] When the remaining power of the EV is higher, the initial charging power is smaller and the initial discharging power is larger. When the EV power is lower, the discharging power decreases. When the remaining power of the EV is lower, the initial charging power is larger and the initial discharging power is smaller. As the EV power increases, the charging power decreases. The charging and discharging power constraints are:

[0198]

[0199] P min,i (t)≤P r,i (t)≤P max,i (t);

[0200] Where P c,i (t) represents the charging power, P r,i (t) represents the rated charging (discharging) power of electric vehicle user i at time t, P d,i (t) represents the discharge power, P min,i (t) represents the minimum charging (discharging) power of electric vehicle user i at time t, P max,i (t) represents the maximum charging (discharging) power of electric vehicle user i at time t.

[0201] S342. Determine the total response potential according to the charge and discharge response potential of the user of the electric vehicle, specifically:

[0202] P EV,i (t) = P nc,i (t)-P min,i (t);

[0203]

[0204] PEV,c,i (t) = P nc,i (t);

[0205] P EV,d,i (t) = η|P min,i (t)|;

[0206] P EV,i (t) = P EV,c,i (t)+P EV,d,i (t);

[0207] P EV,min,i ≤P EV,i ≤P EV,max,i ;

[0208] Where P EV,i (t) represents the charge and discharge response potential of electric vehicle user i at time t, P nc,i (t) represents the natural charging power when not participating in the DR (demand response) process, P EV,c,i (t) represents the charging response potential of electric vehicle user i at time t, P EV,d,i (t) represents the discharge response potential of electric vehicle user i at time t, η represents, P EV,min,i represents the lower limit of the adjustable power of electric vehicle i, P EV,max,i represents the upper limit of the adjustable power of electric vehicle i, P EV,i It represents the charging and discharging response potential of user i of the electric vehicle.

[0209] S343, obtaining an electric vehicle cluster adjustable power calculation model according to the total response potential, the battery safety power constraint and the charge and discharge power constraint, including:

[0210]

[0211] Where ΔP EV (t) represents the adjustable power of the electric vehicle cluster at time t, and n represents the number of electric vehicles.

[0212] S4. Aggregate the adjustable power calculation model of the distributed power source cluster, the adjustable power calculation model of the distributed energy storage cluster and the adjustable power calculation model of the electric vehicle cluster to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0213] In the face of various flexibility resources in the distribution network, a unified model is needed to describe the flexibility characteristics of various resources and aggregate them into a new whole, so that they can participate in the interactive operation of the power market more efficiently and facilitate the unified management and scheduling of the power grid. Specifically, the Minkowski method is used to aggregate the adjustable power calculation model of the distributed power generation cluster, the adjustable power calculation model of the distributed energy storage cluster, and the adjustable power calculation model of the electric vehicle cluster to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

[0214] The adjustable range of the regional distribution network flexibility resources is:

[0215]

[0216] In the formula, Z all represents the adjustable range of regional distribution network flexibility resources, Z j represents the adjustable range of the jth cluster, represents the Minkowski summation operation, ΔP PV (t) represents the adjustable power of the photovoltaic cluster at time t, ΔP W (t) represents the adjustable power of the wind power cluster at time t.

[0217] The power boundary represents the minimum (maximum) flexibility power that the cluster flexibility resources can provide immediately within a period of time. The adjustable upper and lower limits are:

[0218]

[0219] Where P all,min represents the lower limit constraint of the adjustable power of the distribution network, P all,max represents the upper limit constraint of the adjustable power of the distribution network, i∈g represents a wind farm cluster, i∈w represents a photovoltaic power plant cluster, i∈l represents an energy storage cluster, and i∈n represents an electric vehicle cluster.

[0220] The present invention takes into account a variety of adjustable resources such as renewable energy power generation, distributed energy storage, and electric vehicles. Renewable energy power generation is renewable and widely distributed, and can be deployed in various locations to reduce transmission losses. However, its output is affected by meteorological conditions and time and is volatile. The energy storage system has a very fast response time and can be charged and discharged in an instant, which helps to smooth grid fluctuations and respond to sudden demands, and can be configured as needed to provide different levels of capacity and power to meet specific grid regulation needs. Electric vehicles are equipped with large-capacity batteries, and the vehicles can be regarded as distributed energy and energy storage devices. Electric vehicles can be charged at different locations, and their mobility makes them a distributed resource in grid regulation.

