Method and device for constructing and scheduling wide-area distributed energy storage aggregation model

Abstract

The invention provides an energy storage aggregation model construction and scheduling method and device, equipment and a storage medium, and the method comprises the steps: constructing a distributed energy storage aggregation index three-layer model which comprises a target layer, an index layer and a scheme layer, determining a scale parameter of each energy storage unit and a matching degree parameter of each energy storage unit pair, and constructing an 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 energy storage unit pair, grouping the energy storage units based on the energy function of the distributed energy storage aggregation system to form a plurality of energy storage unit groups; and obtaining a test sequence, solving an external characteristic characterization parameter of each energy storage unit group by using an SOC balanced distribution strategy based on the test sequence, and generating the wide-area distributed energy storage aggregation model. According to the invention, cross-regional distributed energy storage can be subjected to model aggregation, and load and storage coordination of the distributed energy storage network is realized.

Description

[Technical field]

[0001] Embodiments of the present invention relate to the technical field of power systems, and in particular to a method and device for constructing and scheduling a wide-area distributed energy storage aggregation model. [Background technology]

[0002] In recent years, the investment, construction and operation of various types of energy storage in power systems have increased year by year, making great contributions to smoothing wind and solar fluctuations and regulating system resources.

[0003] However, in the current energy storage dispatching process, the distributed energy storage in various regions is mostly limited to coordinated dispatching in the region. With the continuous investment and construction of energy storage equipment in the future, the large-scale grid connection of energy storage will inevitably create a computational burden on dispatching. At the same time, due to the spatial distance of distributed energy storage, long-distance (especially cross-regional) power grids cannot fully utilize the dispatching resources of energy storage in various regions in the process of absorbing new energy, resulting in inevitable resource idleness.

[0004] Therefore, how to realize model aggregation of cross-regional distributed energy storage and determine the external characteristics of the energy storage aggregation model suitable for system scheduling to achieve coordination between distributed energy storage networks and loads has become a technical problem that needs to be urgently solved by technical personnel in this field. [Summary of the invention]

[0005] In view of this, the present application provides a method and device for constructing and scheduling a wide-area distributed energy storage aggregation model, which can aggregate the distributed energy storage across regions and determine the external characteristics of the energy storage aggregation model suitable for system scheduling, so as to achieve coordination and cooperation between distributed energy storage networks and loads.

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

[0007] The specific steps include:

[0008] Constructing a three-layer model of a distributed energy storage aggregation index, the three-layer model of a distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer, the indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit;

[0009] Determining a scale parameter of each of the energy storage units and a matching parameter of each energy storage unit pair, wherein the energy storage unit pair is two energy storage units in the energy storage units;

[0010] 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, and grouping the energy storage units based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups;

[0011] A test sequence is obtained, and based on the test sequence, an SOC balanced allocation strategy is used to obtain external characteristic characterization parameters of each energy storage unit group, so as to generate a wide-area distributed energy storage aggregation model.

[0012] Through this application, distributed energy storage across regions can be modeled and aggregated, and the external characteristics of the energy storage aggregation model suitable for system scheduling can be determined to achieve coordination between distributed energy storage networks and loads.

[0013] One 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 energy storage unit pair, the scale parameter of any energy storage unit is determined in the following way, and the any energy storage unit exists in the energy storage units:

[0014] Sorting the importance of each distributed energy storage operation indicator in the indicator layer to construct a judgment matrix;

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

[0016] Wherein, 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 by using the following formula:

[0017]

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

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

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

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

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

[0023] The electrical distance between node i and node j is used to represent the edge weight of the node, and the matching parameter of the energy storage unit pair formed by node i and node j is determined.

[0024] The step of obtaining the electrical distance between node i and node j includes:

[0025] Based on the sensitivity matrix of voltage to reactive power, the influence of reactive power of each node on the voltage of node i and node j is calculated.

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

[0027] One possible approach is that, in the step of 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, and grouping the energy storage units based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups,

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

[0029]

[0030] s i —Size parameter of energy storage unit i;

[0031] s j —Size parameter of energy storage unit j;

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

[0033] δ ij (X)—When the energy storage at nodes i and j is aggregated in the same clusterδ ij (X) = 1, otherwise δ ij (X)=0.

[0034] In 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.

[0035] One possible approach is that, in the step of obtaining a test sequence and obtaining external characteristic characterization parameters of each energy storage unit group based on the test sequence using an SOC balancing allocation strategy, the test sequence is determined based on a net load curve decomposition, and the net load curve is determined based on a daily output curve and a daily load curve of a wind power plant and a photovoltaic power station.

