Micro-grid group energy storage capacity optimal configuration method and device

Abstract

The invention relates to a micro-grid group energy storage capacity optimal configuration method and device, and the method comprises the steps: constructing a micro-grid group electric energy balance rule corresponding to a target micro-grid group; based on the microgrid group electric energy balance rule, determining a target function corresponding to the total operation cost of the target microgrid group within one day, and based on the operation characteristics of the target microgrid group, establishing a plurality of optimal configuration constraint conditions corresponding to the target microgrid group; and based on the target function and the plurality of optimal configuration constraint conditions, constructing an energy storage capacity optimal configuration model corresponding to the target micro-grid group, solving the energy storage capacity optimal configuration model to obtain an energy storage capacity configuration result, and performing optimal configuration on the energy storage capacity of the target micro-grid group according to the energy storage capacity configuration result. Therefore, the problems that most of existing micro-grid group energy storage capacity optimization configuration technologies focus on single energy storage application value, complementary characteristics of energy storage of all sub micro-grids are not fully utilized, and the potential of energy storage in the aspect of improving comprehensive benefits of the micro-grid group is limited are solved.

Description

Technical Field

[0001] This application relates to the field of energy storage optimization configuration technology, and in particular to a method and device for optimizing the configuration of energy storage capacity in a microgrid group. Background Technology

[0002] In recent years, with the rapid development of distributed energy technology, microgrid clusters composed of multiple microgrids are gradually becoming a key link in connecting to the upper-level power grid, serving as an important form of efficient utilization of renewable energy. However, due to the uncertainty of wind and solar power generation within each sub-microgrid, the power supply and demand balance of the microgrid cluster faces significant challenges. Energy storage, as a device with flexible access location and the ability to quickly adjust in both directions, fully leverages its unique power compensation function, enabling the system to possess greater flexibility and regulation capabilities. Therefore, the rational configuration of energy storage capacity in microgrid clusters is crucial.

[0003] Optimization of energy storage capacity configuration in microgrid clusters refers to leveraging the complementary advantages of energy storage in each sub-microgrid through refined management strategies. This requires not only considering the operational economy of the microgrid cluster but also delving into various constraints in the energy storage capacity configuration process, such as the charging and discharging power and capacity limitations of energy storage devices, to ensure the feasibility and practicality of the configuration scheme.

[0004] However, most existing microgrid group energy storage capacity optimization configuration technologies focus on the value of single energy storage applications and do not fully utilize the complementary characteristics of energy storage in each sub-microgrid, thus limiting the potential of energy storage in improving the overall benefits of microgrid groups, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for optimizing the energy storage capacity of a microgrid cluster, in order to address the problems that most existing microgrid cluster energy storage capacity optimization technologies focus on the value of a single energy storage application and do not fully utilize the complementary characteristics of energy storage in each sub-microgrid, thus limiting the potential of energy storage in improving the overall benefits of the microgrid cluster.

[0006] The first aspect of this application provides a method for optimizing the energy storage capacity configuration of a microgrid group, comprising the following steps: constructing a power balance rule for the microgrid group corresponding to a target microgrid group; determining an objective function corresponding to the total daily operating cost of the target microgrid group based on the power balance rule, and establishing multiple optimization configuration constraints for the target microgrid group based on its operating characteristics; constructing an energy storage capacity optimization configuration model for the target microgrid group based on the objective function and the multiple optimization configuration constraints, and solving the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration result, and optimizing the energy storage capacity configuration of the target microgrid group according to the energy storage capacity configuration result.

[0007] Optionally, in one embodiment of this application, the microgrid group power balance rule includes: determining whether there is a power-deficient sub-microgrid in the target microgrid group that meets a preset power deficiency condition or a power-redundant sub-microgrid that meets a preset power redundancy condition; if there is a power-deficient sub-microgrid in the target microgrid group that meets the preset power deficiency condition, then controlling the adjacent sub-microgrids of the power-deficient sub-microgrid to perform power transfer to the power-deficient sub-microgrid, and determining whether the power-deficient sub-microgrid after power transfer meets the preset power balance requirement, wherein, when the power-deficient sub-microgrid does not meet the preset power balance requirement, through the... The power-deficient sub-microgrid purchases electricity from the target grid until it meets a preset power balance requirement. If there is a power-redundant sub-microgrid in the target microgrid group that meets the preset power redundancy condition, power is transferred to the adjacent sub-microgrids of the power-redundant sub-microgrid through the power-redundant sub-microgrid, and it is determined whether the power-redundant sub-microgrid after the power transfer meets the preset power balance requirement. If the power-redundant sub-microgrid does not meet the preset power balance requirement, it sells electricity to the target grid through the power-redundant sub-microgrid until it meets the preset power balance requirement.

