Distributed energy storage optimization regulation and control method considering multi-level power grid cooperation

Abstract

The invention discloses a distributed energy storage optimization regulation and control method considering multi-level power grid collaboration, and belongs to the technical field of power systems, and the method comprises the steps: building a centralized regulation and control model according to the operation data, power utilization cost and safe operation constraint conditions of a multi-level power grid, so as to carry out the regulation and control of the multi-level power grid; decomposing the centralized regulation and control model by using an HGD algorithm to obtain an upper-layer power grid regulation and control model and a lower-layer power grid regulation and control model; performing iterative solution on parameter variables in the upper-layer power grid regulation and control model and the lower-layer power grid regulation and control model, and performing filtering processing on iterative data; and when both the upper-layer power grid regulation and control model and the lower-layer power grid regulation and control model converge, parameter variables in the upper-layer power grid regulation and control model and the lower-layer power grid regulation and control model are obtained so as to regulate and control resources such as distributed energy storage of the multi-level power grid. According to the method, the performance processing requirement on the processor can be reduced, and the probability of leakage of the privacy information of the power grid can be greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed energy storage optimization control method taking into account multi-level power grid coordination. Background Art

[0002] A multi-level power grid refers to a power grid system with multiple hierarchical structures divided into power systems according to different voltage levels, management scopes and functions. This type of power grid system aims to achieve optimal allocation and efficient management of power resources.

[0003] In order to improve the economic benefits of multi-level power grids, the number of various distributed resources such as energy storage and distributed power generation connected to each level of the multi-level power grid is increasing, and the configuration capacity of the connected distributed resources at different voltage levels is extremely different. This leads to the collaborative control problem of the multi-level power grid being large in scale, high in variable dimension, and difficult to solve quickly and accurately.

[0004] In the prior art, when controlling a multi-level power grid, it mainly relies on a processor with powerful working performance to centrally control the multi-level power grid. However, this control method has extremely high requirements on the working performance of the processor. In addition, since the optimization goals, topological structures and internal configurations of different levels of the power grid in the multi-level power grid are different, when the processor controls the multi-level power grid, it is also necessary to transfer massive amounts of data between different levels of the power grid, which can easily lead to the leakage of privacy information of the power grid. At present, there is no more effective solution to this technical problem. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a distributed energy storage optimization control method taking into account the coordination of multi-level power grids, so as to solve the problem that the prior art needs to rely on a processor with powerful working performance to control the multi-level power grid, and the privacy information of the power grid is easily leaked when controlling the multi-level power grid. The specific scheme is as follows:

[0006] In order to solve the above technical problems, the present invention provides a distributed energy storage optimization control method taking into account multi-level power grid coordination, comprising:

[0007] A centralized control model is created according to the operation data, electricity cost and safe operation constraints of a multi-level power grid to control the multi-level power grid; the multi-level power grid includes an upper power grid and multiple lower power grids, the upper power grid is connected to multiple distributed energy storage elements with a rated voltage matching the upper power grid; the lower power grid is connected to multiple distributed energy storage elements with a rated voltage matching the lower power grid; the centralized control model is decomposed by using an HGD algorithm to obtain an upper power grid control model and a lower power grid control model;

[0008] Iteratively solving parameter variables in the upper-layer power grid control model and the lower-layer power grid control model, and filtering the iterative data;

[0009] When both the upper-layer power grid control model and the lower-layer power grid control model converge, parameter variables in the upper-layer power grid control model and the lower-layer power grid control model are obtained to control the multi-level power grid.

[0010] Preferably, the centralized control model is created according to the operation data, electricity cost and safe operation constraints of the multi-level power grid, including:

[0011] Taking the optimization of the comprehensive operation benefit of the multi-level power grid as the goal, and creating a function model according to the operation data of the multi-level power grid, a centralized objective function model is obtained;

[0012] According to the safe operation constraint conditions of the multi-level power grid and the operation data of the multi-level power grid, safe operation constraint models corresponding to the upper power grid, the boundary power grid between the upper power grid and the lower power grid, and the lower power grid are respectively created to obtain an upper power grid safety constraint model, an upper and lower power grid safety constraint model, and a lower power grid safety constraint model.

[0013] Preferably, the expression of the centralized objective function model is:

[0014] ;

[0015] In the formula, and is the node index of the power grid, Subscript for time, is a set of nodes of the upper power grid, is the root node set of the lower power grid, is a set of nodes in a certain lower-level power grid. The node subscripts of the power grid at the boundary corresponding to the root node of the lower-level power grid are , The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the , and are nodes in the upper power grid The cost coefficients of the quadratic, linear and constant terms of distributed generation, is a node in the upper power grid The penalty coefficient for the use of distributed energy storage components, is a node in the lower power grid The active power of the distributed energy storage element connected to the is a node in the lower power grid The penalty coefficient for the use of distributed energy storage components, is a node in the lower power grid The voltage amplitude at The lower grid node Penalty factor for voltage deviation.

[0016] Preferably, the expression of the upper-layer power grid security constraint model is:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] In the formula, is the node index of the power grid, and Respectively represent the nodes in the upper power grid and nodes exist The voltage amplitude at the moment, and Respectively represent the nodes in the upper power grid and nodes exist The voltage phase angle at time and Respectively represent the upper grid connection nodes and nodes The conductance and susceptance of the branch, Indicates branch The shunt susceptance, and Represent the boundary nodes exist The voltage amplitude and voltage phase angle at the moment, and Respectively represent the nodes in the upper power grid exist The active power and reactive power at the moment, Indicates branch The maximum power allowed, The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the and Respectively represent the nodes in the upper power grid exist The reactive power of distributed power sources and distributed energy storage components at all times, and Respectively represent the upper grid nodes exist The active power and reactive power of the load at each moment are given by load forecasting technology. Represents the nodes in the upper power grid exist The storage capacity of energy stored at all times, is the time interval, Represents the nodes in the upper power grid exist The storage capacity of energy stored at all times, and Represents the nodes in the upper power grid The upper and lower limits of energy storage capacity, and Respectively represent the nodes in the upper power grid The upper and lower limits of the active power of distributed energy storage elements, and Respectively represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed energy storage elements, and Respectively represent the nodes in the upper power grid The upper and lower limits of the active power of distributed generation, and Respectively represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed generation, and Respectively represent the nodes in the upper power grid The upper and lower limits of the voltage amplitude, and Respectively represent the nodes in the upper power grid The upper and lower limits of the voltage phase angle, represents the voltage phase angle of the upper power grid balancing node;

