Construction of wide-area distributed energy storage aggregation model, scheduling method and device

Abstract

The application provides a construction and scheduling method and device of an energy storage aggregation model, equipment and a storage medium. The method comprises the following steps: constructing a decentralized energy storage aggregation index three-layer model, which comprises a target layer, an index layer and a scheme layer; determining the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair; constructing a decentralized energy storage aggregation system energy function based on the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair; grouping the energy storage units based on the decentralized energy storage aggregation system energy function to form a plurality of energy storage unit groups; obtaining a test sequence; using an SOC balanced allocation strategy to obtain the external characteristic representation parameter of each energy storage unit group based on the test sequence; and generating a wide-area decentralized energy storage aggregation model. The application can aggregate the cross-regional decentralized energy storage, and realize the coordinated cooperation between the energy storage and the power grid of the decentralized energy storage.

Description

[0001] The embodiment of the application relates to the technical field of power systems, in particular to a construction method and device of a wide-area distributed energy storage aggregation model and a scheduling method.

[0002] In recent years, various types of energy storage are built and operated in power systems year by year, and have made great contributions to the regulation of wind and light fluctuations and the regulation of system resources.

[0003] However, in the current energy storage scheduling process, the distributed energy storage in each region is limited to local coordination and scheduling. With the continuous construction of future energy storage devices, the large-scale grid connection of energy storage will inevitably increase the computational burden of scheduling. At the same time, due to the spatial distance of distributed energy storage, long-distance (especially cross-regional) power grids cannot fully utilize the scheduling resources of energy storage in each region in the process of new energy consumption, resulting in inevitable idle resources.

[0004] Therefore, how to aggregate the models of cross-regional distributed energy storage and determine the external characteristics of the energy storage aggregation model suitable for system scheduling to realize the coordination between the distributed energy storage and the grid load has become a technical problem to be solved by those skilled in the art.

[0005] Therefore, the application provides a construction method and device of a wide-area distributed energy storage aggregation model, which can aggregate the models of cross-regional distributed energy storage and determine the external characteristics of the energy storage aggregation model suitable for system scheduling to realize the coordination between the distributed energy storage and the grid load.

[0006] In a first aspect, the application provides a construction method of a wide-area distributed energy storage aggregation model, which specifically includes the following steps:

[0007] Specifically includes the following steps:

[0008] A distributed energy storage aggregation index three-layer model is constructed, the distributed energy storage aggregation index three-layer model comprising: a target layer, an index layer and a scheme layer, the index layer comprising: at least one distributed energy storage operation index, and the scheme layer comprising at least one energy storage unit;

[0009] The scale parameters of each energy storage unit and the matching degree parameters of each energy storage unit pair are determined, wherein the energy storage unit pair is two energy storage units in the energy storage units;

[0010] ​​​construct an energy function of the distributed energy storage aggregation system based on the scale parameter of each of the energy storage units and the matching degree parameter of each pair of energy storage units, group the energy storage units based on the energy function of the distributed energy storage aggregation system, and form a plurality of energy storage unit groups;

[0011] obtain a test sequence, obtain an external characteristic representation parameter of each of the energy storage unit groups based on the test sequence and the allocation strategy of the SOC balance, and generate a wide-area distributed energy storage aggregation model.

[0012] Through the present application, the distributed energy storage across regions can be aggregated in a model, and the external characteristics of the energy storage aggregation model suitable for system scheduling can be determined, so as to realize the coordinated cooperation between the distributed energy storage and the grid load.

[0013] A possible way is that in the step of determining the scale parameter of each of the energy storage units and the matching degree parameter of each pair of energy storage units, the scale parameter of any energy storage unit existing in the energy storage units is determined in the following way:

[0014] construct a judgment matrix based on the importance degree sorting result of each distributed energy storage operation index in the index layer;

[0015] obtain the scale parameter of the any energy storage unit based on the judgment matrix;

[0016] In the step of obtaining the scale parameter of the any energy storage unit based on the judgment matrix, the scale parameter of the any energy storage unit is determined by using the following formula:

[0017]

[0018] s— the scale parameter of the energy storage unit;

[0019] c k — the kth distributed energy storage operation index;

[0020] w ok — the eigenvector of the corresponding value of the kth distributed energy storage operation index determined based on the judgment matrix.

[0021] A possible way is that in the step of determining the scale parameter of each of the energy storage units and the matching degree parameter of each pair of energy storage units, the matching degree parameter of the energy storage unit pair composed of node i and node j is determined in the following way:

[0022] obtain the electrical distance between node i and node j;

[0023] express the edge weight of the node by the electrical distance between node i and node j, and determine the matching degree parameter of the energy storage unit pair composed of node i and node j.