[0008] Optionally, in one embodiment of this application, the objective function corresponding to determining the total daily operating cost of the target microgrid group includes: calculating the replacement cost and remaining recovery value of the target energy storage equipment; determining the unit investment cost of energy storage capacity, unit investment cost of energy storage power, rated energy storage capacity, and rated energy storage power of each sub-microgrid in the target microgrid group; and calculating the total daily operating cost of the target microgrid group based on the replacement cost, the remaining recovery value, the unit investment cost of energy storage capacity, the unit investment cost of energy storage power, the rated energy storage capacity, and the rated energy storage power. The energy storage investment and construction cost is calculated; based on the energy storage unit operation and maintenance cost and the DG unit operation and maintenance cost of each sub-microgrid, the energy storage operation and maintenance cost and the wind and solar operation and maintenance cost of the target microgrid group are calculated respectively; the electricity purchase cost, the power transmission cost between sub-microgrids, the peak shaving and valley filling revenue, and the low-carbon revenue of the target microgrid group are calculated; the objective function of the energy storage capacity optimization configuration model is constructed based on the energy storage investment and construction cost, energy storage operation and maintenance cost, wind and solar operation and maintenance cost, electricity purchase cost, power transmission cost between sub-microgrids, peak shaving and valley filling revenue, low-carbon revenue, and the preset prediction error penalty and fluctuation penalty.

[0009] Optionally, in one embodiment of this application, establishing multiple optimization configuration constraints corresponding to the target microgrid group based on the operating characteristics of the target microgrid group includes: determining the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power of the target microgrid group, so as to establish power balance constraints among the multiple optimization configuration constraints based on the tie-line power, the distributed generation output, the energy storage discharge power, the energy storage charging power, and the load power; obtaining the theoretical output of the distributed generation, tie-line power limit, and rated energy storage capacity of the target microgrid group, so as to determine the distributed generation output constraint, tie-line power constraint, energy storage charge / discharge state constraint, energy storage power output constraint, energy storage capacity constraint, energy storage SOC constraint, energy storage charge / discharge time constraint, and inter-microgrid power transmission constraint among the multiple optimization configuration constraints based on the theoretical output of the distributed generation, tie-line power limit, and rated energy storage capacity.

[0010] Optionally, in one embodiment of this application, solving the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration result includes: converting the energy storage capacity optimization configuration model into a linear model, and solving the linear model using a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result includes the target energy storage power capacity and the target energy storage rated capacity corresponding to the target microgrid group.

[0011] A second aspect of this application provides a microgrid group energy storage capacity optimization configuration device, comprising: a rule module for constructing a microgrid group power balance rule corresponding to a target microgrid group; an establishment module for determining an objective function corresponding to the total daily operating cost of the target microgrid group based on the microgrid group power balance rule, and establishing multiple optimization configuration constraints corresponding to the target microgrid group based on the operating characteristics of the target microgrid group; and an optimization configuration module for constructing an energy storage capacity optimization configuration model corresponding to the target microgrid group based on the objective function and the multiple optimization configuration constraints, solving the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration result, and optimizing the energy storage capacity of the target microgrid group according to the energy storage capacity configuration result.

[0012] Optionally, in one embodiment of this application, the rule module includes: a judgment unit, configured to judge whether there is a power-deficient sub-microgrid or a power-redundant sub-microgrid satisfying a preset power deficiency condition in the target microgrid group; and a power deficiency unit, configured to, if there is a power-deficient sub-microgrid satisfying the preset power deficiency condition in the target microgrid group, control the adjacent sub-microgrids of the power-deficient sub-microgrid to perform power transfer to the power-deficient sub-microgrid, and judge whether the power-deficient sub-microgrid after power transfer meets a preset power balance requirement, wherein, when the power-deficient sub-microgrid does not meet the preset power balance requirement, the rule module further includes: a judgment unit, configured to judge whether there is a power-deficient sub-microgrid satisfying a preset power deficiency condition in the target microgrid group, and ... meets a preset power balance requirement, and judge whether the power-deficient sub-microgrid meets a preset power balance requirement, and judge whether the power-deficient sub-microgrid meets a preset power balance requirement, and judge whether the power-deficient sub-microgrid meets a preset power balance requirement, and judge whether the power-deficient sub-microgrid meets a preset power balance requirement, and judge whether the power-deficient sub- A power-deficient sub-microgrid purchases electricity from the target grid until the power-deficient sub-microgrid meets a preset power balance requirement. A power redundancy unit is used to transfer power to adjacent sub-microgrids of the power-redundant sub-microgrid if there is a power-redundant sub-microgrid in the target microgrid group that meets the preset power redundancy condition, and to determine whether the power-redundant sub-microgrid after the power transfer meets the preset power balance requirement. If the power-redundant sub-microgrid does not meet the preset power balance requirement, it sells electricity to the target grid through the power-redundant sub-microgrid until the power-redundant sub-microgrid meets the preset power balance requirement.