[0036] In a second aspect, the present application provides a scheduling method for wide-area distributed energy storage, including the wide-area distributed energy storage aggregation model as described in the first aspect, specifically including the following steps:

[0037] Based on the wide-area distributed energy storage aggregation model, based on the constraints of thermal power and energy storage systems and the distributed energy storage topology, an objective function for wind and solar power abandonment rate is established to optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0038] In a third aspect, the present application provides a device for constructing a wide-area distributed energy storage aggregation model, including:

[0039] Construction module: used to construct a three-layer model of distributed energy storage aggregation index, the three-layer model of distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer, the indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit;

[0040] A determination module: used to determine the scale parameter of each of the energy storage units and the matching parameter of each energy storage unit pair, wherein the energy storage unit pair is two energy storage units in the energy storage units;

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

[0042] Acquisition module: used to acquire a test sequence, and based on the test sequence, use a SOC balanced allocation strategy to obtain external characteristic characterization parameters of each energy storage unit group, and generate a wide-area distributed energy storage aggregation model.

[0043] In a fourth aspect, the present application provides a scheduling device for wide-area distributed energy storage, characterized in that it includes the wide-area distributed energy storage aggregation model as described in the first aspect, including:

[0044] Scheduling module: used to establish a wind and solar power abandonment rate objective function based on the distributed energy storage aggregation model, thermal power and energy storage system constraints, and distributed energy storage topology, and optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0045] In a fifth aspect, an embodiment of the present invention provides an electronic device, including:

[0046] at least one processor; and

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

[0048] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method provided by the first aspect or the second aspect.

[0049] In a sixth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute 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 invention are consistent with the technical solutions of the first aspect of the embodiments of the present invention, and the beneficial effects achieved by each aspect and the corresponding feasible implementation methods are similar and will not be described in detail.

Brief Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flow chart of a method for constructing a wide-area distributed energy storage aggregation model provided in this application;

[0053] Figure 2 A schematic diagram of a three-layer model of distributed energy storage aggregation index provided in this application;

[0054] Figure 3 A flow chart of a method for grouping energy storage units provided in this application;

[0055] Figure 4 A structural diagram of a distributed energy storage scheduling model provided for this application;

[0056] FIG5( a ) is a structural diagram of a construction device for a wide-area distributed energy storage aggregation model provided by the present application;

[0057] FIG5( b ) is a structural diagram of a dispatching device for wide-area distributed energy storage provided by the present application;

[0058] Figure 6 A structural diagram of an electronic device provided in an embodiment of the present application. [Specific implementation method]

[0059] In order to better understand the technical solution of the embodiment of the present invention, the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0060] It should be clear that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments in the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of the present invention.

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

[0062] In recent years, the investment, construction and operation of various types of energy storage in power systems have increased year by year, making great contributions to smoothing wind and solar fluctuations and regulating system resources.

[0063] However, in the current energy storage dispatching process, the distributed energy storage in various regions is mostly limited to coordinated dispatching in the region. With the continuous investment and construction of energy storage equipment in the future, the large-scale grid connection of energy storage will inevitably create a computational burden on dispatching. At the same time, due to the spatial distance of distributed energy storage, long-distance (especially cross-regional) power grids cannot fully utilize the dispatching resources of energy storage in various regions in the process of absorbing new energy, resulting in inevitable resource idleness.

[0064] Therefore, how to aggregate the distributed energy storage across regions into models and determine the external characteristics of the energy storage aggregation model suitable for system scheduling to achieve coordination between distributed energy storage networks and loads has become a technical problem that needs to be urgently solved by technical personnel in this field.

[0065] In order to solve the above problems, the present application provides a method, device, electronic device and storage medium for constructing and scheduling a wide-area distributed energy storage aggregation model, 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, so as to achieve coordination and cooperation between distributed energy storage networks and loads.

[0066] First of all, the terms involved in this application are explained:

[0067] 1. Hierarchy Analysis Method: Take a complex multi-objective decision-making problem as a system, decompose the system into multiple indicators at multiple levels, and calculate the ranking of each level through the fuzzy quantification method of qualitative indicators, so that the problem is reduced to solving the weight or order of merit of the lowest level relative to the highest level. It is suitable for decision-making problems with hierarchical evaluation indicators and indicator values ​​that are difficult to describe quantitatively.