[0024] The step of calculating the electrical distance between node i and node j comprises:

[0025] The size of the influence of the reactive power of each node on the voltage of node i and node j is calculated based on the voltage-to-reactive power sensitivity matrix,

[0026] The electrical distance between node i and node j is calculated based on the size of the influence of the reactive power of each node on the voltage of node i and node j.

[0027] One possible way is that the distributed energy storage aggregation system energy function is constructed based on the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair, and the energy storage units are grouped based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups in the step of grouping the energy storage units based on the distributed energy storage aggregation system energy function,

[0028] The distributed energy storage aggregation system energy function is:

[0029]

[0030] s i The scale parameter of energy storage unit i;

[0031] s j The scale parameter of energy storage unit j;

[0032] p ij The matching degree parameter of energy storage unit pair formed by node i and node j;

[0033] δ ij (X) When the energy storage aggregation located at node i and node j is in the same cluster, δ ij (X) = 1, otherwise δ ij (X) = 0.

[0034] In the process of grouping the energy storage units based on the distributed energy storage aggregation system energy function, the grouping result of the energy storage units is the grouping result of the energy storage units determined by the minimum value of the distributed energy storage aggregation system energy function.

[0035] One possible way is that the test sequence is obtained, and the external characteristic representation parameter of each energy storage unit group is calculated based on the test sequence using the SOC balanced allocation strategy in the step of obtaining the test sequence, and the test sequence is determined based on the decomposition of the net load curve, and the net load curve is determined based on the daily output curve of the wind power and photovoltaic power station and the daily load curve.

[0036] In a second aspect, the application provides a wide-area distributed energy storage oriented scheduling method, which comprises the wide-area distributed energy storage aggregation model of the first aspect, and specifically comprises the following steps:

[0037] Based on the wide-area-oriented distributed energy storage aggregation model, a wind and light curtailment rate objective function is established based on thermal power and energy storage system constraints and distributed energy storage topology structure, and charging and discharging power of the energy storage units in the distributed energy storage aggregation model is optimized.

[0038] In a third aspect, the application provides a wide-area-oriented distributed energy storage aggregation model construction device, comprising:

[0039] A construction module is configured to construct a distributed energy storage aggregation index three-layer model, wherein the distributed energy storage aggregation index three-layer model comprises a target layer, an index layer and a scheme layer, the index layer comprises at least one distributed energy storage operation index, and the scheme layer comprises at least one energy storage unit.

[0040] A determination module is configured to determine a scale parameter of each energy storage unit and a matching degree parameter of each energy storage unit pair, wherein the energy storage unit pair refers to two energy storage units in the energy storage units.

[0041] A grouping module is configured to construct a distributed energy storage aggregation system energy function based on the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair, group the energy storage units based on the distributed energy storage aggregation system energy function, and form a plurality of energy storage unit groups.

[0042] An acquisition module is configured to acquire a test sequence, use an SOC balanced allocation strategy to obtain an external characteristic representation parameter of each energy storage unit group based on the test sequence, and generate a wide-area-oriented distributed energy storage aggregation model.

[0043] In a fourth aspect, the application provides a wide-area-oriented distributed energy storage scheduling device, comprising the wide-area-oriented distributed energy storage aggregation model of the first aspect, and comprising:

[0044] A scheduling module is configured to establish a wind and light curtailment rate objective function based on the distributed energy storage aggregation model, thermal power and energy storage system constraints and distributed energy storage topology structure, and optimize charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0045] In a fifth aspect, an electronic device is provided, comprising:

[0046] at least one processor; and

[0047] at least one memory in communication with the processor, wherein:

[0048] The memory stores program instructions executable by the processor, and the processor invoking the program instructions can execute the method provided in the first aspect or the second aspect.

[0049] In a sixth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, which cause the computer to perform the method provided in the first aspect or the second aspect.

[0050] It should be understood that the second to sixth aspects of the embodiments of the present application are consistent with the technical solution of the first aspect of the embodiments of the present application, and the beneficial effects obtained by each aspect and the corresponding feasible implementation manners are similar, and will not be described again.

DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0052] Figure 1 A flow chart of a construction method of a wide-area distributed energy storage aggregation model is provided for the present application;

[0053] Figure 2 A three-layer model schematic diagram of a distributed energy storage aggregation index is provided for the present application;

[0054] Figure 3 A flow chart of an energy storage unit grouping method is provided for the present application;

[0055] Figure 4 A distributed energy storage scheduling model structure diagram is provided for the present application;

[0056] Fig. 5(a) is a construction device structure diagram of a wide-area distributed energy storage aggregation model provided for the present application;

[0057] Fig. 5(b) is a scheduling device structure diagram of a wide-area distributed energy storage provided for the present application;

[0058] Figure 6 An electronic device structure diagram is provided for the embodiments of the present application.