[0013] Optionally, in one embodiment of this application, the establishment module includes: a first calculation unit, used to calculate the replacement cost and remaining recovery value of the target energy storage device; a first determination unit, used to determine the unit investment cost of energy storage capacity, unit investment cost of energy storage power, rated energy storage capacity, and rated energy storage power of each sub-microgrid in the target microgrid group; a second calculation unit, used to calculate the energy storage investment and construction cost of the target microgrid group based on the replacement cost, the remaining recovery value, the unit investment cost of energy storage capacity, the unit investment cost of energy storage power, the rated energy storage capacity, and the rated energy storage power; The third calculation unit is used to calculate the energy storage operation and maintenance cost and wind and solar operation and maintenance cost of the target microgrid group based on the energy storage unit operation and maintenance cost and the DG unit operation and maintenance cost of each sub-microgrid. The fourth calculation unit is used to calculate the electricity purchase cost, inter-sub-microgrid power transmission cost, peak shaving and valley filling revenue, and low-carbon revenue of the target microgrid group. The construction unit is used to construct the objective function of the energy storage capacity optimization configuration model based on the energy storage investment and construction cost, energy storage operation and maintenance cost, wind and solar operation and maintenance cost, electricity purchase cost, inter-sub-microgrid power transmission cost, peak shaving and valley filling revenue, low-carbon revenue, and preset prediction error penalty and fluctuation penalty.

[0014] Optionally, in one embodiment of this application, the establishment module further includes: a second determining unit, configured to determine the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power of the target microgrid group, so as to establish power balance constraints among the plurality of optimization configuration constraints based on the tie-line power, the distributed generation output, the energy storage discharge power, the energy storage charging power, and the load power; and an obtaining unit, configured to obtain the theoretical output of the distributed generation, tie-line power limit, and rated energy storage capacity of the target microgrid group, so as to determine the distributed generation output constraints, tie-line power constraints, energy storage charge / discharge state constraints, energy storage power output constraints, energy storage capacity constraints, energy storage SOC constraints, energy storage charge / discharge time constraints, and inter-microgrid power transmission constraints among the plurality of optimization configuration constraints based on the theoretical output of the distributed generation, tie-line power limit, and rated energy storage capacity.

[0015] Optionally, in one embodiment of this application, the optimization configuration module includes: a solution unit, used to convert the energy storage capacity optimization configuration model into a linear model, and solve the linear model using a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result includes the target energy storage power capacity and the target energy storage rated capacity corresponding to the target microgrid group.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microgrid group energy storage capacity optimization configuration method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the energy storage capacity of a microgrid cluster.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described microgrid group energy storage capacity optimization configuration method.

[0019] Therefore, the embodiments of this application have the following beneficial effects: The embodiments of this application construct a power balance rule for a target microgrid group; based on the power balance rule, determine the objective function corresponding to the total daily operating cost of the target microgrid group; and based on the operating characteristics of the target microgrid group, establish multiple optimization configuration constraints. Based on the objective function and the multiple optimization configuration constraints, construct an energy storage capacity optimization configuration model for the target microgrid group, solve the model to obtain the energy storage capacity configuration result, and optimize the energy storage capacity configuration of the target microgrid group based on the configuration result. This application effectively reduces the operating cost of the microgrid group by determining the power capacity and rated capacity of the energy storage, thereby improving the system's economic efficiency. This solves the problem that existing microgrid group energy storage capacity optimization configuration technologies mostly focus on the value of a single energy storage application, failing to fully utilize the complementary characteristics of energy storage in each sub-microgrid, thus limiting the potential of energy storage in improving the overall benefits of the microgrid group.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for optimizing the energy storage capacity of a microgrid cluster according to an embodiment of this application. Figure 2 A schematic diagram of a microgrid cluster is provided as an embodiment of this application; Figure 3 A block diagram of an energy storage capacity optimization configuration model is provided for one embodiment of this application; Figure 4 This is an example diagram of a microgrid group energy storage capacity optimization configuration device according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Among them, 10-microgrid group energy storage capacity optimization configuration device; 100-rule module, 200-establishment module, 300-optimization configuration module; 501-memory, 502-processor, 503-communication interface. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following describes a method and apparatus for optimizing the energy storage capacity configuration of a microgrid group according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides a method for optimizing the energy storage capacity configuration of a microgrid group. In this method, a power balance rule for the target microgrid group is constructed; based on the power balance rule, an objective function corresponding to the total daily operating cost of the target microgrid group is determined; and based on the operating characteristics of the target microgrid group, multiple optimization configuration constraints are established; based on the objective function and the multiple optimization configuration constraints, an energy storage capacity optimization configuration model for the target microgrid group is constructed, and the model is solved to obtain the energy storage capacity configuration result; and the energy storage capacity of the target microgrid group is optimized based on the configuration result. This application effectively reduces the operating cost of the microgrid group by determining the power capacity and rated capacity of the energy storage, thereby improving the system's economic efficiency. This solves the problem that existing microgrid group energy storage capacity optimization configuration technologies mostly focus on the single application value of energy storage, failing to fully utilize the complementary characteristics of energy storage in each sub-microgrid, thus limiting the potential of energy storage in improving the overall benefits of the microgrid group.

