Cluster division and model aggregation method and device considering energy storage wide area dispersion characteristics

Abstract

The invention belongs to the technical field of novel electric power system dispatching, and particularly relates to a cluster division and model aggregation method and device considering energy storage wide area dispersion characteristics, and the method comprises the steps: determining the target occurrence probability of a plurality of wind and light output typical scenes, meeting a first preset condition, of an electric power system, establishing a target feasible region space expression model of an energy storage system in the power system, thereby establishing a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system, further obtaining a cluster energy storage division result meeting a second preset condition, and determining a target energy storage model of each cluster by using the cluster energy storage division result; and all the target energy storage models are aggregated, so that the aggregated energy storage models are subjected to fitting processing, and an aggregation model meeting a third preset condition is obtained. Therefore, the problem that centralized scheduling is difficult due to the fact that the energy storage wide area is dispersed in the prior art is solved.

Description

Technical Field

[0001] The present application belongs to the field of new power system dispatching technology, and in particular relates to a cluster division and model aggregation method and device that considers the wide-area dispersion characteristics of energy storage. Background Art

[0002] In recent years, with the widespread application of renewable energy such as wind power and solar energy, the uncertainty and volatility of the power system have increased significantly. These uncertainties mainly come from the intermittent and unpredictable nature of renewable energy generation, which poses a challenge to the stable operation of traditional power systems. Among the related technologies, the large-scale development of diversified energy storage technologies has provided the power system with abundant flexible regulation resources, effectively coping with the risks brought by the uncertainty of the power system.

[0003] However, in the related technologies, due to the large number of energy storage resources and their widely dispersed layout, the complexity of power system scheduling increases, causing congestion in communication network lines, and the inability to guarantee the timeliness and accuracy of data transmission, which reduces the flexibility and reliability of the power system and needs to be urgently resolved. Summary of the invention

[0004] The present application provides a cluster division and model aggregation method and device that considers the wide-area dispersed characteristics of energy storage, so as to solve the problems in the related art that the large number of energy storage resources and the wide-area dispersed layout lead to increased complexity in power system scheduling, congestion in communication network lines, inability to ensure the timeliness and accuracy of data transmission, and reduced flexibility and reliability of the power system.

[0005] The first aspect of the present application provides a cluster division and model aggregation method that takes into account the wide-area dispersion characteristics of energy storage, including the following steps: obtaining multiple typical wind and solar power output scenarios of the power system that meet a first preset condition, and determining the target occurrence probability of the multiple typical wind and solar power output scenarios; based on the target occurrence probability, establishing a target feasible domain space representation model of the energy storage system in the power system, and using the target feasible domain space representation model to establish a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system; according to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, obtaining a cluster energy storage division result that meets a second preset condition, and using the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregating all target energy storage models to obtain an aggregated energy storage model, so as to fit the aggregated energy storage model to obtain an aggregated model that meets a third preset condition.

[0006] Optionally, in one embodiment of the present application, obtaining multiple typical wind and solar output scenarios of the power system that meet a first preset condition includes: using a preset target multi-stage scenario tree algorithm to obtain multiple wind and solar output scenarios for each region of the power system; using the multiple wind and solar output scenarios to obtain the multiple typical wind and solar output scenarios that meet the first preset condition.

[0007] Optionally, in one embodiment of the present application, establishing a target feasible domain spatial representation model of the energy storage system in the power system includes: performing power flow simulation calculations on the multiple typical wind and solar output scenarios to generate a net load fluctuation at each node of the power system; and establishing a target feasible domain spatial representation model of the energy storage system in the power system using the net load fluctuation and at least one target line transmission capacity constraint.

[0008] Optionally, in one embodiment of the present application, the cluster energy storage division result that meets the second preset condition is obtained according to the cluster energy storage response capability and the differentiated measurement mechanism of the aggregation model, including: clustering the energy storage under each node based on smoothing system net load fluctuations and multi-dimensional synergy benefits to obtain a preliminary division result; and analyzing the preliminary division result using the cluster energy storage response capability and the differentiated measurement mechanism of the aggregation model under multiple time scales to obtain the cluster energy storage division result that meets the second preset condition.

[0009] Optionally, in one embodiment of the present application, the target energy storage model is aggregated, and the aggregated energy storage model is subjected to fitting processing to obtain an aggregated model that satisfies a third preset condition, including: based on a preset Chino polyhedron algorithm, performing Minkowski summation in the form of a Chino polyhedron on the target energy storage model of each cluster to obtain the aggregated energy storage model; and performing target polyhedron cutting on the aggregated energy storage model to obtain the aggregated model that satisfies the third preset condition.