CONCRETE EMBODIMENT

[0059] In order to better understand the technical solutions of the embodiments of the present application, the embodiments of the present application will be described in detail below with reference to the drawings.

[0060] It should be noted that the described embodiments are merely some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the embodiments of the present application.

[0061] The terms used in the embodiments of the present application are merely for the purpose of describing the specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0062] In recent years, various types of energy storage have been increasing in the power system year by year, and have made great contributions to the regulation of wind and light fluctuations and the regulation of system resources.

[0063] However, in the current energy storage scheduling process, the distributed energy storage in each region is limited to local coordination and scheduling. With the continuous construction of future energy storage devices, the large-scale grid connection of energy storage will inevitably increase the computational burden of scheduling. At the same time, due to the spatial distance of distributed energy storage, long-distance (especially cross-regional) power grids cannot fully utilize the scheduling resources of energy storage in each region in the process of new energy consumption, resulting in inevitable idle resources.

[0064] Therefore, how to aggregate the distributed energy storage across regions and determine the external characteristics of the energy storage aggregation model suitable for system scheduling to realize the coordination between the distributed energy storage and the grid load storage has become a technical problem to be solved by those skilled in the art.

[0065] In order to solve the above problems, the present application provides a construction and scheduling method and device for wide-area distributed energy storage aggregation model, electronic equipment and storage medium, so as to aggregate the distributed energy storage across regions and determine the external characteristics of the energy storage aggregation model suitable for system scheduling, and realize the coordination between the distributed energy storage and the grid load storage.

[0066] First of all, the terms involved in the present application are described:

[0067] 1. Analytic hierarchy process: a complex multi-objective decision-making problem is regarded as a system, the system is divided into multiple indexes at multiple levels, the ranking of each level is calculated by a qualitative index fuzzy quantization method, the problem is reduced to the weight or order of preference of the lowest layer relative to the highest layer, which is suitable for decision-making problems with hierarchical and staggered evaluation indexes, and the index value is difficult to quantify.

[0068] The specific steps are as follows:

[0069] 1) Establish the hierarchy of index evaluation, the lowest layer is the alternative scheme, the highest layer is the overall goal, and the intermediate layer is the index layer, which can be flexibly selected according to different application scenarios;

[0070] 2) Refer to the 1-9 scale method determined by Saaty to compare each index in each index layer with each other, to h i,j indicate the importance of index i relative to index j, and construct a judgment matrix H=(h i,j ) n×n ; Calculate the maximum eigenvalue of the judgment matrix and the corresponding eigenvector, and perform consistency check, if the consistency check is passed, it can be considered that the error of the constructed matrix H is small, and the above eigenvector after normalization is the weight coefficient of all indexes in the index layer, otherwise the judgment matrix needs to be modified until the consistency check is passed.

[0071] The construction method of a wide-area distributed energy storage aggregation model provided in the present application is described below, with reference to Figure 1 , and specifically includes the following steps:

[0072] S101: Construct a three-layer model of distributed energy storage aggregation index.

[0073] As known from the foregoing, the three-layer model of distributed energy storage aggregation index includes: a target layer, an index layer, and a scheme layer, the index layer includes at least one distributed energy storage operation index, and the scheme layer includes at least one energy storage unit.

[0074] For example, with reference to Figure 2 , the target layer A is: distributed energy storage aggregation index, and the index layer C includes: rated capacity, charge and discharge power, response time, cost per kilowatt-hour, and charge and discharge efficiency.

[0075] The scheme layer p includes a plurality of energy storage units.

[0076] Suppose the total number of indexes is n, and the kth index is denoted as c k . The scheme layer is all distributed energy storages to be aggregated, denoted as P layer, and suppose the total number of energy storages is m, and the ith energy storage unit is denoted as p i .

[0077] At this time, the three-layer model of distributed energy storage aggregation index is constructed according to the aforementioned analytic hierarchy process.

[0078] S102: Determine the size parameters of each energy storage unit and the matching degree parameters of each energy storage unit pair.

[0079] Wherein, the energy storage unit pair is two energy storage units in the energy storage unit.

[0080] On the basis of S101, one possible design is that in this step, the aforementioned analytic hierarchy process is first used to perform single-layer sorting on the index layer, that is, the aforementioned 1-9 scale method determined by Saaty is used to compare each index in each index layer with each other, that is, in this step, first, the importance of each distributed energy storage operation index in the index layer is sorted, and a judgment matrix is constructed based on the sorting result.