[0025] Specifically, Figure 1 This is a flowchart illustrating a method for optimizing the energy storage capacity of a microgrid cluster, as provided in an embodiment of this application.

[0026] like Figure 1 As shown, the method for optimizing the energy storage capacity configuration of this microgrid group includes the following steps: In step S101, the power balance rule of the microgrid group corresponding to the target microgrid group is constructed. This application first takes the formation of a microgrid group from multiple microgrids and its connection to the upper-level power grid as the background, and considers the overall economic efficiency of the microgrid group to establish the power balance rules within the microgrid group.

[0027] Optionally, in one embodiment of this application, the power balance rule for a microgrid group includes: determining whether there is a power-deficient sub-microgrid in the target microgrid group that meets a preset power deficiency condition or a power-redundant sub-microgrid that meets a preset power redundancy condition; if there is a power-deficient sub-microgrid in the target microgrid group that meets the preset power deficiency condition, then controlling the adjacent sub-microgrids of the power-deficient sub-microgrid to perform power transfer to the power-deficient sub-microgrid, and determining whether the power-deficient sub-microgrid after power transfer meets the preset power balance requirement, wherein when the power-deficient sub-microgrid does not meet the preset power balance requirement, power is purchased from the target grid through the power-deficient sub-microgrid until the power-deficient sub-microgrid meets the preset power balance requirement; if there is a power-redundant sub-microgrid in the target microgrid group that meets the preset power redundancy condition, then power is transferred from the adjacent sub-microgrids of the power-redundant sub-microgrid through the power-redundant sub-microgrid, and determining whether the power-redundant sub-microgrid after power transfer meets the preset power balance requirement, wherein when the power-redundant sub-microgrid does not meet the preset power balance requirement, power is sold to the target grid through the power-redundant sub-microgrid until the power-redundant sub-microgrid meets the preset power balance requirement.

[0028] In the embodiments of this application, such as Figure 2 As shown, the microgrid system consists of three sub-microgrids, each with a tie-line switch to the external power grid. Each sub-microgrid contains wind turbines, photovoltaics, energy storage, and loads. Power exchange can occur between the sub-microgrids, forming a microgrid cluster connected to the external power grid. Shared energy storage is configured in the system to ensure the complementarity of energy storage charging and discharging needs.

[0029] It should be noted that the power balance rule within a microgrid group refers to the flexible switching between three modes—off-grid operation, partial grid-connected operation, and grid-connected operation—to achieve power balance among the sub-microgrids within the microgrid group when there is a power shortage or redundancy in the sub-microgrids.

[0030] Specifically, the power balance in the case of power shortage within the sub-microgrid is as follows: when there is a power shortage in the sub-microgrid within the microgrid group, power is preferentially transferred from the adjacent sub-microgrid. If power balance cannot be achieved (i.e., the preset power balance requirements cannot be met), then power is purchased from the grid.

[0031] The specific power balance under the power redundancy situation within the sub-microgrid is as follows: when there is power redundancy in the sub-microgrids within the microgrid group, power is preferentially transferred to adjacent sub-microgrids. If power balance cannot be achieved, then power is sold to the grid.

[0032] Therefore, the embodiments of this application provide reliable data and technical support for the optimized configuration of energy storage capacity in microgrid groups by constructing the power balance rules of the target microgrid group.