[0068] The specific steps are:

[0069] 1) Establish a hierarchical structure of indicator evaluation, with the lowest level being the available options, the highest level being the overall goal, and the middle level being the indicator level, which can be flexibly selected according to different application scenarios;

[0070] 2) Compare the indicators in each level of the indicator layer with reference to the 1-9 scale method determined by Saaty, and use h i,j Indicates the importance of index i relative to index j, and constructs the judgment matrix H = (h i,j ) n×n ; Calculate the maximum eigenvalue and the corresponding eigenvector of the judgment matrix, and perform a consistency test on them. If the consistency test is passed, it can be considered that the error of the constructed matrix H is small. The above eigenvector is normalized to be the weight coefficient of all indicators in the indicator layer of this level. Otherwise, the judgment matrix needs to be modified until it passes the consistency test.

[0071] The following is an explanation of a method for constructing a wide-area distributed energy storage aggregation model provided by the present application, referring to Figure 1 , specifically including the following steps:

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

[0073] In combination with the foregoing, it can be seen that the three-layer model of the distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer. The indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit.

[0074] For example, refer to Figure 2 , target layer A is: distributed energy storage aggregation index, and indicator layer C includes: rated capacity, charging and discharging power, response time, cost per kilowatt-hour, and charging and discharging efficiency.

[0075] The solution layer p includes multiple energy storage units.

[0076] Assume that the total number of indicators is n and the kth indicator is c k The solution layer is all distributed energy storage to be aggregated, denoted as P layer. Assuming the total number of energy storage is m, the i-th energy storage unit is denoted as p i .

[0077] At this point, a three-layer model of distributed energy storage aggregation index is about to be constructed with reference to the aforementioned analytic hierarchy process.

[0078] S102: Determine the scale parameter of each energy storage unit and the matching parameter of each energy storage unit pair.

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

[0080] Based on S101, a possible design is that, in this step, the aforementioned analytic hierarchy process is first used to perform a single-layer sorting of the indicator layer, that is, the indicators in each level of the indicator layer are compared pairwise corresponding to the 1-9 scaling method determined by reference to Saaty. That is to say, in this step, first, the importance of each distributed energy storage operation indicator in the indicator layer is sorted, and a judgment matrix is ​​constructed based on the sorting results.

[0081] After the judgment matrix is ​​constructed, the scale parameter of any energy storage unit is obtained based on the judgment matrix.

[0082] Specifically, firstly, the eigenvector corresponding to the maximum eigenvalue of the judgment matrix is ​​obtained. Here, the eigenvector is expressed as: w0 = [w 01 ,w 02 ,...,w 0n ], and then use the eigenvector to obtain the scale parameters of the energy storage unit.

[0083] Specifically, the i-th energy storage unit p i The aggregation index is:

[0084]

[0085] s—scaling parameter of energy storage unit

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

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

[0088] Regarding the matching degree parameter, firstly, S102a is executed to obtain the electrical distance between the node i and the node j.

[0089] Specifically, in order to obtain the electrical distance between the node i and the node j, first, it is necessary to obtain the magnitude of the influence of each reactive power on the voltage of the node i and the node j based on the sensitivity matrix of voltage to reactive power.

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

[0091]

[0092] Among them, S VQ,kk —Ratio of the reactive power change rate at node k to the voltage change rate at node k;

[0093] s VQ,ik—The ratio of the reactive power change rate at node k to the voltage change rate at node i.

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

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

[0096]

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

[0098] d jk —Indicates the impact of the reactive power change of node j on the voltage of node k.

[0099] Here, the reason why the electrical distance is not determined directly using nodes i and j, but the electrical distance is obtained by using the magnitude of the influence of the reactive power of each node in the distributed energy storage topology on the voltage of nodes i and j is because the influence of the network topology and other nodes is taken into account. The result obtained by using this method to obtain the electrical distance is more accurate. Thus, the electrical distance between the two nodes is obtained.

[0100] Regarding the method of obtaining the electrical distance, the above scheme is only one feasible method for those skilled in the art. When those skilled in the art obtain the electrical distance, no limitation is made here.

[0101] Thus, the electrical distance between the two points is obtained, and then S102a is executed: the electrical distance between node i and node j is used to represent the edge weight of the node, and the matching degree parameter of the energy storage unit pair formed by node i and node j is determined.