[0081] When the judgment matrix is constructed, the scale parameter of any energy storage unit is calculated based on the judgment matrix.

[0082] Specifically, first, the eigenvector corresponding to the maximum eigenvalue of the judgment matrix is calculated, and in this case, the eigenvector is represented as: w0=[w 01 ,w 02 ,...,w 0n ], and then the scale parameter of the energy storage unit is calculated using the eigenvector.

[0083] Specifically, the aggregation index of the i i th energy storage unit p

[0084]

[0085] s—scale parameter of the energy storage unit

[0086] c k —kth distributed energy storage operation index;

[0087] w ok —eigenvector corresponding to the value of the kth distributed energy storage operation index determined based on the judgment matrix.

[0088] For the matching degree parameter, first, S102a is performed to calculate the electrical distance between node i and node j.

[0089] Specifically, to calculate the electrical distance between node i and node j, first, the sensitivity matrix of voltage to reactive power is used to calculate the size of the influence of the reactive power of each node on the voltage of node i and node j.

[0090] Further, taking node k as an example, d ij represents the size of the influence of the change of the reactive power of node i on the voltage of node k, and its formula is:

[0091]

[0092] where S VQ,kk —ratio of the change rate of the reactive power of node k to the change rate of the voltage of node k;

[0093] s VQ,ikThe ratio of the reactive power change rate of node k to the voltage change rate of node i.

[0094] Then, the electrical distance between node i and node j is calculated based on the magnitude of the influence of the respective reactive power on the voltage of node i and node j.

[0095] Specifically, the electrical distance between node i and node j can be calculated by the following formula:

[0096]

[0097] d ik represents the magnitude of the influence of the reactive power change of node i on the voltage of node k;

[0098] d jk represents the magnitude of the influence of the reactive power change of node j on the voltage of node k.

[0099] In this regard, the electrical distance is not directly determined by node i and node j, but is calculated by the magnitude of the influence of the reactive power of each node in the distributed energy storage topology on the voltage of node i and node j, because the network topology and the influence of other nodes are considered, and the result calculated by this method is more accurate. Thus, the electrical distance between two nodes is calculated.

[0100] For the method of calculating the electrical distance, the above-mentioned scheme is only one implementation method for those skilled in the art, and the electrical distance is calculated by those skilled in the art, which is not limited here.

[0101] Thus, the electrical distance between two points is calculated, and then S102a is performed: the edge weight of the node is represented by the electrical distance between node i and node j, and the matching degree parameter of the energy storage unit pair composed of node i and node j is determined.

[0102] Specifically, the concept of modularity in community discovery is used to map to the distributed energy storage system, and the matching degree is used to represent the strength of the electrical coupling between two energy storage units.

[0103] Further, the matching degree parameter of the energy storage unit pair composed of node i and node j can be represented as:

[0104]

[0105] wherein, L ij the electrical distance between node i and node j;

[0106] L i =∑ j L ij represents the sum of the electrical distances of all nodes connected to node i;

[0107] L j =∑ i L ij denotes the sum of the electrical distances of all nodes connected to node j;

[0108] δ(i,j) = 1 when the energy storages located at node i and node j are in the same cluster, otherwise δ(i,j) = 0.

[0109] S103: constructing a distributed energy storage aggregation system energy function based on the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair, grouping the energy storage units based on the distributed energy storage aggregation system energy function, and forming a plurality of energy storage unit groups;

[0110] Specifically, in this step, for a known group X, the distributed energy storage aggregation system energy function of m energy storage units is:

[0111]

[0112] s i — the scale parameter of energy storage unit i;

[0113] s j — the scale parameter of energy storage unit j;

[0114] p ij — the matching degree parameter of the energy storage unit pair composed of node i and node j;

[0115] δ ij (X) — δ ij (X) = 1 when the energy storages located at node i and node j are in the same cluster, otherwise δ ij (X) = 0.

[0116] The grouping result of the energy storage units can be determined in the following steps:

[0117] The grouping original information T of the energy storage units and the number of clusters K are obtained, the distributed energy storage aggregation system energy is calculated, the grouping information T' of the energy storage units is regenerated in the form of moving and replacing groups of all members, and the distributed energy storage aggregation system energy is recalculated.

[0118] Understandably, the grouping information T' of the energy storage units can determine a plurality of distributed energy storage aggregation system energy function values, and for convenience of description, the plurality of distributed energy storage aggregation system energy function values determined by the grouping information T' of the energy storage units are defined as a set A, and the distributed energy storage aggregation system energy function value determined by the grouping original information T of the energy storage units is defined as a.