[0010] The second aspect of the present application provides a cluster division and model aggregation device that takes into account the wide-area dispersion characteristics of energy storage, including: an acquisition module, used to obtain multiple typical wind and solar power output scenarios of the power system that meet the first preset condition, and determine the target occurrence probability of the multiple typical wind and solar power output scenarios; an establishment module, used to establish a target feasible domain space representation model of the energy storage system in the power system based on the target occurrence probability, and use the target feasible domain space representation model to establish a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system; a processing module, used to obtain a cluster energy storage division result that meets the second preset condition according to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, and use the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all target energy storage models to obtain an aggregated energy storage model, so as to fit the aggregated energy storage model to obtain an aggregated model that meets the third preset condition.

[0011] Optionally, in one embodiment of the present application, the acquisition module includes: a first acquisition unit, used to use a preset target multi-stage scenario tree algorithm to obtain multiple wind and solar power output scenarios for each region of the power system; a second acquisition unit, used to use the multiple wind and solar power output scenarios to obtain the multiple typical wind and solar power output scenarios that meet the first preset condition.

[0012] Optionally, in one embodiment of the present application, the establishment module includes: a generation unit, used to perform power flow simulation calculations on the multiple typical wind and solar power output scenarios to generate a net load fluctuation at each node of the power system; an establishment unit, used to establish a target feasible domain space representation model of the energy storage system in the power system using the net load fluctuation and at least one target line transmission capacity constraint.

[0013] Optionally, in one embodiment of the present application, the processing module includes: a division unit, used to cluster the energy storage under each node based on smoothing system net load fluctuations and multi-dimensional synergy benefits to obtain a preliminary division result; an analysis unit, used to analyze the preliminary division result using the cluster energy storage response capability under multiple time scales and the differentiation measurement mechanism of the aggregation model to obtain the cluster energy storage division result that meets the second preset condition.

[0014] Optionally, in one embodiment of the present application, the processing module includes: a first processing unit, used to perform Minkowski summation in the form of a Chino polyhedron on the target energy storage model of each cluster based on a preset Chino polyhedron algorithm to obtain the aggregated energy storage model; a second processing unit, used to perform target polyhedron cutting on the aggregated energy storage model to obtain the aggregated model that meets the third preset condition.

[0015] A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage as described in the above embodiments.

[0016] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned cluster division and model aggregation method considering the wide-area distributed characteristics of energy storage.

[0017] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned cluster division and model aggregation method considering the wide-area distributed characteristics of energy storage.

[0018] The embodiment of the present application can determine the target occurrence probability of multiple typical wind and solar output scenarios that meet the first condition of the power system, and then establish a target feasible domain spatial representation model of the energy storage system in the power system, thereby establishing a differentiated measurement mechanism for the cluster energy storage response capability and aggregation model of the energy storage system, and then obtain the cluster energy storage division result that meets the second condition, and use the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all target energy storage models to fit the aggregated energy storage model to obtain an aggregation model that meets the third condition, effectively ensuring the timeliness and accuracy of data transmission, and improving the flexibility and reliability of the power system. As a result, it solves the problems of increased complexity in power system scheduling due to the large number of energy storage resources and their widely dispersed layout, causing congestion in communication network lines, and being unable to ensure the timeliness and accuracy of data transmission, and reducing the flexibility and reliability of the power system.

[0019] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flow chart of a cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage provided according to an embodiment of the present application;

[0022] Figure 2 A structural reference diagram of a multi-stage scene tree method according to a specific embodiment of the present application;

[0023] Figure 3A schematic diagram of adjusting the boundary of an energy storage model at multiple time scales according to a specific embodiment of the present application;

[0024] Figure 4 A logical schematic diagram of the boundary of an energy storage model at multiple time scales according to a specific embodiment of the present application;

[0025] Figure 5 A schematic diagram of a feasible region cutting surface of an energy storage model under various factors in a specific embodiment of the present application;

[0026] Figure 6 A schematic diagram of the structure of a cluster division and model aggregation device considering the wide-area dispersion characteristics of energy storage provided according to an embodiment of the present application;