[0102] Specifically, the concept of modularity in the community discovery method is used and mapped 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] Furthermore, the matching parameter of the energy storage unit pair formed by node i and node j can be expressed as:

[0104]

[0105] Among them, 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 represents the sum of the electrical distances of all nodes connected to node j;

[0108] When the energy storage located at node i and node j are aggregated in the same cluster, δ(i,j)=1, 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, and grouping the energy storage units based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups;

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

[0111]

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

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

[0114] p ij —The matching parameter of the energy storage unit pair formed by node i and node j;

[0115] δ ij (X)—When the energy storage at nodes i and j is aggregated in the same clusterδ ij (X) = 1, otherwise δ ij (X)=0.

[0116] Specifically, the following steps and methods can be used to determine the grouping results of energy storage units:

[0117] The original grouping information T and the number of clusters K of the energy storage unit are obtained, the energy of the distributed energy storage aggregation system is obtained, and the grouping information T' of the energy storage unit is regenerated by traversing all members and moving and changing groups, and the energy of the distributed energy storage aggregation system is obtained again.

[0118] It can be understood that the grouping information T' of the energy storage unit can determine multiple distributed energy storage aggregation system energy function values. For the convenience of description, the multiple distributed energy storage aggregation system energy function values ​​determined by the grouping information T' of the energy storage unit can be defined as set A, and the distributed energy storage aggregation system energy function value determined by the original grouping information T of the energy storage unit is defined as a.

[0119] Specifically, an iterative method may be used, for example, assuming that the grouping information T'={T1, T2...T N}, assuming that the distributed energy storage aggregation system energy corresponding to T1 is less than the distributed energy storage aggregation system energy corresponding to the grouping information T of the current energy storage unit, the original grouping information of the current energy storage unit is modified to T based on T1, that is, < the grouping information of the current energy storage unit is changed to T1. If it is assumed that the distributed energy storage aggregation system energy corresponding to T2 is less than the distributed energy storage aggregation system energy corresponding to the grouping information T1 of the current energy storage unit, then the grouping information of the current energy storage unit is changed to T2, and so on.

[0120] Min(A, a) is obtained as the original grouping information of the energy storage unit with the cluster number K+1, and the above operation is repeated until K=m-1.

[0121] Through the above method, the final grouping result of the energy storage units can be determined.

[0122] S104: Acquire a test sequence, and based on the test sequence, use a SOC balanced allocation strategy to obtain external characteristic characterization parameters 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 the net load curve decomposition, and the net load curve is determined based on the daily output curve and the daily load curve of the wind power plant and the photovoltaic power plant.

[0124] Specifically, we first obtain the daily output curves of wind power and photovoltaic power stations, and then use k-means clustering to cluster the wind power and photovoltaic output curves respectively, and get the cluster center as the typical scenario of wind and solar power output.

[0125] At the same time, since the daily fluctuation of load is relatively small, the average value of the daily load curve is directly calculated to obtain the typical load scenario. Therefore, the typical scenario of wind and solar output is subtracted from the typical scenario of load to obtain the net load curve of the typical scenario. The relatively stable base power in the net load can be considered to be met by conventional units, while the component with large fluctuations can be smoothed by the energy storage system. Therefore, the net load curve is decomposed, and the inverse number of the AC component obtained by the decomposition is used as the test sequence to characterize the overall power demand of the energy storage.

[0126] Since the large SOC difference between individuals will affect the overall operating state and stability of external characteristics after energy storage aggregation, it is necessary to consider maintaining the SOC balance between energy storage units and use the Sigmoid function to correct the charging and discharging power of energy storage, so that energy storage units with higher SOC participate more in the discharge process, and energy storage with lower SOC participate more in charging, and finally make the SOC values ​​of all energy storage in the cluster consistent. The energy storage aggregation scheduling model is tested using the test sequence to obtain stable external characteristic parameters. The power is distributed within the energy storage cluster according to the SOC balance strategy. Taking 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, with 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 charging Sigmoid function, with 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 Indicates the overall charging power of the energy storage cluster

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

[0135]

[0136] Where P C,i —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 energy storage i, η C,i is the charging efficiency of energy storage i.

[0138] Similarly, the expression of energy storage discharge power can be derived, power distribution within the cluster can be performed according to the SOC balancing strategy, the external characteristics after energy storage aggregation can be tested, and the upper and lower limit parameters when the power and available capacity are relatively stable can be obtained to obtain the energy storage aggregation scheduling model.

[0139] Specifically, the external characteristic 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 cost per kilowatt-hour.

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

[0141] 1) Construct an energy function to aggregate distributed energy storage models;

[0142] 2) The net load curve and the SOC balanced allocation strategy are used to determine the external characteristic characterization parameters of each energy storage unit, which is suitable for system scheduling and realizes the coordination between the distributed energy storage network and the storage.