[0119] Specifically, an iterative method can be used, for example, assuming the form of mobile group re-generation of the grouping information T' = {T1, T2...T of the energy storage unit N If it is assumed that the energy of the distributed energy storage aggregation system corresponding to T1 < the energy of the distributed energy storage aggregation system corresponding to the grouping information T of the current energy storage unit, the grouping original information of the current energy storage unit is modified based on T1 to become T, that is, the grouping information of the current energy storage unit becomes T1. If it is assumed that the energy of the distributed energy storage aggregation system corresponding to T2 < the energy of the distributed energy storage aggregation system corresponding to the grouping information T1 of the current energy storage unit, the grouping information of the current energy storage unit becomes T2, and so on.

[0120] The min(A, a) is taken as the grouping original information of the energy storage unit of the cluster number K+1, and the above operation is repeatedly performed until K = m-1.

[0121] In the above manner, the final grouping result of the energy storage unit can be determined.

[0122] S104: Obtain a test sequence, use the SOC balanced allocation strategy based on the test sequence to obtain the external characteristic representation parameter of each energy storage unit group, and generate a wide-area distributed energy storage aggregation model.

[0123] Here, the test sequence is determined based on decomposition of a net load curve, and the net load curve is determined based on the daily output curve of a wind power plant and a photovoltaic power plant and the daily load curve.

[0124] Specifically, first, the daily output curve of the wind power plant and the photovoltaic power plant is obtained, then the k-means clustering is used to cluster the output curves of the wind power plant and the photovoltaic power plant respectively, and the clustering centers are obtained as typical scenarios of wind and light output.

[0125] At the same time, since the daily fluctuation of the load is relatively small, the average value of the daily load curve is directly obtained, and the typical scenario of the load is obtained. Therefore, the typical scenario of the load minus the typical scenario of the wind and light output is the net load curve of the typical scenario. The relatively stable base power in the net load can be considered to be satisfied by the conventional unit, and the fluctuating component can be smoothed by the energy storage system, so the net load curve is decomposed, and the reciprocal of the alternating component obtained by the decomposition is taken as the test sequence, representing the power demand of the energy storage as a whole.

[0126] Because the SOC difference between individuals is too large to affect the stability of the operating state and external characteristics of the whole after energy storage aggregation, it is necessary to consider maintaining the SOC balance between energy storage units. The charging and discharging power of the energy storage is corrected using the Sigmoid function, so that the energy storage units with higher SOC participate more in the discharging process, and the energy storage with lower SOC participates more in the charging, and finally the SOC values of all energy storages in the cluster tend to be consistent. The test sequence is used to test the energy storage aggregation scheduling model to obtain stable external characteristic parameters, and the power allocation is carried out inside the energy storage cluster according to the SOC balancing strategy. Taking the charging power as an example, the strategy can be expressed as:

[0127]

[0128] P C,i , P N,i represent the charging power and rated power of the i th energy storage unit respectively;

[0129] f C (SOC i ) represents the Sigmoid function of charging, taking the SOC value of the i th energy storage as the independent variable;

[0130] P C,j , P N,j represent the charging power and rated power of the j th energy storage unit respectively;

[0131] f C (SOC j ) represents the Sigmoid function of charging, taking the SOC value of the j th energy storage as the independent variable.

[0132] ∑ i P C,i = P C,total ;

[0133] P C,total represents the charging power of the whole energy storage cluster

[0134] Therefore, the charging power of the i th energy storage unit is:

[0135]

[0136] In the formula, P C,i is the charging power of the i th energy storage unit;

[0137] SOC i max represents the maximum value of the SOC of the i th energy storage unit, E N,i is the rated capacity of the energy storage i, and η C,i is the charging efficiency of the energy storage i.

[0138] The expression of the energy storage discharge power can be derived in the same way, the power distribution within the cluster is performed according to the SOC balancing strategy, the upper and lower limit parameters when the power and available capacity are relatively stable are obtained by testing the external characteristics of the energy storage aggregation, and the energy storage aggregation scheduling model is obtained.

[0139] Specifically, the external characteristic representation parameters of the energy storage unit group include but are not limited to: the capacity of the energy storage unit, the maximum charge and discharge power, and the degree of electricity cost.

[0140] The energy storage aggregation model can be constructed in the above manner, and the application has the following beneficial effects:

[0141] 1) Construct an energy function to model the dispersed energy storage;

[0142] 2) Determine the external characteristic representation parameters of each energy storage unit by using the net load curve and the SOC balancing distribution strategy, which is suitable for system scheduling and realizes the coordinated cooperation between the dispersed energy storage and the grid.