[0029] The expression of the upper and lower power grid security constraint model is:

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] In the formula, and Respectively represent the corresponding nodes at the boundary exist The voltage amplitude and voltage phase angle at the moment, and Represents the branch at the boundary The conductance and susceptance of and Represent the boundary nodes exist The active power and reactive power at the moment, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and Represent the boundary nodes Upper and lower limits for active power, and Represent the boundary nodes Upper and lower limits for reactive power, and Represent the boundary nodes The upper and lower limits of the voltage amplitude, and Represent the boundary nodes The upper and lower limits of the voltage phase angle;

[0035] The expression of the lower-layer power grid security constraint model is:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] In the formula, Node is Node The parent node of and Respectively represent the lower power grid branches exist The active power and reactive power flowing through at all times, and Respectively represent the lower power grid branches exist The active power and reactive power at the moment, and Respectively represent the branches of the lower power grid connecting node 0 and node 1 in The active power and reactive power flowing through at all times, and Respectively represent the lower grid nodes exist The active power and reactive power at the moment, and Respectively represent the leaf node set and node of the lower layer power grid The collection of child nodes of Representing a collection Remove the collection The nodes in and Respectively represent the lower grid nodes and nodes At the moment The voltage amplitude, is the voltage amplitude of the root node of the lower grid at time, and Respectively represent the lower power grid branches The resistance and reactance, and Respectively represent the lower grid nodes At the moment The active power and reactive power of the load, Indicates the lower grid node At the moment The reactive power of energy storage, and Respectively represent the lower grid nodes At the moment and time The storage capacity of energy storage, and Respectively represent the lower grid nodes The upper and lower limits of energy storage capacity, and Respectively represent the lower grid nodes The upper and lower limits of the energy storage active power, and Respectively represent the lower grid nodes The upper and lower limits of the energy storage reactive power.

[0045] Preferably, the centralized control model is decomposed by using the HGD algorithm to obtain an upper-layer power grid control model and a lower-layer power grid control model, including:

[0046] Decomposing the centralized objective function model by using the HGD algorithm to obtain an upper-layer control objective function model and a lower-layer control objective function model;

[0047] Decomposing the upper-layer power grid security constraint model, the upper-layer and lower-layer power grid security constraint model and the lower-layer power grid security constraint model by using the HGD algorithm, and determining the upper-layer power grid security constraint model and the upper-layer and lower-layer power grid security constraint model as the upper-layer regulation security constraint model, and determining the lower-layer power grid security constraint model as the lower-layer regulation security constraint model;

[0048] Among them, the expression of the upper-level control objective function model is:

[0049] ;

[0050] ;

[0051] In the formula, is the node index of the upper power grid, is the node index of the power grid at the boundary, Subscript for time, is a set of nodes of the upper power grid, is the root node set of the lower power grid, The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the , and are nodes in the upper power grid Cost coefficients of quadratic, linear and constant terms of distributed generation, is a node in the upper power grid The penalty coefficient for the use of distributed energy storage, is the intermediate variable, Represents the boundary nodes exist The voltage amplitude at the moment, is the Lagrange multiplier associated with the formula; is a node in the lower power grid In The voltage amplitude at the moment, The lower grid node Penalty coefficient for voltage deviation, lower grid node represents the root node of the lower power grid, Represents the node of the lower grid The collection of child nodes;

[0052] The expression of the lower-level control objective function model is:

[0053] ;

[0054] In the formula, is the node index of the lower power grid, Subscript for time, is the node set of the lower-layer power grid. The node subscripts of the boundary power grid corresponding to the root node of the lower-layer power grid are , is a node in the lower power grid The penalty coefficient for the use of distributed energy storage components, The lower grid node The active power of the distributed energy storage element connected to the is a node in the lower power grid Penalty factor for voltage deviation, is a node in the lower power grid The voltage amplitude, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and They represent the Lagrange multipliers associated with the formula respectively.

[0055] Preferably, the iteratively solving the parameter variables in the upper-layer power grid control model and the lower-layer power grid control model includes:

[0056] Initialize the lower-level control objective function model and calculate the parameters , and , and the parameters , and Sending to the upper power grid;

[0057] When the upper power grid receives the parameter , and , calculate the parameters in the upper-level control objective function model , and , and the parameters , and Send to each lower power grid;

[0058] When each lower-layer power grid receives the parameters , and , calculate the parameters in the lower-level control objective function model , and , and the parameters , and Sending to the upper power grid;

[0059] Repeat the above steps when the upper power grid receives the parameter , and , calculate the parameters in the upper-level control objective function model , and , and the parameters , and The steps of sending the parameters to each lower power grid, and when each lower power grid receives the parameters , and , calculate the parameters in the lower-level control objective function model , and , and the parameters , and The step of sending to the upper power grid is performed until the upper control objective function model and the lower control objective function model converge.

[0060] Preferably, the filtering process of the iterative data includes:

[0061] Using the first filtering model and the second filtering model to filter the iterative data in the upper power grid control model and the lower power grid control model;

[0062] The expression of the first filtering model is:

[0063] ;

[0064] In the formula, are the parameters passed to the upper power grid, is the number of iterations, is the filter coefficient, , is the value of the previous iteration, is calculated by the lower-level power grid control model ;

[0065] The expression of the second filtering model is:

[0066] ;

[0067] In the formula, are the parameters passed to the lower grid, is the number of iterations, is the filter coefficient, , is the value of the previous iteration, is calculated by the upper power grid control model .