[0221] The present invention groups distributed power sources, distributed energy storage and electric vehicles through a clustering algorithm, and establishes an adjustable power calculation model to analyze and comprehensively consider their distribution network regulation capabilities. This method helps to more accurately evaluate the adjustable capacity of the regional distribution network, provide a basis and guidance for the participation of flexible resources in peak and frequency regulation, effectively reduce the peak-to-valley difference, smooth out load fluctuations, and maintain stable operation of the power grid.

[0222] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0223] A distribution network adjustable capacity evaluation terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned distribution network adjustable capacity evaluation method is implemented.

[0224] In summary, the present invention provides a method and terminal for evaluating the adjustable capacity of a distribution network, which uses a clustering algorithm to cluster distributed power sources, distributed energy storage and electric vehicles based on wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data, respectively, to obtain distributed power source clusters, distributed energy storage clusters and electric vehicle clusters, and establishes a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster for the distributed power source cluster, distributed energy storage cluster and electric vehicle cluster based on wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data. The adjustable power calculation model aggregates the adjustable power calculation model of the distributed power source cluster, the adjustable power calculation model of the distributed energy storage cluster, and the adjustable power calculation model of the electric vehicle cluster to obtain the adjustable range and the adjustable upper and lower limit constraints of the regional distribution network flexibility resources. The present invention takes into account a variety of adjustable resources such as new energy power generation, distributed energy storage, and electric vehicles, and uses a clustering algorithm combined with an adjustable power calculation model to evaluate the adjustable capacity of the regional distribution network, thereby accurately evaluating the adjustable capacity of the distribution network, which helps to provide a basis and guidance for the participation of flexible resources in peak and frequency regulation, effectively reduce the peak-to-valley difference, and smooth out load fluctuations to maintain the smooth operation of the power grid. In addition, different flexibility resources use different clustering algorithms for cluster division, which is more in line with the characteristics of flexibility resources, and achieves reliable and accurate cluster division, so as to achieve accurate adjustable capacity evaluation in the future. At the same time, different flexibility resources use different clustering algorithms for cluster division, which is more in line with the characteristics of flexibility resources, and achieves reliable and accurate cluster division, so as to achieve accurate adjustable capacity evaluation in the future.

[0225] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating the adjustable capacity of a distribution network, characterized in that: Includes steps: Obtain wind power-related data, photovoltaic-related data, energy storage-related data and electric vehicle-related data in the distribution network; Using a clustering algorithm to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively, to obtain a distributed power source cluster, a distributed energy storage cluster and an electric vehicle cluster; Based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, respectively establish a distributed power source cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power source cluster, the distributed energy storage cluster and the electric vehicle cluster; The distributed power source cluster adjustable power calculation model, the distributed energy storage cluster adjustable power calculation model and the electric vehicle cluster adjustable power calculation model are aggregated to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

2. A method for evaluating the adjustable capacity of a distribution network according to claim 1, characterized in that: The clustering algorithm is used to cluster distributed power sources, distributed energy storage and electric vehicles based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data, and the distributed power source cluster, distributed energy storage cluster and electric vehicle cluster are obtained, including: The Fast Unfolding clustering algorithm is used to cluster the distributed power sources based on the wind power related data and the photovoltaic related data to obtain a distributed power source cluster; Using a fuzzy C-means clustering algorithm to cluster the distributed energy storage based on the energy storage related data to obtain a distributed energy storage cluster; An improved k-means clustering algorithm is used to cluster the electric vehicles based on the electric vehicle related data to obtain electric vehicle clusters.

3. A method for evaluating the adjustable capacity of a distribution network according to claim 2, characterized in that: The Fast Unfolding clustering algorithm is used to cluster the distributed power sources based on the wind power related data and the photovoltaic related data, and the obtained distributed power source cluster includes: Calculating the net load of the distributed power source node according to the distributed power output and load of the distributed power source node of the distribution network, and using the net load of the distributed power source node as the first grouping indicator; The reactive power regulation capacity and active power regulation capacity of distributed power sources are used as the second grouping index; Normalizing the first grouping index and the second grouping index respectively to obtain a normalized first grouping index and a normalized second grouping index; Calculating the similarity between distributed power supply nodes based on the normalized first grouping index and the normalized second grouping index; Randomly select the cluster where any distributed power source node is located and merge it with other clusters to obtain a new cluster; Calculating an incremental value of a modularity function corresponding to the new cluster based on the similarity between the distributed power supply nodes; If the maximum increment value of the current modularity function is greater than the first preset value, merging the cluster where any distributed power source node is located with the cluster corresponding to the maximum increment value of the modularity function to obtain a new cluster; If the maximum increment value of the current modularity function is not greater than the first preset value, the cluster where any distributed power source node is located is not merged with the cluster corresponding to the maximum increment value of the modularity function; The cluster is taken as a new distributed power node, and the step of calculating the similarity between distributed power nodes based on the normalized first clustering index and the normalized second clustering index is returned to execute until the maximum incremental value of the modularity function no longer changes, thereby obtaining a distributed power cluster.