[0143] On the basis of the foregoing embodiment, the application provides a scheduling method for wide-area dispersed energy storage, specifically comprising the following steps:

[0144] Based on the wide-area dispersed energy storage aggregation model, the wind and light curtailment rate objective function is established based on the thermal power and energy storage system constraints and the dispersed energy storage topology structure, and the charge and discharge power of the energy storage unit in the dispersed energy storage aggregation model is optimized.

[0145] Specifically, referring to Figure 4 In the dispersed energy storage scheduling model provided by the application, the upper model includes the foregoing dispersed energy storage aggregation model, and for the construction method of the dispersed energy storage aggregation model, it is not repeated here.

[0146] Here, based on the thermal power and energy storage system constraints and the dispersed energy storage topology structure, the wind and light curtailment rate objective function is established, and the upper model can obtain the charge and discharge power of the aggregation model.

[0147] The lower model decomposes the upper model to realize the economic optimal distribution within the energy storage cluster.

[0148] Referring to FIG. 5(a), the application provides a construction device for a wide-area dispersed energy storage aggregation model, comprising:

[0149] The construction module is used for constructing a dispersed energy storage aggregation index three-layer model, the dispersed energy storage aggregation index three-layer model comprising: a target layer, an index layer and a scheme layer, the index layer comprising: at least one dispersed energy storage operation index, and the scheme layer comprising at least one energy storage unit;

[0150] A determining module is configured to determine a scale parameter of each of the energy storage units and a matching degree parameter of each pair of energy storage units, wherein the pair of energy storage units refers to two of the energy storage units;

[0151] A grouping module is configured to construct a distributed energy storage aggregation system energy function based on the scale parameter of each of the energy storage units and the matching degree parameter of each pair of energy storage units, group the energy storage units based on the distributed energy storage aggregation system energy function, and form a plurality of groups of energy storage units;

[0152] An obtaining module is configured to obtain a test sequence, obtain an external characteristic representation parameter of each of the groups of energy storage units by using an SOC-balanced allocation strategy based on the test sequence, and generate a wide-area distributed energy storage aggregation model.

[0153] The construction device for the wide-area distributed energy storage aggregation model provided in the embodiment shown in FIG. 5(a) can be used to implement the embodiment of the present application Figure 1 The technical scheme, implementation principle and technical effects of the method embodiment can be further referred to the related description in the method embodiment.

[0154] Referring to FIG. 5(b), the present application provides a scheduling device for wide-area distributed energy storage, which comprises:

[0155] A scheduling module is configured to establish a wind and light curtailment rate objective function based on the distributed energy storage aggregation model, the thermal power and energy storage system constraints and the distributed energy storage topology, and optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0156] Figure 6 A structural schematic diagram of an embodiment of an electronic device of the embodiment of the present application is shown in FIG. Figure 6 The above electronic device can include at least one processor and at least one memory in communication with the processor, wherein the memory stores program instructions executable by the processor, and the processor calling the program instructions can execute the method for constructing a wide-area distributed energy storage aggregation model provided in the embodiment of the present application Figures 1 to 4 The method for constructing a wide-area distributed energy storage aggregation model provided in the embodiment shown in FIG.

[0157] Figure 6 A block diagram of an exemplary electronic device suitable for implementing the embodiment of the present application is shown. Figure 6 The electronic device shown is merely an example, and should not impose any limitation on the functions and use range of the embodiment of the present application.

[0158] As Figure 6As shown, the electronic device is in the form of a general purpose computing device. The components of the electronic device can include, but are not limited to, one or more processors 410, system memory 430, and a communication bus 440 that connects the various system components, including the system memory 430 and the processing unit 410.

[0159] The communication bus 440 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics bus (e.g., AGP or Accelerated Graphics Port), an input / output bus, and a local bus using any of a variety of bus architectures. By way of example, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0160] The electronic device typically includes a variety of computer system readable media. Such media can be any available media that is accessible by the electronic device and includes both volatile and non-volatile media, removable and non-removable media.

[0161] The memory 430 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device can further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 430 can include a program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

[0162] The program / utility, having a set (at least one) of program modules, can be stored in the memory 430 by way of example, and not limitation, an operating system, one or more application programs, other program modules, and program data, each or some combination thereof, can include implementation of a networking environment. The program modules are generally carried in the memories of the electronic device and implement the functions described in embodiments of the application.