[0027] Figure 7 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the cluster division and model aggregation method and device of the embodiment of the present application considering the wide-area dispersion characteristics of energy storage with reference to the accompanying drawings. In view of the problems mentioned in the background technology center above, such as the large number of energy storage resources and the wide-area dispersion layout, which lead to the increased complexity of power system scheduling, the congestion of communication network lines, the inability to ensure the timeliness and accuracy of data transmission, and the reduction of the flexibility and reliability of the power system, the present application provides a cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage. In this method, the target occurrence probability of multiple typical wind and solar output scenarios that meet the first condition of the power system can be determined, and then the target feasible domain space representation model of the energy storage system in the power system is established, so as to establish a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system, and then obtain the cluster energy storage division result that meets the second condition, and use the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all the target energy storage models, so as to fit the aggregated energy storage model to obtain the aggregation model that meets the third condition, effectively improving the flexibility and reliability of the power system. This solves the problems of increased complexity in power system scheduling due to the large number of energy storage resources and their widely dispersed layout, the inability to ensure the timeliness and accuracy of data transmission, and reduced flexibility and reliability of the power system.

[0030] Specifically, Figure 1A schematic flow chart of a cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the cluster division and model aggregation method considering the wide-area distributed characteristics of energy storage includes the following steps:

[0032] In step S101, a plurality of typical wind and solar power output scenarios of the power system that meet a first preset condition are obtained, and target occurrence probabilities of the plurality of typical wind and solar power output scenarios are determined.

[0033] In the embodiment of the present application, the first preset condition is to determine the conditions of the typical wind and solar power output scenario according to the wind power and photovoltaic output scenarios in various regions of the power system.

[0034] It can be understood that the embodiments of the present application can obtain multiple typical wind and solar power output scenarios of the power system that meet the first condition, and determine the probability of occurrence of each typical wind and solar power output scenario, so as to more accurately predict the output of wind power generation and photovoltaic power generation in the future period of time, and provide data support for power grid scheduling.

[0035] Among them, in one embodiment of the present application, multiple typical wind and solar power output scenarios of the power system that meet the first preset condition are obtained, including: using a preset target multi-stage scenario tree algorithm to obtain multiple wind and solar power output scenarios for each region of the power system; using multiple wind and solar power output scenarios to obtain multiple typical wind and solar power output scenarios that meet the first preset condition.

[0036] During the actual implementation process, the embodiment of the present application takes the minimum spanning tree algorithm as the core, and uses the multi-stage scenario tree algorithm to obtain multiple wind and solar power output scenarios in each region of the power system. Then, the genetic algorithm is combined with the mixed Gaussian model, and multiple wind and solar power output scenarios are used to obtain multiple wind and solar power output typical scenarios that meet the first condition. Among them, the embodiment of the present application can substitute multiple wind and solar power output scenarios into the genetic algorithm training, with the goal of minimizing the Euclidean distance of the data in the typical wind and solar power output scenarios, and quantify the occurrence probability value of each typical wind and solar power output scenario according to the error between the mixed Gaussian models, thereby effectively improving the accuracy of power system prediction.

[0037] For example, Figure 2 As shown, the multi-stage scene tree in the embodiment of the present application carries out scenery scene generation, with the minimum spanning tree algorithm as the core, and the specific formula is:

[0038]

[0039] Among them, T represents the spanning tree, v and u represent the vertices that are not included and the vertices that are already in the spanning tree, respectively, (u, v) represents the edge between two vertices, and V represents the set of vertices that have not yet been included. The core working principle of the minimum spanning tree algorithm is to select the edge with the smallest weight connected to the current spanning tree from the set of unselected vertices each time, and add the edge and the corresponding vertex to the spanning tree.

[0040] Secondly, the embodiment of the present application can obtain a typical scene of wind and solar power output, that is, substitute the generated wind and solar scene into the genetic algorithm for optimization. The core goal of the optimization is to minimize the Euclidean distance of the data at the same time point in each scene, and fit the generated data in combination with the Gaussian mixture model. The following is the probability density function of the Gaussian mixture model, namely:

[0041]

[0042] Among them, x represents the data point to be fitted, data K represents the number of Gaussian distributions, and π i represents the mixing coefficient of the i-th Gaussian distribution, satisfying N(x|μ i ,Σ i ) represents the i-th Gaussian distribution, μ i represents the mean, Σ i represents the covariance matrix.