[0068] Beneficial effect: It can be seen that in a distributed energy storage optimization and control method taking into account the coordination of multi-level power grids provided by the present invention, first, a centralized control model is created according to the operating data, electricity cost and safe operation constraints of the multi-level power grid, so as to use the centralized control model to control the multi-level power grid; wherein the multi-level power grid includes an upper power grid and multiple lower power grids, and the upper power grid is connected to multiple distributed energy storage elements with a rated voltage matching the upper power grid; the lower power grid is connected to multiple distributed energy storage elements with a rated voltage matching the lower power grid; then, the HGD algorithm is used to decompose the centralized control model to obtain an upper power grid control model and a lower power grid control model, and the parameter variables in the upper power grid control model and the lower power grid control model are iteratively solved, and the iterative data are filtered; finally, when the upper power grid control model and the lower power grid control model converge, the parameter variables in the upper power grid control model and the lower power grid control model are obtained to control the multi-level power grid.

[0069] Compared with the prior art, since this method uses the HGD algorithm to decompose the centralized control model, and decomposes the centralized control model into an upper-layer power grid control model and a lower-layer power grid control model, when the processor controls the multi-level power grid, it only needs to coordinate the upper-layer power grid control model and the lower-layer power grid control model, without the need to centrally control the operating data of each level of the multi-level power grid, thereby reducing the performance processing requirements for the processor and significantly reducing the processor's computing burden. In addition, when the processor controls the multi-level power grid, it only needs to exchange data between the upper-layer power grid control model and the lower-layer power grid control model, without the need to exchange data between different levels of the power grid, thereby greatly reducing the probability of leakage of power grid privacy information. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0071] Figure 1 A flow chart of a distributed energy storage optimization and control method taking into account multi-level power grid coordination provided by an embodiment of the present invention;

[0072] Figure 2 A schematic diagram of the connection of various nodes in a multi-level power grid provided by an embodiment of the present invention;

[0073] Figure 3Schematic diagram of iterative calculation of the upper-level control target model and the lower-level control target function model. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1 , Figure 1 A flowchart of a distributed energy storage optimization control method taking into account multi-level power grid coordination provided by an embodiment of the present invention, the method comprising:

[0076] Step S11: creating a centralized control model according to the operation data, electricity cost and safe operation constraints of the multi-level power grid to control the multi-level power grid; the multi-level power grid includes an upper power grid and multiple lower power grids, the upper power grid is connected to multiple distributed energy storage elements with a rated voltage matching the upper power grid; the lower power grid is connected to multiple distributed energy storage elements with a rated voltage matching the lower power grid;

[0077] Step S12: Decomposing the centralized control model by using the HGD algorithm to obtain an upper-layer power grid control model and a lower-layer power grid control model;

[0078] Step S13: iteratively solving parameter variables in the upper-layer power grid control model and the lower-layer power grid control model, and filtering the iterative data;

[0079] Step S14: when both the upper-layer power grid control model and the lower-layer power grid control model converge, parameter variables in the upper-layer power grid control model and the lower-layer power grid control model are obtained to control the multi-level power grid.

[0080] In this embodiment, a distributed energy storage optimization and control method taking into account the coordination of multi-level power grids is provided. Using this method to control the multi-level power grid can not only reduce the requirements for processor working performance, but also reduce the risk of leakage of power grid privacy information when controlling the multi-level power grid.

[0081] Among them, the multi-level power grid includes an upper power grid and multiple lower power grids. The upper power grid is connected to multiple distributed energy storage elements with rated voltages matching the upper power grid, and the lower power grid is connected to multiple distributed energy storage elements with rated voltages matching the lower power grid. If the node set of the upper power grid is denoted as M, the root nodes of all lower power grids constitute the boundary nodes of the upper power grid and the lower power grid, and this set is denoted as B. For a lower power grid, if its internal root node is connected to a node of the upper power grid, it is denoted as b∈B, and the set of all nodes of the lower power grid is denoted as L b .

[0082] In other words, M is the set of nodes in the upper power grid; B is the root node set of the lower power grid, that is, the set of boundary nodes between the upper power grid and the lower power grid; for a lower power grid, the node number of its root node connected to the upper power grid corresponding to the upper power grid is b∈B, then the set of all nodes of the lower power grid is recorded as L b .

[0083] See also Figure 2 , Figure 2 A schematic diagram of the connection of various nodes in a multi-level power grid provided by an embodiment of the present invention. Figure 2 In the figure, the nodes in the blue wireframe constitute the upper power grid node set M. The nodes in the yellow wireframe constitute the boundary node set B. The nodes with different numbers in the yellow wireframe all belong to set B. The nodes in the yellow wireframe are also the root nodes of each lower power grid. The nodes in the green wireframe constitute the lower power grid node set L. b , where the boundary node corresponding to the root node of the lower grid is numbered b. In addition, the nodes are renumbered in each lower grid, and the root node is numbered 0∈L b , the node connected to the root node is numbered 1∈L b .

[0084] In this method, firstly, a centralized control model is created based on the operation data, electricity cost and safe operation constraints of the multi-level power grid, and the centralized control model is used to control the multi-level power grid. In practical applications, the comprehensive operation benefit of the multi-level power grid can be optimized as the goal, and a centralized control model can be created based on the operation data and safe operation constraints of the multi-level power grid. The safe operation constraints of the multi-level power grid refer to the constraints that can ensure the safe and stable operation of the multi-level power grid, such as the constraints of the operation of its internal equipment and the voltage and current constraints of each branch in the power grid architecture, etc., in order to ensure the safe and stable operation of the multi-level power grid.

[0085] After that, the centralized control model is decomposed using the HGD (Heterogeneous Decomposition) algorithm to obtain the upper-layer power grid control model and the lower-layer power grid control model. After the centralized control model is decomposed into the upper-layer power grid control model and the lower-layer power grid control model using the HGD algorithm, it is equivalent to decomposing a complex and difficult-to-solve large system into multiple subsystems to reduce the complexity of solving the problem.