[0033] In step S102, based on the power balance rules of the microgrid group, the objective function corresponding to the total daily operating cost of the target microgrid group is determined, and based on the operating characteristics of the target microgrid group, multiple optimization configuration constraints corresponding to the target microgrid group are established.

[0034] Furthermore, based on the power balance rules of microgrid groups, the embodiments of this application also need to start from the operational economy of microgrid groups, and establish the objective function of the energy storage capacity optimization configuration model with the goal of minimizing the total daily operating cost of the target microgrid group system; at the same time, the embodiments of this application also need to consider the operating characteristics of microgrid groups, thereby establishing multiple optimization configuration constraints of the energy storage capacity optimization configuration model.

[0035] Optionally, in one embodiment of this application, determining the objective function corresponding to the total daily operating cost of the target microgrid group includes: calculating the replacement cost and remaining recovery value of the target energy storage equipment; determining the unit investment cost of energy storage capacity, unit investment cost of energy storage power, rated energy storage capacity, and rated energy storage power of each sub-microgrid in the target microgrid group; calculating the energy storage investment and construction cost of the target microgrid group based on the replacement cost, remaining recovery value, unit investment cost of energy storage capacity, unit investment cost of energy storage power, rated energy storage capacity, and rated energy storage power; calculating the energy storage operation and maintenance cost and wind and solar operation and maintenance cost of the target microgrid group based on the unit operation and maintenance cost of energy storage and the unit operation and maintenance cost of DG for each sub-microgrid; calculating the electricity purchase cost, inter-sub-microgrid power transmission cost, peak shaving and valley filling revenue, and low-carbon revenue of the target microgrid group; and constructing the objective function of the energy storage capacity optimization configuration model based on the energy storage investment and construction cost, energy storage operation and maintenance cost, wind and solar operation and maintenance cost, electricity purchase cost, inter-sub-microgrid power transmission cost, peak shaving and valley filling revenue, low-carbon revenue, and preset prediction error penalty and fluctuation penalty.

[0036] It should be noted that the embodiments of this application need to determine the energy storage investment and construction costs, energy storage operation and maintenance costs, wind and solar operation and maintenance costs, wind and solar losses, electricity purchase costs, inter-microgrid power transmission costs, prediction error penalties, fluctuation penalties, peak shaving and valley filling benefits, and low-carbon benefits in order to construct the objective function of the energy storage capacity optimization configuration model, as described below. (1) The investment and construction cost of energy storage is: (1) in, For the first i Unit investment cost of energy storage capacity of individual microgrids; For the first i Unit investment cost of energy storage capacity of individual microgrids; For the first i Rated capacity of individual microgrid energy storage; For the firsti Rated power of individual microgrid energy storage; Costs associated with the replacement of energy storage devices; This refers to the remaining recycling value of energy storage equipment.

[0037] The replacement cost of energy storage equipment is: (2) in, k Number of times the battery body can be replaced; T For battery energy storage lifespan; The initial investment cost is used as the basis for calculating the total life-cycle cost during the planning period. If the project cycle is short, the replacement cost is not considered.

[0038] The remaining recycling value of energy storage equipment is: (3) in, c res The recovery factor is fixed.

[0039] (2) The operation and maintenance cost of energy storage is shown in the following formula: (4) in, For the first i The unit operation and maintenance cost of individual microgrid energy storage.

[0040] (3) The mathematical expression for the operation and maintenance cost of wind and solar power is: (5) in, For the first i The unit operation and maintenance cost of a microgrid DG.

[0041] (4) The mathematical expression for wind and light loss is: (6) in, Cost per unit of photovoltaic loss; Cost per unit of wind power loss; For the first i Photovoltaic power loss of individual microgrids; For the first i Wind power loss in individual microgrids.

[0042] (5) The mathematical expression for electricity purchase cost is: (7) in, This refers to the electricity purchase price from the grid by the microgrid cluster; This refers to the electricity price sold from the microgrid cluster to the grid. For the first i The power absorbed by each microgrid from the grid; For the first i The power that a microgrid sends to the grid.

[0043] (6) The mathematical expression for the cost of power transmission between sub-microgrids is: (8) in, Cost of unit power transmission between sub-microgrids; This refers to the power transmission capacity between sub-microgrids.

[0044] (7) The mathematical expression for the prediction error penalty is: (9) in, This is the prediction error penalty coefficient; For the first i The photovoltaic prediction error penalty power of individual microgrids; For the first i Wind power prediction error penalty power for individual microgrids.

[0045] (8) The mathematical expression for the fluctuation penalty is: (10) in, This refers to the power of the tie line.