[0043] Therefore, the embodiment of the present application can be used to calculate the probability of occurrence of a given data point through iterative calculation according to the determined model parameters, and then derive the probability value of occurrence of each typical wind and solar power output scenario.

[0044] In step S102, based on the target occurrence probability, a target feasible domain space representation model of the energy storage system in the power system is established, and the target feasible domain space representation model is used to establish a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system.

[0045] In an embodiment of the present application, the target feasible domain space representation model is a feasible domain space representation model that describes the flexibility regulation capability of energy storage.

[0046] It can be understood that the embodiments of the present application can establish a target feasible domain space representation model of the energy storage system in the power system based on the target occurrence probability. For example, the embodiments of the present application can be based on the uncertainty probability representation of wind power and photovoltaic power, taking into account the topological structure of the power system and the transmission capacity of each line, to establish a feasible domain space representation model that describes the flexibility adjustment capability of energy storage. Then, based on the feasible domain space representation model, with the goal of smoothing the net load fluctuation of the system and the optimization of multi-dimensional synergistic benefits of each energy storage in the following steps, a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system is established through a matching algorithm based on spatial hierarchical merging and grouping, that is, a cluster division method that measures the response capability of energy storage and the differentiation of aggregation models at multiple time scales, which effectively improves the flexibility of the power system.

[0047] Among them, in one embodiment of the present application, a target feasible domain space representation model of the energy storage system in the power system is established, including: performing power flow simulation calculations on multiple typical wind and solar power output scenarios to generate the net load fluctuation at each node of the power system; and establishing the target feasible domain space representation model of the energy storage system in the power system using the net load fluctuation and at least one target line transmission capacity constraint.

[0048] As a possible implementation method, the embodiment of the present application can take into account the grid structure and line carrying capacity of the power system, perform power flow simulation calculations on data of multiple typical wind and solar output scenarios to generate the net load fluctuation at each node of the power system, and use the net load fluctuation and line transmission capacity constraints to model the flexible adjustment feasible domain of the energy storage equipment at each node of the power system, that is, establish a target feasible domain space representation model of the energy storage system in the power system, so that the embodiment of the present application can improve the operating efficiency and stability of the power system by reasonably adjusting the energy storage equipment.

[0049] For example, the embodiments of the present application can perform power flow simulation calculations, wherein the principles of the simulation calculations are: first, the output consumption of wind power and photovoltaic power is satisfied; then, the output ranges of the controllable units in the power system are adjusted to the maximum value; the energy storage device temporarily does not consider capacity constraints, and selects charging and discharging actions based on the power surplus of the power system.

[0050] Next, the embodiment of the present application may consider the adjustable space description of energy storage flexibility constrained by line, wherein, in view of the differences of multi-heterogeneous energy storage devices, a generalized energy storage model is established to describe the feasible domain of energy storage devices for scheduling, that is, a feasible domain space description model of the energy storage system in the power system is established, namely:

[0051]

[0052] E min ≤E t ≤E max (4)

[0053] 0≤|P t+τ -P t |≤ΔP max (5)

[0054] 0≤P t ≤P l,max -P real (6)

[0055] Among them, the above formulas (3)-(5) respectively represent the power, capacity and climbing constraints of energy storage.

[0056] Among them, P t Indicates the power of energy storage, P t,ch With P t,dis They represent the charging and discharging power of the energy storage at time t, μ t,ch With μ t,dis Indicates the charge and discharge state of the corresponding energy storage at time t, and Respectively represent the maximum value of energy storage charging and discharging; E t Indicates the energy value corresponding to the energy storage time t, E min With E max represents the energy boundary of energy storage; P t+τ Indicates the power output of the energy storage after time τ, ΔP max represents the climbing constraint of energy storage; P l,max Indicates the maximum electric energy carrying capacity of the line connected to the energy storage, P real Indicates the flow that has passed through the route calculated by the flow simulation.

[0057] In step S103, a cluster energy storage division result that meets the second preset condition is obtained according to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, and the cluster energy storage division result is used to determine the target energy storage model of each cluster, and all target energy storage models are aggregated to obtain an aggregated energy storage model, so that the aggregated energy storage model is matched and processed to obtain an aggregated model that meets the third preset condition.

[0058] In the embodiment of the present application, the second preset condition is a condition for adapting to multi-time scale scheduling requirements; the third preset condition is a condition for multi-heterogeneous energy storage at the provincial level.