4. The method for evaluating the adjustable capacity of a distribution network according to claim 2, characterized in that: The improved k-means clustering algorithm is used to cluster the electric vehicles based on the electric vehicle related data, and the obtained electric vehicle clusters include: Randomly select K electric vehicle nodes from the distribution network as the initial clustering centers; Calculate the distances between the remaining electric vehicle nodes and the K cluster centers; Determine the evaluation index of clustering effect according to the universal gravitation model; Assigning the remaining electric vehicle nodes corresponding to the maximum value of the evaluation index to the cluster center, and updating the cluster center; Determine whether the evaluation index reaches a second preset value or the current iteration number reaches a preset number. If so, stop the iteration to obtain the electric vehicle cluster. If not, return to execute the step of calculating the distance between the remaining electric vehicle nodes and the K cluster centers.

5. The method for evaluating the adjustable capacity of a distribution network according to claim 1, characterized in that: The method of establishing a distributed power supply cluster adjustable power calculation model, a distributed energy storage cluster adjustable power calculation model and an electric vehicle cluster adjustable power calculation model for the distributed power supply cluster, the distributed energy storage cluster and the electric vehicle cluster based on the wind power related data, the photovoltaic related data, the energy storage related data and the electric vehicle related data respectively comprises: Establishing a wind power cluster adjustable power calculation model based on the distributed power source cluster according to the wind power related data; Establishing a photovoltaic cluster adjustable power calculation model based on the distributed power supply cluster according to the photovoltaic related data; Establishing an adjustable power calculation model of a distributed energy storage cluster based on the distributed energy storage cluster according to the energy storage related data; An electric vehicle cluster adjustable power calculation model is established based on the electric vehicle cluster according to the electric vehicle related data.

6. A method for evaluating the adjustable capacity of a distribution network according to claim 5, characterized in that: The step of establishing a wind power cluster adjustable power calculation model based on the distributed power source cluster according to the wind power related data includes: Determine the output power of the wind turbine generator set according to the wind power related data; Determining the adjustable potential of a single wind power according to the output power of the wind turbine group; Determine a wind power cluster adjustable power calculation model according to the adjustable potential of the single wind power; The step of establishing a photovoltaic cluster adjustable power calculation model based on the distributed power supply cluster according to the photovoltaic related data includes: Determine the output power of the photovoltaic unit according to the photovoltaic related data; Determining the adjustable potential of a single photovoltaic generator according to the output power of the photovoltaic group; A photovoltaic cluster adjustable power calculation model is determined according to the adjustable potential of the single photovoltaic generator.

7. A method for evaluating the adjustable capacity of a distribution network according to claim 5, characterized in that: The step of establishing a distributed energy storage cluster adjustable power calculation model based on the distributed energy storage cluster according to the energy storage related data includes: Establish energy storage charging and discharging constraints; Determine the power of the energy storage load group according to the energy storage related data; A distributed energy storage cluster adjustable power calculation model is obtained based on the energy storage load group power and the energy storage charging and discharging constraints.

8. A method for evaluating the adjustable capacity of a distribution network according to claim 5, characterized in that: The step of establishing an adjustable power calculation model for an electric vehicle cluster based on the electric vehicle cluster according to the electric vehicle related data comprises: Establish battery safety capacity constraints and charge and discharge power constraints for electric vehicles; Determining a total response potential based on the charge and discharge response potentials of users of the electric vehicle; An adjustable power calculation model for an electric vehicle cluster is obtained according to the total response potential, the battery safety power constraint and the charge and discharge power constraint.

9. The method for evaluating the adjustable capacity of a distribution network according to claim 1, characterized in that: The adjustable power calculation model of the distributed power cluster, the adjustable power calculation model of the distributed energy storage cluster and the adjustable power calculation model of the electric vehicle cluster are aggregated to obtain the adjustable range and the adjustable upper and lower limit constraints of the regional distribution network flexibility resources, including: The Minkowski method is used to aggregate the adjustable power calculation model of the distributed power source cluster, the adjustable power calculation model of the distributed energy storage cluster and the adjustable power calculation model of the electric vehicle cluster to obtain the adjustable range and adjustable upper and lower limit constraints of the regional distribution network flexibility resources.

10. A terminal for evaluating the adjustable capacity of a distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step in the method for evaluating the adjustable capability of a distribution network according to any one of claims 1 to 9 is implemented.

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