[0163] The processor 410 performs a variety of functions as described in embodiments of the application described herein. These functions can include operations related to the execution of one or more software applications and / or the processing of data.Figures 1 to 4 The embodiment shown provides a construction method of a wide-area distributed energy storage aggregation model.

[0164] The embodiment of the present application provides a non-transitory computer readable storage medium storing computer instructions, which causes the computer to execute the embodiment of the present application Figures 1 to 4 The embodiment shown provides a construction method of a wide-area distributed energy storage aggregation model.

[0165] The computer readable storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0166] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which a computer readable program code is borne. Such a propagated data signal can take multiple forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can transmit, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device.

[0167] The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0168] Computer program code for carrying out operations of embodiments of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0169] The above description of specific embodiments of the present application has been presented for the purpose of illustration. Other embodiments are within the scope of the following claims. In some instances, the acts or steps described in the claims can be performed in a different order than the order described in the embodiments without altering the desired results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0170] In the description of embodiments of the present application reference has been made to descriptive terms, such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. which are intended to convey that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. The illustrative appearances of such terms in various places in the specification are not necessarily intended to convey the same meaning or significance in all instances. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Moreover, the descriptions of the various embodiments or examples of the present application have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments or examples described. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the present application. The terminology used is for the purpose of describing examples and is not intended to limit the scope of the present application.

[0171] Furthermore, the terms "first", "second", or the like, are used herein only to describe all possible combinations and do not connote any relative importance or order of precedence. Thus, the features defined with "first", "second" etc. can include at least one of the features. In the description of embodiments of the present application, the meaning of "a plurality" is at least two, for example, two, three, etc., unless otherwise specifically defined.

[0172] ​​​​Any processes or methods described in the flowcharts or elsewhere herein can be understood as representing one or more modules, segments, or portions of code that include executable instructions for performing specific logical functions or steps in the processes, and the various embodiments of the present application can include additional implementations in which additional, fewer, or none of the functions are performed, in different orders or in different manners, and that these claims should not be limited to the actions illustrated and described.

[0173] The word "if" can be interpreted to mean "upon" or "when" or "in response to determining," or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "upon determining," or "in response to determining," or "upon detecting," or "in response to detecting [the stated condition or event]," depending on the context.

[0174] It should be noted that the terminal involved in the embodiments of the present application can include, but is not limited to, a personal computer (PC), a personal digital assistant (PDA), a wireless handheld device, a tablet computer, a mobile phone, an MP3 player, an MP4 player, and the like.

[0175] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed system, device, and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0176] In addition, each function unit in the various embodiments of the embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software function units.

[0177] The integrated unit in the form of the software function unit can be stored in a computer readable storage medium. The software function unit is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute part of steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various storage medium capable of storing program codes.

[0178] The above merely describes preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing a wide-area distributed energy storage aggregation model, characterized in that, Specifically, the steps include the following: A three-layer model for distributed energy storage aggregation index is constructed, comprising: a target layer, an indicator layer, and a scheme layer. The indicator layer includes at least one distributed energy storage operation indicator, and the scheme layer includes at least one energy storage unit. Determine the scale parameters of each energy storage unit and the matching degree parameters of each pair of energy storage units, wherein the pair of energy storage units consists of two energy storage units in the energy storage unit; Based on the scale parameters of each energy storage unit and the matching degree parameters of each pair of energy storage units, a distributed energy storage aggregation system energy function is constructed. Based on the distributed energy storage aggregation system energy function, the energy storage units are grouped to form several energy storage unit groups. Obtain test sequences, and use the SOC equalization allocation strategy to obtain the external characteristic characterization parameters of each energy storage unit group based on the test sequences, and generate a wide-area distributed energy storage aggregation model. In the steps of determining the scale parameters of each energy storage unit and the matching degree parameters of each pair of energy storage units, the scale parameters of any energy storage unit, wherein any energy storage unit exists within the energy storage units, are determined in the following manner: The importance of each distributed energy storage operation indicator in the indicator layer is ranked, and a judgment matrix is ​​constructed based on the ranking results. The scale parameter of any energy storage unit is obtained based on the judgment matrix; In the step of obtaining the scale parameter of any energy storage unit based on the judgment matrix, the scale parameter of any energy storage unit is determined using the following formula: s—Scale parameter of the energy storage unit; c k —The kth distributed energy storage operation indicator; w ok —The feature vector corresponding to the k-th distributed energy storage operation index determined by the judgment matrix; In the step of constructing a distributed energy storage aggregation system energy function based on the scale parameters of each energy storage unit and the matching degree parameters of each energy storage unit pair, and grouping the energy storage units into several energy storage unit groups based on the distributed energy storage aggregation system energy function, The energy function of the distributed energy storage aggregation system is: s i —Scale parameters of energy storage unit i; s j —Scale parameters of energy storage unit j; p ij —The matching degree parameters of node i and node j constitute the energy storage unit pair; δ ij (X) — When energy storage located at nodes i and j is aggregated in the same cluster, δ ij (X) = 1, otherwise δ ij (X) = 0; During the process of grouping the energy storage units based on the energy function of the distributed energy storage aggregation system, the grouping result of the energy storage units is: the grouping result of the energy storage units determined by the minimum value of the energy function of the distributed energy storage aggregation system.