[0059] It can be understood that the embodiments of the present application can obtain cluster energy storage division results that adapt to multi-time scale scheduling needs based on the cluster energy storage response capability and the differentiated measurement mechanism of the aggregation model, and use the cluster energy storage division results to determine the energy storage model of each cluster, and aggregate all energy storage models. For example, the energy storage models within each cluster are aggregated using the Chino polyhedron method to obtain an aggregated energy storage model. Then, the aggregated energy storage model is adaptively multi-faceted by using model approximation to ensure the accuracy of the model, so that the aggregation model of multi-heterogeneous energy storage at the provincial level can be obtained, providing resource allocation and model aggregation work preparation for power system scheduling at the provincial level, effectively ensuring the timeliness and accuracy of data transmission, and improving the flexibility and reliability of the power system.

[0060] Optionally, in one embodiment of the present application, a cluster energy storage division result that meets the second preset condition is obtained according to a differentiated measurement mechanism of cluster energy storage response capability and aggregation model, including: clustering the energy storage under each node based on smoothing system net load fluctuations and multi-dimensional synergy benefits to obtain a preliminary division result; and analyzing the preliminary division result using a differentiated measurement mechanism of cluster energy storage response capability and aggregation model under multiple time scales to obtain a cluster energy storage division result that meets the second preset condition.

[0061] In some embodiments, the embodiments of the present application can cluster the energy storage under each node based on smoothing the net load fluctuations of the system and multi-dimensional synergistic benefits using a matching algorithm of spatial hierarchical merging and grouping in the spatial dimension, wherein the matching process aims to smooth the net load fluctuations of the system and optimize the multi-dimensional synergistic benefits to obtain a preliminary division result. Secondly, the preliminary division result can be analyzed using a differentiated measurement mechanism of cluster energy storage response capability and aggregation model at multiple time scales. For example, a measurement mechanism at multiple time scales is established in the time dimension, such as establishing a three-stage time scale, analyzing the response capability loss caused by the changes in the division of each energy storage cluster at the spatial dimension level between each stage, and the barrel effect loss caused by the differentiation between each energy storage in each model clustering process. As a result, it can not only help the power system better cope with instantaneous and long-term changes in power demand, but also optimize the configuration of energy storage resources, reduce unnecessary economic losses, and effectively improve the operating efficiency and stability of the power system.

[0062] For example, the embodiment of the present application can cluster the energy storage under each node. First, based on the simulated power flow calculation results of the power system, that is, the net load fluctuation under each node, a multi-dimensional coordinated output index between the source-load-storage matching index and the energy storage is established, as shown in the following formulas (7) and (8), respectively:

[0063]

[0064] Among them, λ1 represents the standard deviation of each node in the system within a scheduling cycle after considering energy storage to smooth the net load; P pure,i represents the net load power value at node i, represents the average net load power of node i in a cycle, P i represents the net load value at node i that has not yet been adjusted by energy storage; C i , With P i,max They represent the electricity cost, maximum capacity and maximum charging power of energy storage at node i respectively, and λ2 represents the continuous operation cost.

[0065] Next, the doctor index is calculated for all energy storage in the power system, and the goal is to maximize the total index result after comprehensive division.

[0066] Furthermore, the embodiment of the present application can perform multi-time scale cluster division evaluation, consider the impact of effective adaptation aggregation results on multi-time scale scheduling, divide the evaluation indicators from three time scales: day-ahead, intraday, and real-time, and comprehensively measure the impact of the aggregation effect on the time scale, that is:

[0067]

[0068] Among them, γ1 and γ2 represent the total loss caused to the system by the deviation of all cluster division results from the day-ahead stage to the intraday stage and from the intraday stage to the real-time stage; C re represents the unit adjustment cost of the overall cluster model after division; T and T' represent the time before and after the model adjustment respectively; P max With P' max Respectively represent the maximum power boundary of the model before and after adjustment; C loss represents the unit cost of fluctuation of net load of each node in the system after cluster adjustment; P pure,i With P' pure,i They respectively represent the net load change of node i after the node is charged and discharged through energy storage, and Δ represents the mathematical symbol of the change.

[0069] Secondly, the embodiment of the present application can perform a cluster division iterative algorithm, that is, use a spatial hierarchical merge group matching algorithm, which is mainly performed by iteratively calculating the losses caused by each energy storage participating in the cluster or across other clusters to participate in the regulation. In the specific grid structure, the group matching of spatial objects is achieved by hierarchical clustering of path points. In this process, the improved path similarity calculation formula is used:

[0070]

[0071] Among them, P A With P BRespectively represent the points on the paths A and B, d(P A ,P B ) represents the distance between corresponding points on paths A and B.