[0143] Based on the above embodiments, the present application provides a scheduling method for wide-area distributed energy storage, which specifically includes the following steps:

[0144] Based on the wide-area distributed energy storage aggregation model, based on the constraints of thermal power and energy storage systems and the distributed energy storage topology, an objective function for wind and solar power abandonment rate is established to optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0145] Specifically, refer to Figure 4 In the distributed energy storage scheduling model provided in the present application, the upper model includes the aforementioned distributed energy storage aggregation model. The construction method of the distributed energy storage aggregation model will not be described here in order to avoid repetition.

[0146] Here, based on the constraints of thermal power and energy storage systems and the distributed energy storage topology, an objective function for wind and solar power abandonment rate is established, and the upper-level model can calculate the charging and discharging power of the aggregated model.

[0147] The economically optimal internal allocation of the energy storage cluster can be achieved by decomposing the upper model through the lower model.

[0148] 5(a), the present application provides a device for constructing a wide-area distributed energy storage aggregation model, including:

[0149] Construction module: used to construct a three-layer model of distributed energy storage aggregation index, the three-layer model of distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer, the indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit;

[0150] A determination module: used to determine the scale parameter of each of the energy storage units and the matching parameter of each energy storage unit pair, wherein the energy storage unit pair is two energy storage units in the energy storage units;

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

[0152] Acquisition module: used to acquire a test sequence, and based on the test sequence, use the SOC balanced 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.

[0153] The construction device for the wide-area distributed energy storage aggregation model provided in the embodiment shown in FIG5(a) can be used to implement the embodiment of the present invention. Figure 1 The technical solution of the method embodiment shown, its implementation principle and technical effects can be further referred to the relevant description in the method embodiment.

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

[0155] Scheduling module: used to establish a wind and solar power abandonment rate objective function based on the distributed energy storage aggregation model, based on the thermal power and energy storage system constraints and the distributed energy storage topology structure, and optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

[0156] Figure 6 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 6 As shown, the electronic device may include at least one processor; and at least one memory connected to the processor in communication, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the embodiments of the present invention. Figure 1 to Figure 4 The illustrated embodiment provides a method for constructing a wide-area distributed energy storage aggregation model.

[0157] Figure 6 A block diagram of an exemplary electronic device suitable for implementing an exemplary embodiment of the present invention is shown. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0158] like Figure 6As shown, the electronic device is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 410, memory 430, and a communication bus 440 connecting different system components (including memory 430 and 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 acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0160] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

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

[0162] A program / utility having a set (at least one) of program modules may be stored in memory 430, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described in the embodiments of the present invention.

[0163] The processor 410 executes various functional applications and data processing by running the programs stored in the memory 430, such as implementing the embodiments of the present invention. Figure 1 to Figure 4 The illustrated embodiment provides a method for constructing a wide-area distributed energy storage aggregation model.

[0164] An embodiment of the present invention provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute an embodiment of the present invention. Figure 1 to Figure 4 The illustrated embodiment provides a method for constructing a wide-area distributed energy storage aggregation model.

[0165] The above-mentioned 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 electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ReadOnly Memory; hereinafter referred to as: ROM), an erasable programmable read-only memory (Erasable Programmable ReadOnly Memory; hereinafter referred to as: EPROM) or flash memory, optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0166] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0167] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0168] The computer program code for performing the operation of the embodiments of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (Local Area Network; hereinafter referred to as: LAN) or a wide area network (Wide Area Network; hereinafter referred to as: WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0169] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0170] In the description of the embodiments of the present invention, 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 embodiments of the present invention. In the embodiments of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more 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 the embodiments of the present invention and the features of the different embodiments or examples, without contradiction.

[0171] 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 the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0172] 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 more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred implementation of the embodiments of the present invention 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 invention belong.

[0173] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0174] It should be noted that the terminals involved in the embodiments of the present invention may include but are not limited to personal computers (Personal Computer; hereinafter referred to as: PC), personal digital assistants (Personal Digital Assistant; hereinafter referred to as: PDA), wireless handheld devices, tablet computers (Tablet Computer), mobile phones, MP3 players, MP4 players, etc.

[0175] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0176] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0177] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk and other media that can store program codes.

[0178] The above description is only a preferred embodiment of the embodiment of the present invention and is not intended to limit the embodiment of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of the present invention should be included in the scope of protection of the embodiment of the present invention.