[0086] After the centralized control model is decomposed into the upper power grid control model and the lower power grid control model using the HGD algorithm, the parameter variables in the upper power grid control model and the lower power grid control model are iteratively solved, and the iterative data are filtered until both the upper power grid control model and the lower power grid control model converge. The purpose of this step is, on the one hand, to solve the variable parameters in the upper power grid control model and the lower power grid control model, and on the other hand, filtering the iterative data is to ensure the accuracy and reliability of the iterative data solution results.

[0087] When both the upper-level power grid control model and the lower-level power grid control model converge, the parameter variables in the upper-level power grid control model and the lower-level power grid control model are obtained to obtain the target control quantity; finally, the target control quantity is used to control the multi-level power grid.

[0088] Compared with the prior art, since this method uses the HGD algorithm to decompose the centralized control model, and decomposes the centralized control model into an upper-layer power grid control model and a lower-layer power grid control model, when the processor controls the multi-level power grid, it only needs to coordinate the upper-layer power grid control model and the lower-layer power grid control model, without the need to centrally control the operating data of each level of the multi-level power grid, thereby reducing the performance processing requirements for the processor and significantly reducing the processor's computing burden. In addition, when the processor controls the multi-level power grid, it only needs to exchange data between the upper-layer power grid control model and the lower-layer power grid control model, without the need to exchange data between different levels of the power grid, thereby greatly reducing the probability of leakage of power grid privacy information.

[0089] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. As a preferred implementation method, the above steps: creating a centralized control model according to the operation data, electricity cost and safe operation constraints of the multi-level power grid include:

[0090] Taking the optimization of the comprehensive operation benefit of the multi-level power grid as the goal, a function model is created according to the operation data of the multi-level power grid to obtain a centralized objective function model;

[0091] According to the safe operation constraint conditions of the multi-level power grid and the operation data of the multi-level power grid, safe operation constraint models corresponding to the upper power grid, the boundary power grid between the upper and lower power grids, and the lower power grid are respectively created to obtain the upper power grid safety constraint model, the upper and lower power grid safety constraint model, and the lower power grid safety constraint model.

[0092] When creating a centralized control model, you actually need to create two function models. The first function model has the best overall benefit, including reducing the power generation cost of distributed power sources in the upper power grid, reducing the use of distributed energy storage in the upper and lower power grids, and reducing the voltage deviation of the lower power grid. The second function model needs to ensure the safe and stable operation of multi-level power grids.

[0093] Among them, when creating the first function model, it is necessary to take the comprehensive operation benefit of the multi-level power grid as the optimal goal, and create the function model according to the operation data of the multi-level power grid to obtain a centralized objective function model. When creating the second function model, it is necessary to create it according to the safe operation constraint conditions of the multi-level power grid and the operation data of the multi-level power grid. Considering that there are an upper power grid and multiple lower power grids in the multi-level power grid, therefore, when creating the second function model, it is necessary to create a safe operation constraint model corresponding to the upper power grid, a safe operation constraint model corresponding to the boundary power grid between the upper power grid and the lower power grid, and a safe operation constraint model corresponding to the lower power grid according to the safe operation constraint conditions of the multi-level power grid and the operation data of the multi-level power grid, and thereby obtain the upper power grid safety constraint model, the upper and lower power grid safety constraint model, and the lower power grid safety constraint model.

[0094] Obviously, through the technical solution provided in this embodiment, a centralized control model for centralized and unified control of multi-level power grids can be created.

[0095] As a preferred implementation, the expression of the above centralized objective function model is:

[0096] ;

[0097] In the formula, and is the node index of the power grid, Subscript for time, is a collection of nodes of the upper power grid. is the root node set of the lower power grid, is a set of nodes in a certain lower-level power grid. The node subscripts of the power grid at the boundary corresponding to the root node of the lower-level power grid are , For the upper grid node The active power of the distributed power source connected to For the upper grid node The active power of the distributed energy storage element connected to the , and They are nodes in the upper power grid. The cost coefficients of the quadratic, linear and constant terms of distributed generation, A node in the upper power grid The penalty coefficient for the use of distributed energy storage components, A node in the lower grid The active power of the distributed energy storage element connected to the A node in the lower grid The penalty coefficient for the use of distributed energy storage components, A node in the lower grid The voltage amplitude at The lower grid node Penalty factor for voltage deviation.

[0098] In this embodiment, the centralized objective function is specifically described. The centralized objective function includes the upper grid objective function and the lower grid objective function. The upper grid objective function is to minimize the generation cost of distributed power sources in the upper grid and the penalty for energy storage use, that is:

[0099] Formula 1: ;

[0100] In the formula, is the node index of the upper power grid, Subscript for time, For the upper grid node The active power of the distributed power source connected to For the upper grid node The active power of the distributed energy storage element connected to the , and They are nodes in the upper power grid. The cost coefficients of the quadratic, linear and constant terms of distributed generation, A node in the upper power grid The penalty coefficient for the use of distributed energy storage components.

[0101] The objective function of the lower grid connected to the upper grid node b is to minimize the energy storage usage penalty and voltage deviation penalty, that is:

[0102] Formula 2: ;

[0103] In the formula, A node in the lower grid The active power of the distributed energy storage element connected to the A node in the lower grid The penalty coefficient for the use of distributed energy storage components, A node in the lower grid The voltage amplitude at The lower grid node Penalty factor for voltage deviation.

[0104] Finally, we get the centralized objective function model, that is, Formula 3:

[0105] .