[0072] By calculating the economic penalty corresponding to each path, the changes in the path adjustment process are obtained through iterative calculation, so as to select the optimal path. By recalculating and adjusting the paths of the original divided clusters, that is, the preliminary division results, the division results that balance the time and space demands are finally obtained to ensure that each cluster can efficiently provide power support within its service area while minimizing the economic cost of the overall system. This can help the power system achieve the optimal allocation of resources while meeting the time and space demands.

[0073] For example, combining Figure 3 and Figure 4 As shown in the figure, the schematic diagram and logic diagram of the multi-time and space scale energy storage cluster division method are shown respectively. First, the energy storage is preliminarily clustered by establishing the source-load-storage matching index and the multi-dimensional synergy index between energy storage; then, considering the adaptability of the division effect to the multi-time scale scheduling process, taking into account the need for dynamic re-division of the division results at each stage, the multi-time scale division cost and division effect are comprehensively measured, and the index is integrated into the spatial hierarchical merged group matching algorithm, and finally the cluster energy storage division result that adapts to the multi-time scale scheduling needs is obtained.

[0074] Optionally, in one embodiment of the present application, the target energy storage models are aggregated, and the aggregated energy storage models are fit processed to obtain an aggregated model that meets a third preset condition, including: based on a preset Chino polyhedron algorithm, performing Minkowski summation in the form of a Chino polyhedron on the target energy storage model of each cluster to obtain the aggregated energy storage model; performing target polyhedron cutting on the aggregated energy storage model to obtain an aggregated model that meets the third preset condition.

[0075] In some embodiments, the embodiments of the present application can use the Chino polyhedron algorithm to describe the energy storage model within each cluster, use Minkowski summation to obtain the aggregated model, and consider the accuracy of the model. An adaptive polyhedron cutting method can be used to approximate the aggregated model, thereby improving the accuracy and computational efficiency of the model, and improving the accuracy and efficiency of energy storage device cluster management in the power system.

[0076] For another example, an embodiment of the present application can perform model aggregation based on the Chino polyhedron algorithm, wherein, due to the particularity of the Chino polyhedron morphology, it can be represented by three elements: center point, vector, and expansion set. When performing Minkowski summation on the energy storage model composed of Chino polyhedrons, the new aggregation model can be directly obtained by directly using the sum of center point coordinates, vector-vector sum, and expansion set algebraic sum.

[0077] Next, the embodiment of the present application can perform adaptive multi-faceted cutting on the new aggregation model, wherein the embodiment of the present application can perform plane cutting on the aggregated energy storage model. In order to effectively reduce the impact of the large capacity differences between multi-heterogeneous energy storage on power, the stretching of the Chino polyhedron in the capacity direction is converted into an optimization problem, namely:

[0078] minF(·)=E eq -E sum (C1,P1,C2,P2,...) (13)

[0079] Among them, E eq represents the capacity boundary of the re-equivalent aggregation model, E sum (C1, P1, C2, P2, ...) represents the initial state representation model of each energy storage inside the aggregation model at the output of P1, P2, etc., with reference to the corresponding energy storage cost C1, C2, etc. By re-optimizing the capacity boundary of the aggregation model before and after adjustment, an adaptive capacity boundary is obtained, which effectively improves the management efficiency of the energy storage cluster in the power system, and improves the overall performance and economic benefits of the power system.

[0080] For example, Figure 5 As shown, a feasible domain description model for adapting to the flexibility adjustment of multi-heterogeneous energy storage proposed according to an embodiment of the present application is shown, wherein the model refines the analysis of the energy storage output model by analyzing the carrying capacity and channel blockage of specific connecting lines; then, after the model is subjected to the Minkowski summation, the overall shape of the model remains unchanged, and considering the influence of the "barrel effect" in cluster energy storage, the preliminary aggregation model can be adjusted again, and through plane cutting, the boundary of the output model and the boundary of the equivalent model are approached inward, which effectively provides resource allocation and model aggregation work preparation for system scheduling at the provincial level.