Claims

1. A method for constructing a wide-area distributed energy storage aggregation model, characterized in that: The specific steps include: Constructing a three-layer model of a distributed energy storage aggregation index, the three-layer model of a distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer, the indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit; Determining a scale parameter of each of the energy storage units and a matching parameter of each energy storage unit pair, wherein the energy storage unit pair is two energy storage units in the energy storage units; 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, and grouping the energy storage units based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups; A test sequence is obtained, and based on the test sequence, an SOC balanced allocation strategy is used to obtain external characteristic characterization parameters of each energy storage unit group, so as to generate a wide-area distributed energy storage aggregation model.

2. The method according to claim 1, characterized in that In the step of determining the scale parameter of each of the energy storage units and the matching degree parameter of each energy storage unit pair, the scale parameter of any energy storage unit present in the energy storage units is determined in the following manner: Sorting the importance of each distributed energy storage operation indicator in the indicator layer, and constructing a judgment matrix based on the sorting results; Obtaining a scale parameter of any energy storage unit based on the judgment matrix; Wherein, 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 by using the following formula: s—scale parameter of energy storage unit; c k —kth distributed energy storage operation index; w ok —The characteristic vector of the corresponding value of the kth distributed energy storage operation index determined based on the judgment matrix.

3. 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 parameter of each energy storage unit pair, the matching parameter of the energy storage unit pair formed by node i and node j is determined in the following manner: Find the electrical distance between node i and node j; The electrical distance between node i and node j is used to represent the edge weight of the node, and the matching degree parameter of the energy storage unit pair formed by node i and node j is determined; The step of obtaining the electrical distance between node i and node j includes: Based on the sensitivity matrix of voltage to reactive power, the influence of reactive power of each node on the voltage of node i and node j is calculated. The electrical distance between the node i and the node j is calculated based on the influence of the reactive power of each node on the voltage of the node i and the node j.

4. The method according to claim 1, characterized in that: In the step of 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, and grouping the energy storage units based on the distributed energy storage aggregation system energy function to form a plurality of energy storage unit groups, The energy function of the distributed energy storage aggregation system is: s i —Size parameter of energy storage unit i; s j —Size parameter of energy storage unit j; p ij —The matching parameter of the energy storage unit pair formed by node i and node j; δ ij (X)—When the energy storage at nodes i and j is aggregated in the same clusterδ ij (X) = 1, otherwise δ ij (X) = 0; In 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.

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

6. A dispatching method for wide-area distributed energy storage, characterized in that: A wide-area distributed energy storage aggregation model constructed by the method according to any one of claims 1 to 5 specifically comprises the following steps: Based on the wide-area distributed energy storage aggregation model, based on the constraints of thermal power and energy storage systems and the distributed energy storage topology, an objective function for wind and solar power abandonment rate is established to optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

7. A device for constructing a wide-area distributed energy storage aggregation model, characterized in that: include: Construction module: used to construct a three-layer model of distributed energy storage aggregation index, the three-layer model of distributed energy storage aggregation index includes: a target layer, an indicator layer and a solution layer, the indicator layer includes: at least one distributed energy storage operation indicator, and the solution layer includes at least one energy storage unit; A determination module: used to determine the scale parameter of each of the energy storage units and the matching parameter of each energy storage unit pair, wherein the energy storage unit pair is two energy storage units in the energy storage units; A grouping module: used to construct an energy function of a distributed energy storage aggregation system based on the scale parameter of each energy storage unit and the matching degree parameter of each energy storage unit pair, and to group the energy storage units based on the energy function of the distributed energy storage aggregation system to form a plurality of energy storage unit groups; Acquisition module: used to acquire a test sequence, and based on the test sequence, use a SOC balanced allocation strategy to obtain external characteristic characterization parameters of each energy storage unit group, and generate a wide-area distributed energy storage aggregation model.

8. A dispatching device for wide-area distributed energy storage, characterized in that: A wide-area distributed energy storage aggregation model constructed according to the method described in any one of claims 1 to 5, comprising: Scheduling module: used to establish a wind and solar power abandonment rate objective function based on the distributed energy storage aggregation model, thermal power and energy storage system constraints, and distributed energy storage topology, and optimize the charging and discharging power of the energy storage units in the distributed energy storage aggregation model.

9. An electronic device, characterized in that: include: at least one processor; as well as at least one memory in communication with the processor, wherein: The memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 5 or claim 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method according to any one of claims 1 to 5 or claim 6.

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