[0046] (9) The mathematical expression for the peak shaving and valley filling benefits is: (11) in, The peak shaving and valley filling benefit coefficient; Time-of-use pricing; This refers to the energy storage discharge power; This refers to the energy storage discharge power; This is the variable for initiating energy storage discharge operation; it is a 0-1 variable. This is the variable used to initiate the energy storage discharge operation; it is a 0-1 variable.

[0047] (10) The mathematical expression for the low-carbon benefits is: (12) Among them, carbon emission coefficient .

[0048] Subsequently, in this embodiment of the application, an objective function for the energy storage capacity optimization configuration model can be established based on the aforementioned parameters such as energy storage investment and construction costs, as shown in the following equation: (13) in, For energy storage investment and construction costs; For energy storage operation and maintenance costs; For wind and solar power operation and maintenance costs; Loss of scenic beauty; For electricity purchase costs; Costs of power transmission between sub-microgrids; Penalty for prediction error; For fluctuation penalty; To generate revenue from peak shaving and valley filling; For low-carbon benefits.

[0049] Based on the above objective function, the embodiments of this application can use the Shapley value method to solve the problem of allocating the shared energy storage configuration cost among multiple microgrids. That is, the embodiments of this application use the microgrid MG i The benefit derived from the overall benefit (i.e., the cost savings resulting from building shared energy storage) is shown in the following formula: (14) in, v ( S ) for MG i Participate in the alliance S The entire alliance S The resulting benefits; v ( S { i}) to remove microgrid MG i Time Alliance S The resulting benefits; v ( S )- v ( S { i}) represents the microgrid MG i Participating in different alliances S The marginal contribution created for oneself and the alliance.

[0050] Therefore, the embodiments of this application aim to minimize the total operating cost of the target microgrid group system within a day, and construct an objective function for the energy storage capacity optimization configuration model, thereby ensuring the reliability of the objective function.

[0051] Optionally, in one embodiment of this application, based on the operating characteristics of the target microgrid group, multiple optimization configuration constraints corresponding to the target microgrid group are established, including: determining the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power of the target microgrid group, so as to establish power balance constraints among multiple optimization configuration constraints based on the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power; obtaining the theoretical output of distributed generation, tie-line power limit, and rated energy storage capacity of the target microgrid group, so as to determine the distributed generation output constraint, tie-line power constraint, energy storage charge / discharge state constraint, energy storage power output constraint, energy storage capacity constraint, energy storage SOC constraint, energy storage charge / discharge time constraint, and inter-microgrid power transmission constraint among multiple optimization configuration constraints based on the theoretical output of distributed generation, tie-line power limit, and rated energy storage capacity.

[0052] In practical implementation, embodiments of this application can establish multiple optimization configuration constraints for the energy storage capacity optimization configuration model based on the operating characteristics of microgrid clusters. The specific optimization configuration constraints are as follows: (1) The mathematical expression for the power balance constraint is: (15) in, For tie line power; Provide power for distributed power sources; This refers to the energy storage discharge power; Power for energy storage charging; This represents the load power.

[0053] (2) The mathematical expression for the output constraint of distributed power sources is: (16) in, It contributes to the theory of distributed power generation.

[0054] (3) The mathematical expression for tie-line power constraints is: (17) in, Power limit for tie lines.

[0055] (4) The mathematical expression for the energy storage charge / discharge state constraints is: (18) in, This is the variable for initiating energy storage discharge operation; it is a 0-1 variable. The variable used to initiate the energy storage charging operation is a 0-1 variable.

[0056] (5) The mathematical expression for the energy storage power output constraint is: (19) in, This represents the minimum energy storage discharge power. This represents the maximum energy storage discharge power. This represents the minimum charging power for energy storage. This represents the maximum charging power for energy storage.

[0057] (6) The mathematical expression for the energy storage capacity constraint is: (20) in, for The amount of energy stored at any given moment; for The amount of energy stored at any given moment; This represents the minimum energy storage capacity. This represents the maximum energy storage capacity.

[0058] (7) The mathematical expression for the energy storage SOC constraint is: (twenty one) in, The state of charge of the stored energy at time t; This represents the minimum state of charge for energy storage. This represents the maximum state of charge of the stored energy. This refers to the rated capacity of the energy storage. For energy storage charging efficiency; The discharge efficiency of energy storage; The state of charge of energy storage at time 0; The state of charge of energy storage at 24 hours; Let t be the duration of the state at time t.

[0059] (8) The mathematical expression for the energy storage charging and discharging time constraint is: (twenty two) in, This refers to the rated capacity of the energy storage.