[0106] As a preferred implementation, the expression of the upper-layer power grid security constraint model is:

[0107] Formula 4: ;

[0108] Formula 5: ;

[0109] Formula 6:

[0110] ;

[0111] Formula 7: ;

[0112] Formula 8: ;

[0113] Formula 9: ;

[0114] Formula 10: ;

[0115] Formula 11: ;

[0116] Formula 12: ;

[0117] Formula 13: ;

[0118] Formula 14: ;

[0119] In the formula, is the node index of the power grid, and Represent the nodes in the upper power grid and nodes exist The voltage amplitude at the moment, and Represent the nodes in the upper power grid and nodes exist The voltage phase angle at time and Respectively represent the upper grid connection nodes and nodes The conductance and susceptance of the branch, Indicates branch The shunt susceptance, and Represent the boundary nodes exist The voltage amplitude and voltage phase angle at the moment, and Represent the nodes in the upper power grid exist The active power and reactive power at the moment, Indicates branch The maximum power allowed, For the upper grid node The active power of the distributed power source connected to For the upper grid node The active power of the distributed energy storage element connected to the and Represent the nodes in the upper power grid and nodes exist The reactive power of distributed power sources and distributed energy storage components at all times, and Respectively represent the upper grid nodes exist The active power and reactive power of the load at each moment are given by load forecasting technology. Represents a node in the upper power grid exist The storage capacity of energy stored at all times, is the time interval, Represents a node in the upper power grid exist The storage capacity of energy stored at all times, and Represents a node in the upper power grid The upper and lower limits of energy storage capacity, and Represent the nodes in the upper power grid The upper and lower limits of the active power of distributed energy storage elements, and Represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed energy storage elements, and Represent the nodes in the upper power grid The upper and lower limits of the active power of distributed generation, and Represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed generation, and Represent the nodes in the upper power grid The upper and lower limits of the voltage amplitude, and Represent the nodes in the upper power grid The upper and lower limits of the voltage phase angle, Indicates the voltage phase angle of the upper grid balancing node;

[0120] The expressions of the upper and lower power grid security constraint models are as follows:

[0121] Formula 15: ;

[0122] Formula 16: ;

[0123] Formula 17: ;

[0124] Formula 18: ;

[0125] In the formula, and Respectively represent the corresponding nodes at the boundary exist The voltage amplitude and voltage phase angle at the moment, and Represents the branch at the boundary The conductance and susceptance of and Represent the boundary nodes exist The active power and reactive power at the moment, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and Represent the boundary nodes Upper and lower limits for active power, and Represent the boundary nodes Upper and lower limits for reactive power, and Represent the boundary nodes The upper and lower limits of the voltage amplitude, and Represent the boundary nodes The upper and lower limits of the voltage phase angle;

[0126] The expression of the lower-level power grid security constraint model is:

[0127] Formula 19: ;

[0128] Formula 20: ;

[0129] Formula 21: ;

[0130] Formula 22: ;

[0131] Formula 23: ;

[0132] Formula 24: ;

[0133] Formula 25:

[0134] Formula 26:

[0135] ;

[0136] In the formula, Node is Node The parent node of and Respectively represent the lower power grid branches exist The active power and reactive power flowing through at all times, and Respectively represent the lower power grid branches exist The active power and reactive power at the moment, and Respectively represent the branches of the lower power grid connecting node 0 and node 1. The active power and reactive power flowing through at all times, and Represent the lower grid nodes respectively exist The active power and reactive power at the moment, and Represents the leaf node set and node of the lower power grid respectively The collection of child nodes of Representing a collection Remove the collection The nodes in and Represent the lower grid nodes respectively and nodes At the moment The voltage amplitude, is the voltage amplitude of the root node of the lower grid at time, and Respectively represent the lower power grid branches The resistance and reactance, and Represent the lower grid nodes respectively The active power and reactive power of the load at the moment, Represents the lower grid node The reactive power stored at the moment, and Represent the lower grid nodes respectively At the moment and time The storage capacity of energy storage, and Represent the lower grid nodes respectively The upper and lower limits of energy storage capacity, and Represent the lower grid nodes respectively The upper and lower limits of the energy storage active power, and Represent the lower grid nodes respectively The upper and lower limits of the energy storage reactive power.

[0137] In this embodiment, the upper power grid security constraint model, the upper and lower power grid security constraint model and the lower power grid security constraint model are specifically described. Among them, formula 4, formula 5, formula 6, formula 7, formula 8, formula 12, formula 13 and formula 14 are the flow constraint conditions for the safe operation of the upper power grid, and formula 9, formula 10 and formula 11 are the equipment constraint conditions for the safe operation of the upper power grid. Formula 15, formula 16, formula 17 and formula 18 are all the flow constraint conditions for the safe operation of the upper and lower power grids. Formula 19, formula 20, formula 21, formula 22, formula 23, formula 24 and formula 25 are the flow constraint conditions for the safe operation of the lower power grid, and formula 26 is the equipment constraint condition for the safe operation of the lower power grid.

[0138] It should be noted that, in this embodiment, node 0 is the root node of the lower power grid, and node 1 is the node connected to the root node of the lower power grid. Considering that the lower power grid has a simple structure and is radial, in this embodiment, when creating the lower power grid security constraint model, the power flow constraint conditions of the lower power grid are simplified, and the phase angle and the loss of the distribution network are ignored.

[0139] Obviously, through the technical solution provided in this embodiment, the upper-layer power grid security constraint model, the upper and lower-layer power grid security constraint model and the lower-layer power grid security constraint model can be accurately created.

[0140] As a preferred implementation, the above step of: using the HGD algorithm to decompose the centralized control model to obtain the upper power grid control model and the lower power grid control model includes:

[0141] The centralized objective function model is decomposed by using the HGD algorithm to obtain the upper-level control objective function model and the lower-level control objective function model;

[0142] The upper grid security constraint model, the upper and lower grid security constraint model and the lower grid security constraint model are decomposed by using the HGD algorithm, and the upper grid security constraint model and the upper and lower grid security constraint model are determined as the upper-level regulation security constraint model, and the lower-level grid security constraint model is determined as the lower-level regulation security constraint model.