[0081] According to the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage proposed in the embodiment of the present application, the target occurrence probability of multiple typical wind and solar output scenarios of the power system that meet the first condition can be determined, and then the target feasible domain spatial representation model of the energy storage system in the power system is established, thereby establishing a differentiated measurement mechanism for the cluster energy storage response capability and aggregation model of the energy storage system, and then obtaining the cluster energy storage division result that meets the second condition, and using the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all the target energy storage models to fit the aggregated energy storage model to obtain an aggregation model that meets the third condition, effectively improving the flexibility and reliability of the power system. As a result, the problems of increased complexity in power system scheduling due to the large number of energy storage resources and their wide-area dispersion are solved, and the timeliness and accuracy of data transmission cannot be guaranteed, which reduces the flexibility and reliability of the power system.

[0082] Next, a cluster division and model aggregation device considering the wide-area dispersion characteristics of energy storage proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0083] Figure 6 It is a block diagram of a cluster division and model aggregation device considering the wide-area dispersion characteristics of energy storage according to an embodiment of the present application.

[0084] like Figure 6 As shown, the cluster division and model aggregation device 10 considering the wide-area dispersion characteristics of energy storage includes: an acquisition module 100, a building module 200 and a processing module 300.

[0085] Specifically, the acquisition module 100 is used to acquire multiple typical wind and solar power output scenarios of the power system that meet the first preset condition, and determine the target occurrence probabilities of the multiple typical wind and solar power output scenarios.

[0086] Establish module 200, which is used to establish a target feasible domain space representation model of the energy storage system in the power system based on the target occurrence probability, and use the target feasible domain space representation model to establish a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system.

[0087] The processing module 300 is used to obtain a cluster energy storage division result that meets the second preset condition based on the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, and use the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all the target energy storage models to obtain an aggregated energy storage model, so as to fit the aggregated energy storage model to obtain an aggregated model that meets the third preset condition.

[0088] Optionally, in one embodiment of the present application, the acquisition module 100 includes: a first acquisition unit and a second acquisition unit.

[0089] Among them, the first acquisition unit is used to obtain multiple wind and solar power output scenarios in each region of the power system using a preset target multi-stage scenario tree algorithm.

[0090] The second acquisition unit is used to use multiple wind-solar output scenes to acquire multiple typical wind-solar output scenes that meet the first preset condition.

[0091] Optionally, in one embodiment of the present application, the establishing module 200 includes: a generating unit and an establishing unit.

[0092] Among them, the generation unit is used to perform power flow simulation calculations on multiple typical wind and solar power output scenarios to generate the net load fluctuation at each node of the power system.

[0093] A unit is established, which is used to establish a target feasible domain space representation model of an energy storage system in a power system by using a net load fluctuation amount and at least one target line transmission capacity constraint.

[0094] Optionally, in one embodiment of the present application, the processing module 300 includes: a dividing unit and an analyzing unit.

[0095] Among them, the division unit is used to cluster the energy storage under each node based on smoothing the net load fluctuation of the system and multi-dimensional synergy benefits to obtain preliminary division results.

[0096] The analysis unit is used to analyze the preliminary division result by using the cluster energy storage response capability and aggregation model differentiation measurement mechanism under multiple time scales to obtain the cluster energy storage division result that meets the second preset condition.

[0097] Optionally, in one embodiment of the present application, the processing module 300 includes: a first processing unit and a second processing unit.

[0098] The first processing unit is used to perform Minkowski summation in the form of a Chino polyhedron on the target energy storage model of each cluster based on a preset Chino polyhedron algorithm to obtain an aggregated energy storage model.

[0099] The second processing unit is used to perform target multi-faceted cutting on the aggregated energy storage model to obtain an aggregated model that meets a third preset condition.

[0100] It should be noted that the aforementioned explanation of the embodiment of the cluster division and model aggregation method considering the wide-area dispersed characteristics of energy storage is also applicable to the cluster division and model aggregation device considering the wide-area dispersed characteristics of energy storage of this embodiment, and will not be repeated here.

[0101] According to the cluster division and model aggregation device that takes into account the wide-area dispersed characteristics of energy storage proposed in the embodiment of the present application, the target occurrence probability of multiple typical wind and solar output scenarios of the power system that meet the first condition can be determined, and then a target feasible domain spatial representation model of the energy storage system in the power system is established, thereby establishing a differentiated measurement mechanism for the cluster energy storage response capability and aggregation model of the energy storage system, and then obtaining a cluster energy storage division result that meets the second condition, and using the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all target energy storage models to fit the aggregated energy storage model to obtain an aggregation model that meets the third condition, effectively improving the flexibility and reliability of the power system. As a result, the problems of increased complexity in power system scheduling due to the large number of energy storage resources and their widely dispersed layout, the inability to ensure the timeliness and accuracy of data transmission, and reduced flexibility and reliability of the power system are solved.