[0060] (9) The constraints on power transmission between microgrids include the following two forms: 1) State constraint: Two microgrids cannot simultaneously purchase and sell electricity, as shown in the following formula: (twenty three) in, This is the variable used to initiate the microgrid electricity purchase operation; it is a 0-1 variable. This is the variable used to initiate microgrid electricity sales operations; it is a 0-1 variable.

[0061] 2) Power Constraints: Sub-microgrids prioritize power autonomy before considering external power sales, thereby avoiding ineffective power flow, as shown in the following equation: (twenty four) Therefore, the embodiments of this application establish multiple optimization configuration constraints for the energy storage capacity optimization configuration model based on the operating characteristics of the microgrid group, thereby providing a reliable data basis for the construction of the energy storage capacity optimization configuration model.

[0062] In step S103, based on the objective function and multiple optimization configuration constraints, an energy storage capacity optimization configuration model corresponding to the target microgrid group is constructed, and the energy storage capacity optimization configuration model is solved to obtain the energy storage capacity configuration result. The energy storage capacity of the target microgrid group is then optimized based on the energy storage capacity configuration result.

[0063] Furthermore, embodiments of this application can construct an energy storage capacity optimization configuration model corresponding to the target microgrid group based on the objective function and multiple optimization configuration constraints, such as... Figure 3 As shown, the energy storage capacity optimization configuration model is solved to obtain the energy storage capacity configuration result, and then the energy storage capacity of the target microgrid group is optimized based on the energy storage capacity configuration result.

[0064] Optionally, in one embodiment of this application, solving the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration result includes: converting the energy storage capacity optimization configuration model into a linear model, and solving the linear model using a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result includes the target energy storage power capacity and the target energy storage rated capacity corresponding to the target microgrid group.

[0065] It should be noted that the embodiments of this application may employ the Big M method to transform the nonlinear part of the energy storage capacity optimization configuration model into a linearized model for solution, and provide the MATLAB platform to call the Gurobi solver to solve the linearized model in order to determine the power capacity and rated capacity of the energy storage, thereby effectively reducing the operating cost of the microgrid group and improving the system economy.

[0066] Therefore, this application embodiment considers the energy storage configuration strategy of subgrid self-consistency and inter-group sharing, and optimizes it with the goal of achieving the best economic operation of microgrid groups. This provides a reference for the optimized configuration of energy storage capacity in microgrid groups, further reduces the operating cost of distribution networks, and promotes the consumption of new energy.

[0067] The microgrid group energy storage capacity optimization configuration method proposed in this application involves: constructing a power balance rule for the target microgrid group; determining the objective function corresponding to the total daily operating cost of the target microgrid group based on the power balance rule; establishing multiple optimization configuration constraints for the target microgrid group based on its operating characteristics; constructing an energy storage capacity optimization configuration model for the target microgrid group based on the objective function and the multiple optimization configuration constraints; solving the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration result; and optimizing the energy storage capacity configuration of the target microgrid group based on the energy storage capacity configuration result. This application effectively reduces the operating cost of the microgrid group by determining the power capacity and rated capacity of the energy storage, thereby improving the system's economic efficiency.

[0068] Secondly, the microgrid group energy storage capacity optimization configuration device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0069] Figure 4 This is a block diagram of a microgrid group energy storage capacity optimization configuration device according to an embodiment of this application.

[0070] like Figure 4 As shown, the microgrid group energy storage capacity optimization configuration device 10 includes: a rule module 100, an establishment module 200, and an optimization configuration module 300.

[0071] Among them, the rule module 100 is used to construct the power balance rules of the target microgrid group.

[0072] Module 200 is established to determine the objective function corresponding to the total daily operating cost of the target microgrid group based on the power balance rules of the microgrid group, and to establish multiple optimization configuration constraints corresponding to the target microgrid group based on the operating characteristics of the target microgrid group.

[0073] The optimization configuration module 300 is used to construct an energy storage capacity optimization configuration model corresponding to the target microgrid group based on the objective function and multiple optimization configuration constraints, solve the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration result, and optimize the energy storage capacity configuration of the target microgrid group according to the energy storage capacity configuration result.

[0074] Optionally, in one embodiment of this application, the rule module 100 includes: a judgment unit, a power shortage unit, and a power redundancy unit.

[0075] The judgment unit is used to determine whether there is a power-deficient sub-microgrid or a power-redundant sub-microgrid in the target microgrid group that meets the preset power deficiency conditions or the preset power redundancy conditions.