2. The method according to claim 1, characterized in that, In the step of determining the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair, the matching degree parameter of the energy storage unit pair consisting of node i and node j is determined in the following manner: Find the electrical distance between node i and node j; The edge weight of the nodes is represented by the electrical distance between nodes i and j, and the matching degree parameter of the energy storage unit pair formed by nodes i and j is determined. The steps for determining the electrical distance between node i and node j include: The magnitude of the impact of reactive power on the voltage at each node and the voltage at node j is determined based on the voltage-reactive power sensitivity matrix. The electrical distance between nodes i and j is determined based on the magnitude of the impact of the reactive power of each node on the voltage of node i and node j.

3. The method according to claim 1, characterized in that, In the step of obtaining the test sequence and using the SOC equalization allocation strategy to obtain the external characteristic characterization parameters of each energy storage unit group, the test sequence is determined based on the net load curve decomposition, and the net load curve is determined based on the daily power generation curve and daily load curve of the wind power and photovoltaic power plants.

4. A scheduling method for widely distributed energy storage, characterized in that, The widely distributed energy storage aggregation model constructed by the method described in any one of claims 1 to 3 specifically includes the following steps: Based on the aforementioned wide-area distributed energy storage aggregation model, and considering the constraints of thermal power and energy storage systems as well as the distributed energy storage topology, an objective function for wind and solar curtailment rate is established to optimize the charging and discharging power of energy storage units in the distributed energy storage aggregation model.

5. A device for constructing a wide-area distributed energy storage aggregation model, characterized in that, include: The building module is used to construct a three-layer model of distributed energy storage aggregation index. The three-layer model of distributed energy storage aggregation index includes: target layer, indicator layer and scheme layer. The indicator layer includes at least one distributed energy storage operation indicator, and the scheme layer includes at least one energy storage unit. Determination module: used to determine the scale parameters of each energy storage unit and the matching degree parameters of each pair of energy storage units, wherein the pair of energy storage units is two energy storage units in the energy storage unit; Grouping module: used to construct the energy function of the distributed energy storage aggregation system based on the scale parameter of each energy storage unit and the matching degree parameter of each pair of energy storage units, and to group the energy storage units based on the distributed energy storage aggregation system energy function to form several energy storage unit groups; Acquisition module: used to acquire test sequences, and based on the test sequences, use the SOC equalization allocation strategy to obtain the external characteristic characterization parameters of each energy storage unit group, and generate a wide-area distributed energy storage aggregation model; In the steps of constructing a distributed energy storage aggregation system energy function based on the scale parameters of each energy storage unit and the matching degree parameters of each pair of energy storage units, and grouping the energy storage units into several energy storage unit groups based on the distributed energy storage aggregation system energy function, The energy function of the distributed energy storage aggregation system is: s i —Scale parameters of energy storage unit i; s j —Scale parameters of energy storage unit j; p ij —The matching degree parameters of node i and node j constitute the energy storage unit pair; δ ij (X) — When energy storage located at nodes i and j is aggregated in the same cluster, δ ij (X) = 1, otherwise δ ij (X) = 0; During the process of grouping the energy storage units based on the energy function of the distributed energy storage aggregation system, the grouping result of the energy storage units is: the grouping result of the energy storage units determined by the minimum value of the energy function of the distributed energy storage aggregation system.

6. A dispatching device for wide-area distributed energy storage, characterized in that, The widely distributed energy storage aggregation model constructed by the method as described in any one of claims 1 to 3 includes: Scheduling module: Based on the distributed energy storage aggregation model, constraints of thermal power and energy storage systems, and distributed energy storage topology, it establishes an objective function for wind and solar curtailment rates and optimizes the charging and discharging power of energy storage units in the distributed energy storage aggregation model.

7. An electronic device, characterized in that, include: At least one processor; as well as At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can invoke the program instructions to perform the method as described in any one of claims 1 to 3 or claim 4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as claimed in any one of claims 1 to 3 or claim 4.

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