[0143] Among them, the expression of the upper-level control objective function model is:

[0144] Formula 27: ;

[0145] ;

[0146] In the formula, is the node index of the upper power grid, is the node index of the power grid at the boundary, Subscript for time, is a collection of nodes of the upper power grid. is the root node set of the lower power grid, is the active power of the distributed power source connected to the upper grid node, For the upper grid node The active power of the distributed energy storage element connected to the , and They are nodes in the upper power grid. Cost coefficients of quadratic, linear and constant terms of distributed generation, A node in the upper power grid The penalty coefficient for the use of distributed energy storage, is the intermediate variable, Represents the boundary nodes exist The voltage amplitude at the moment, is the Lagrange multiplier associated with the formula; A node in the lower grid In The voltage amplitude at the moment, The lower grid node Penalty coefficient for voltage deviation, lower grid node represents the root node of the lower power grid, Represents the node of the lower grid The collection of child nodes;

[0147] The expression of the lower-level regulation objective function model is:

[0148] Formula 28: ;

[0149] In the formula, is the node index of the lower power grid, Subscript for time, is the node set of the lower-layer power grid. The node subscripts of the boundary power grid corresponding to the root node of the lower-layer power grid are , A node in the lower grid The penalty coefficient for the use of distributed energy storage components, The lower grid node The active power of the distributed energy storage element connected to the A node in the lower grid Penalty factor for voltage deviation, A node in the lower grid The voltage amplitude, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and They represent the Lagrange multipliers associated with the formula respectively.

[0150] In this embodiment, after the centralized objective function model is decomposed by the HGD algorithm, an upper-layer control objective function model and a lower-layer control objective function model are obtained. The upper-layer control objective function model (Formula 27) is the objective function corresponding to the control of the upper-layer power grid, and the lower-layer control objective function model (Formula 28) is the objective function corresponding to the control of the lower-layer power grid.

[0151] At the same time, after using the HGD algorithm to decompose the upper power grid security constraint model, the upper and lower power grid security constraint model, and the lower power grid security constraint model, the corresponding constraints that can ensure the safe and stable operation of the upper and lower power grids will be obtained. Among them, formulas 4 to 18 are the corresponding constraints that can ensure the safe and stable operation of the upper power grid, and formulas 19 to 25 are the corresponding constraints that can ensure the safe and stable operation of the lower power grid. In other words, formulas 4 to 18 are the upper-level regulation security constraint model, and formulas 19 to 25 are the lower-level regulation security constraint model.

[0152] In practical applications, by giving parameters , , , the upper-level control objective function model (Formula 27) can be solved to obtain the control quantity , , , And the power grid status and , and thus obtain the new boundary node voltage , and the Lagrange multipliers corresponding to Formula 15 and Formula 16 can also be obtained and . That is, and They represent the Lagrange multipliers corresponding to Formula 15 and Formula 16 respectively.

[0153] By giving parameters , , , the lower-level control objective function model (Formula 28) can be solved to obtain the control quantity , And the power grid status , and thus obtain the active power of the boundary node and reactive power , and we can also get The corresponding Lagrange multiplier is , and finally calculated . That is, In formula 23 The Lagrange multiplier corresponding to .

[0154] As a preferred implementation, the above step of iteratively solving the parameter variables in the upper-layer power grid control model and the lower-layer power grid control model includes:

[0155] Step 1: Initialize the lower-level control objective function model and calculate the parameters , and , and set the parameter , and Send to upper power grid;

[0156] Step 2: When the upper grid receives the parameters , and When , the parameters in the upper-level control objective function model are calculated , and , and set the parameter , and Send to each lower power grid;

[0157] Step 3: When each lower-level power grid receives the parameters , and , calculate the parameters in the lower-level control objective function model , and , and set the parameter , and Send to upper power grid;

[0158] Step 4: Repeat steps 2 and 3 until the upper-level control objective function model and the lower-level control objective function model converge.

[0159] In this embodiment, the iterative solution process of the parameter variables in the upper grid control model and the lower grid control model is specifically described. In step 1, the parameters are first initialized in each lower grid. , , , calculate the lower-level control objective function model and obtain the parameters , and , and set the parameter , and Send to the upper power grid; Step 2: When the upper power grid receives the information transmitted by the lower power grid, it can , and Under the premise of , and ( ); When the parameters in the upper-level control objective function model are calculated , and When the upper power grid will set the parameter , and Step 3: After receiving the information from the upper power grid, each lower power grid can , and Under the premise of , and Then pass it to the upper power grid; Step 4: Repeat steps 2 and 3 until the upper-level control objective function model (Formula 27) and the lower-level control objective function model (Formula 28) converge.

[0160] See also Figure 3 , Figure 3 This is a schematic diagram of the iterative calculation of the upper-level control target model and the lower-level control target function model. Figure 3 The multi-level power grid shown includes an upper power grid and multiple lower power grids, wherein the upper power grid is connected to multiple distributed energy storage elements whose rated voltage matches the upper power grid; and the lower power grid is connected to multiple distributed energy storage elements whose rated voltage matches the lower power grid.

[0161] As a preferred implementation, the above step of filtering the iterative data includes:

[0162] Filtering the iterative data in the upper power grid control model and the lower power grid control model using the first filter model and the second filter model;

[0163] Among them, the expression of the first filtering model is:

[0164] ;

[0165] In the formula, are the parameters passed to the upper power grid. is the number of iterations, is the filter coefficient, , is the value of the previous iteration, Calculated by the lower-level power grid control model ;

[0166] The expression of the second filtering model is:

[0167] ;

[0168] In the formula, are the parameters passed to the lower power grid. is the number of iterations, is the filter coefficient, , is the value of the previous iteration, Calculated by the upper power grid control model .