[0102] Figure 7A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0103] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0104] When the processor 702 executes the program, the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage provided in the above embodiment is implemented.

[0105] Furthermore, the electronic device further comprises:

[0106] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0107] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0108] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0109] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0110] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0111] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0112] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage.

[0113] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0115] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0116] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0118] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A clustering and model aggregation method considering the wide-area dispersion characteristics of energy storage, characterized in that: The following steps are involved: Acquire multiple typical wind and solar power output scenarios of the power system that meet a first preset condition, and determine target occurrence probabilities of the multiple typical wind and solar power output scenarios; Based on the target occurrence probability, a target feasible domain space representation model of the energy storage system in the power system is established, and the cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system is established by using the target feasible domain space representation model; According to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, a cluster energy storage division result that meets the second preset condition is obtained, and the cluster energy storage division result is used to determine the target energy storage model of each cluster, and all target energy storage models are aggregated to obtain an aggregated energy storage model, so as to fit the aggregated energy storage model to obtain an aggregated model that meets the third preset condition.

2. The method according to claim 1, characterized in that The multiple typical wind and solar power output scenarios of the power system that meet the first preset condition include: Using a preset target multi-stage scenario tree algorithm, a plurality of wind and solar power output scenarios are obtained for each region of the power system; The multiple wind-solar power output scenes are used to obtain the multiple wind-solar power output typical scenes that meet the first preset condition.

3. The method according to claim 1, characterized in that The step of establishing a target feasible domain spatial representation model of the energy storage system in the power system includes: Performing power flow simulation calculation on the multiple typical wind and solar power output scenarios to generate net load fluctuation at each node of the power system; A target feasible domain space representation model of the energy storage system in the power system is established by using the net load fluctuation and at least one target line transmission capacity constraint.

4. The method according to claim 3, characterized in that The step of obtaining a cluster energy storage division result that meets a second preset condition according to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism includes: Based on smoothing the net load fluctuation of the system and multi-dimensional synergy benefits, the energy storage under each node is clustered to obtain a preliminary division result; The preliminary division result is analyzed using the cluster energy storage response capability under multiple time scales and the aggregation model differentiation measurement mechanism to obtain the cluster energy storage division result that meets the second preset condition.

5. The method according to claim 1, characterized in that: The aggregating the target energy storage model and performing fitting processing on the aggregated energy storage model to obtain an aggregated model that meets a third preset condition includes: Based on a preset Chino polyhedron algorithm, performing Minkowski summation in Chino polyhedron form on the target energy storage model of each cluster to obtain the aggregated energy storage model; The aggregated energy storage model is subjected to target multi-faceted cutting to obtain the aggregated model that meets the third preset condition.

6. A cluster division and model aggregation device considering the wide-area dispersion characteristics of energy storage, characterized in that: include: An acquisition module, used to acquire multiple typical wind and solar power output scenarios of the power system that meet a first preset condition, and determine target occurrence probabilities of the multiple typical wind and solar power output scenarios; Establishing a module for establishing a target feasible domain space representation model of the energy storage system in the power system based on the target occurrence probability, and establishing a cluster energy storage response capability and aggregation model differentiation measurement mechanism of the energy storage system using the target feasible domain space representation model; A processing module is used to obtain a cluster energy storage division result that meets the second preset condition according to the cluster energy storage response capability and the aggregation model differentiation measurement mechanism, and use the cluster energy storage division result to determine the target energy storage model of each cluster, and aggregate all the target energy storage models to obtain an aggregated energy storage model, so as to fit the aggregated energy storage model to obtain an aggregated model that meets the third preset condition.

7. The device according to claim 6, characterized in that The acquisition module comprises: A first acquisition unit is used to obtain multiple wind and solar power output scenarios in each region of the power system by using a preset target multi-stage scenario tree algorithm; The second acquisition unit is used to use the multiple wind-solar output scenes to acquire the multiple wind-solar output typical scenes that meet the first preset condition.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the cluster division and model aggregation method considering the wide-area dispersion characteristics of energy storage as described in any one of claims 1 to 5.

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