[0169] In this embodiment, the process of filtering the iterative data in the upper grid control model and the lower grid control model is specifically described. is the subscript for the number of iterations, then in the In the first iteration, the obtained lower-level power grid control model can be Recorded as , then the lower power grid transmits to the upper power grid The following relationship needs to be satisfied:

[0170] Formula 29: ;

[0171] In the In the first iteration, the upper grid control model can be calculated to obtain Recorded as , then the upper power grid transmits to the lower power grid The following relationship needs to be satisfied:

[0172] Formula 30: ;

[0173] When both the upper-level control objective function model and the lower-level control objective function model converge, the parameter variables are calculated by iterative convergence. , , , , , To control the multi-level power grid. For the upper grid node The active power of the distributed power source connected to For the upper grid node The active power of the distributed energy storage element connected to the and Represent the nodes in the upper power grid exist The reactive power of distributed power sources and distributed energy storage components at all times, The lower grid node The active power of the distributed energy storage element connected to the The lower grid node The reactive power of distributed energy storage elements connected at

[0174] In addition, in practical applications, it is sometimes possible to use a hierarchical collaborative method to control multi-level power grids. For example, the control model of the multi-level power grid is decomposed by the alternating direction multiplier method to form a one-master-multiple-slave optimization problem, and the model is iteratively solved. However, this type of method requires the introduction of multipliers and penalty factors in the model, and also requires the construction of augmented Lagrangian functions to relax the coupled operating constraints at the boundaries of different levels of power grids to decouple the model. This will result in the hard operating constraints at the boundaries of the power grid not being met, resulting in the final solution being infeasible. At the same time, due to the obvious differences between different levels of the power grid in the multi-level power grid, the constructed network and equipment physical models show heterogeneous characteristics. Therefore, the hierarchical collaborative control method of the traditional multi-level power grid will also have problems such as poor convergence and difficulty in meeting optimality.

[0175] In the multi-level power grid control method provided in the present application, when establishing the safety operation constraints corresponding to the upper power grid, the boundary power grid between the upper power grid and the lower power grid, and the lower power grid, since the safety operation constraints are not relaxed, the hard constraints at the boundary of the upper power grid and the lower power grid can be satisfied, thereby obtaining a feasible solution result. In addition, in the present application, since the multi-level power grid is divided into the upper power grid and the lower power grid, and the power grid modules in the upper power grid and the lower power grid are divided into different levels according to the voltage level of the distributed energy storage element, the heterogeneous characteristics between different power grid levels are fully considered, so that the control method of the multi-level power grid provided in the present application has better convergence.

[0176] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0177] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0178] The above is a detailed introduction to a distributed energy storage optimization and control method taking into account the coordination of multi-level power grids provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A distributed energy storage optimization control method taking into account multi-level power grid coordination, characterized in that: include: A centralized control model is created according to the operation data, electricity cost and safe operation constraints of a multi-level power grid to control the multi-level power grid; the multi-level power grid includes an upper power grid and multiple lower power grids, and the upper power grid is connected to multiple distributed energy storage elements with rated voltages matching the upper power grid; A plurality of distributed energy storage elements having a rated voltage matching that of the lower power grid are connected to the lower power grid; Decomposing the centralized control model by using the HGD algorithm to obtain an upper-layer power grid control model and a lower-layer power grid control model; Iteratively solving parameter variables in the upper-layer power grid control model and the lower-layer power grid control model, and filtering the iterative data; When both the upper-layer power grid control model and the lower-layer power grid control model converge, parameter variables in the upper-layer power grid control model and the lower-layer power grid control model are obtained to control the multi-level power grid.

2. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 1, characterized in that: The centralized control model is created based on the operation data, electricity cost and safe operation constraints of the multi-level power grid, including: Taking the optimization of the comprehensive operation benefit of the multi-level power grid as the goal, and creating a function model according to the operation data of the multi-level power grid, a centralized objective function model is obtained; According to the safe operation constraint conditions of the multi-level power grid and the operation data of the multi-level power grid, safe operation constraint models corresponding to the upper power grid, the boundary power grid between the upper power grid and the lower power grid, and the lower power grid are respectively created to obtain an upper power grid safety constraint model, an upper and lower power grid safety constraint model, and a lower power grid safety constraint model.

3. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 2, characterized in that: The expression of the centralized objective function model is: ； In the formula, and is the node index of the power grid, Subscript for time, is a set of nodes of the upper power grid, is the root node set of the lower power grid, is a set of nodes in a certain lower-level power grid. The node subscripts of the power grid at the boundary corresponding to the root node of the lower-level power grid are , The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the , and are nodes in the upper power grid The cost coefficients of the quadratic, linear and constant terms of distributed generation, is a node in the upper power grid The penalty coefficient for the use of distributed energy storage components, is a node in the lower power grid The active power of the distributed energy storage element connected to the is a node in the lower power grid The penalty coefficient for the use of distributed energy storage components, is a node in the lower power grid The voltage amplitude at The lower grid node Penalty factor for voltage deviation.

4. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 3, characterized in that: The expression of the upper-layer power grid security constraint model is: ； ； ； ； ； ； ； ； ； ； ； In the formula, is the node index of the power grid, and Respectively represent the nodes in the upper power grid and nodes exist The voltage amplitude at the moment, and Respectively represent the nodes in the upper power grid and nodes exist The voltage phase angle at time and Respectively represent the upper grid connection nodes and nodes The conductance and susceptance of the branch, Indicates branch The shunt susceptance, and Represent the boundary nodes exist The voltage amplitude and voltage phase angle at the moment, and Respectively represent the nodes in the upper power grid exist The active power and reactive power at the moment, Indicates branch The maximum power allowed, The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the and Respectively represent the nodes in the upper power grid exist The reactive power of distributed power sources and distributed energy storage components at all times, and Respectively represent the upper grid nodes exist The active power and reactive power of the load at each moment are given by load forecasting technology. Represents the nodes in the upper power grid exist The storage capacity of energy storage at all times, is the time interval, Represents the nodes in the upper power grid exist The storage capacity of energy storage at all times, and Represents the nodes in the upper power grid The upper and lower limits of energy storage capacity, and Respectively represent the nodes in the upper power grid The upper and lower limits of the active power of distributed energy storage elements, and Respectively represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed energy storage elements, and Respectively represent the nodes in the upper power grid The upper and lower limits of the active power of distributed generation, and Respectively represent the nodes in the upper power grid The upper and lower limits of the reactive power of distributed generation, and Respectively represent the nodes in the upper power grid The upper and lower limits of the voltage amplitude, and Respectively represent the nodes in the upper power grid The upper and lower limits of the voltage phase angle, represents the voltage phase angle of the upper power grid balancing node; The expression of the upper and lower power grid security constraint model is: ； ； ； ； In the formula, and Represent the corresponding nodes at the boundary exist The voltage amplitude and voltage phase angle at the moment, and Represents the branch at the boundary The conductance and susceptance of and Represent the boundary nodes exist The active power and reactive power at the moment, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and Represent the boundary nodes The upper and lower limits of active power, and Represent the boundary nodes Upper and lower limits for reactive power, and Represent the boundary nodes The upper and lower limits of the voltage amplitude, and Represent the boundary nodes The upper and lower limits of the voltage phase angle; The expression of the lower-layer power grid security constraint model is: ； ； ； ； ； ； ； ； In the formula, Node is Node The parent node of and Respectively represent the lower power grid branches exist The active power and reactive power flowing through at all times, and Respectively represent the lower power grid branches exist The active power and reactive power at the moment, and Respectively represent the branches of the lower power grid connecting node 0 and node 1 in The active power and reactive power flowing through at all times, and Respectively represent the lower grid nodes exist The active power and reactive power at the moment, and Respectively represent the leaf node set and node of the lower layer power grid The collection of child nodes of Representing a collection Remove the collection The nodes in and Respectively represent the lower grid nodes and nodes At the moment The voltage amplitude, is the voltage amplitude of the root node of the lower grid at time, and Respectively represent the lower power grid branches The resistance and reactance, and Respectively represent the lower grid nodes At the moment The active power and reactive power of the load, Indicates the lower grid node At the moment The reactive power of energy storage, and Respectively represent the lower grid nodes At the moment and time The storage capacity of energy storage, and Respectively represent the lower grid nodes The upper and lower limits of energy storage capacity, and Respectively represent the lower grid nodes The upper and lower limits of the energy storage active power, and Respectively represent the lower grid nodes The upper and lower limits of the energy storage reactive power.

5. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 4, characterized in that: The centralized control model is decomposed by using the HGD algorithm to obtain an upper-layer power grid control model and a lower-layer power grid control model, including: Decomposing the centralized objective function model by using the HGD algorithm to obtain an upper-layer control objective function model and a lower-layer control objective function model; Decomposing the upper-layer power grid security constraint model, the upper-layer and lower-layer power grid security constraint model and the lower-layer power grid security constraint model by using the HGD algorithm, and determining the upper-layer power grid security constraint model and the upper-layer and lower-layer power grid security constraint model as the upper-layer regulation security constraint model, and determining the lower-layer power grid security constraint model as the lower-layer regulation security constraint model; Among them, the expression of the upper-level control objective function model is: ； ； In the formula, is the node index of the upper power grid, is the node index of the power grid at the boundary, Subscript for time, is a set of nodes of the upper power grid, is the root node set of the lower power grid, The upper grid node The active power of the distributed power source connected to The upper grid node The active power of the distributed energy storage element connected to the , and are nodes in the upper power grid Cost coefficients of quadratic, linear and constant terms of distributed generation, is a node in the upper power grid The penalty coefficient for the use of distributed energy storage, is the intermediate variable, Represents the boundary nodes exist The voltage amplitude at the moment, is the Lagrange multiplier associated with the formula; is a node in the lower power grid In The voltage amplitude at the moment, The lower grid node Penalty coefficient for voltage deviation, lower grid node represents the root node of the lower power grid, Represents the node of the lower grid The collection of child nodes; The expression of the lower-level control objective function model is: ； In the formula, is the node index of the lower power grid, Subscript for time, is the node set of the lower-layer power grid. The node subscripts of the boundary power grid corresponding to the root node of the lower-layer power grid are , is a node in the lower power grid The penalty coefficient for the use of distributed energy storage components, The lower grid node The active power of the distributed energy storage element connected to the is a node in the lower power grid Penalty factor for voltage deviation, is a node in the lower power grid The voltage amplitude, and Respectively indicate that the boundaries are The active power and reactive power flowing to the lower grid at all times, and They represent the Lagrange multipliers associated with the formula respectively.

6. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 5, characterized in that: The iteratively solving the parameter variables in the upper-layer power grid control model and the lower-layer power grid control model includes: Initialize the lower-level control objective function model and calculate the parameters , and , and the parameters , and Sending to the upper power grid; When the upper power grid receives the parameter , and , calculate the parameters in the upper-level control objective function model , and , and the parameters , and Send to each lower power grid; When each lower-layer power grid receives the parameters , and , calculate the parameters in the lower-level control objective function model , and , and the parameters , and Sending to the upper power grid; Repeat the above steps when the upper power grid receives the parameter , and , calculate the parameters in the upper-level control objective function model , and , and the parameters , and The steps of sending the parameters to each lower power grid, and when each lower power grid receives the parameters , and , calculate the parameters in the lower-level control objective function model , and , and the parameters , and The step of sending to the upper power grid is performed until the upper control objective function model and the lower control objective function model converge.

7. A distributed energy storage optimization control method taking into account multi-level power grid coordination according to claim 6, characterized in that: The filtering process of the iterative data comprises: Using the first filtering model and the second filtering model to filter the iterative data in the upper power grid control model and the lower power grid control model; The expression of the first filtering model is: ； In the formula, are the parameters passed to the upper power grid, is the number of iterations, is the filter coefficient, , is the value of the previous iteration, is calculated by the lower-level power grid control model ; The expression of the second filtering model is: ； In the formula, are the parameters passed to the lower grid, is the number of iterations, is the filter coefficient, , is the value of the previous iteration, is calculated by the upper power grid control model .

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