[0076] The power shortage unit is used to control the adjacent sub-microgrids of the power shortage sub-microgrid to transfer power to the power shortage sub-microgrid if there is a power shortage sub-microgrid in the target microgrid group that meets the preset power shortage conditions, and to determine whether the power shortage sub-microgrid after power transfer meets the preset power balance requirements. When the power shortage sub-microgrid does not meet the preset power balance requirements, it purchases electricity from the target grid through the power shortage sub-microgrid until the power shortage sub-microgrid meets the preset power balance requirements.

[0077] The power redundancy unit is used to transfer power to the adjacent sub-microgrids of the power redundancy sub-microgrid if there is a power redundancy sub-microgrid in the target microgrid group that meets the preset power redundancy conditions, and to determine whether the power redundancy sub-microgrid after power transfer meets the preset power balance requirements. When the power redundancy sub-microgrid does not meet the preset power balance requirements, it sells electricity to the target grid through the power redundancy sub-microgrid until the power redundancy sub-microgrid meets the preset power balance requirements.

[0078] Optionally, in one embodiment of this application, the establishment module 200 includes: a first calculation unit, a first determination unit, a second calculation unit, a third calculation unit, a fourth calculation unit, and a construction unit.

[0079] The first calculation unit is used to calculate the replacement cost and remaining recycling value of the target energy storage equipment.

[0080] The first determining unit is used to determine the unit investment cost of energy storage capacity, the unit investment cost of energy storage power, the rated energy storage capacity, and the rated energy storage power of each sub-microgrid in the target microgrid group.

[0081] The second calculation unit is used to calculate the energy storage investment and construction cost of the target microgrid group based on the replacement cost, remaining recovery value, unit investment cost of energy storage capacity, unit investment cost of energy storage power, rated energy storage capacity, and rated energy storage power.

[0082] The third calculation unit is used to calculate the energy storage operation and maintenance cost and wind and solar operation and maintenance cost of the target microgrid group based on the energy storage unit operation and maintenance cost and the DG unit operation and maintenance cost of each sub-microgrid.

[0083] The fourth calculation unit is used to calculate the electricity purchase cost of the target microgrid group, the power transmission cost between sub-microgrids, the peak shaving and valley filling benefits, and the low-carbon benefits.

[0084] The construction unit is used to construct the objective function of the energy storage capacity optimization configuration model based on the energy storage investment and construction cost, energy storage operation and maintenance cost, wind and solar operation and maintenance cost, electricity purchase cost, power transmission cost between sub-microgrids, peak shaving and valley filling benefits, low carbon benefits, and preset prediction error penalty and fluctuation penalty.

[0085] Optionally, in one embodiment of this application, the establishment module 200 further includes a second determining unit and an acquiring unit.

[0086] The second determining unit is used to determine the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power of the target microgrid group, so as to establish power balance constraints among multiple optimization configuration constraints based on the tie-line power, distributed generation output, energy storage discharge power, energy storage charging power, and load power.

[0087] The acquisition unit is used to acquire the theoretical output of distributed generation sources, tie-line power limits, and rated energy storage capacity of the target microgrid group. Based on the theoretical output of distributed generation sources, tie-line power limits, and rated energy storage capacity, it determines the distributed generation output constraints, tie-line power constraints, energy storage charge / discharge state constraints, energy storage power output constraints, energy storage capacity constraints, energy storage SOC constraints, energy storage charge / discharge time constraints, and inter-microgrid power transmission constraints among multiple optimization configuration constraints.

[0088] Optionally, in one embodiment of this application, the optimization configuration module 300 includes: a solution unit, used to convert the energy storage capacity optimization configuration model into a linear model, and solve the linear model through a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result includes the target energy storage power capacity and the target energy storage rated capacity corresponding to the target microgrid group.

[0089] It should be noted that the foregoing explanation of the embodiment of the microgrid group energy storage capacity optimization configuration method also applies to the microgrid group energy storage capacity optimization configuration device of this embodiment, and will not be repeated here.

[0090] The microgrid group energy storage capacity optimization configuration device proposed in this application includes a rule module for constructing the power balance rules of the target microgrid group; an establishment module for determining the objective function corresponding to the total daily operating cost of the target microgrid group based on the power balance rules, and establishing multiple optimization configuration constraints for the target microgrid group based on its operating characteristics; and an optimization configuration module for constructing an energy storage capacity optimization configuration model for the target microgrid group based on the objective function and multiple optimization configuration constraints, solving the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration result, and optimizing the energy storage capacity configuration of the target microgrid group based on the energy storage capacity configuration result. This application effectively reduces the operating cost of the microgrid group by determining the power capacity and rated capacity of the energy storage, thereby improving the system's economic efficiency.

[0091] Figure 5A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0092] When the processor 502 executes the program, it implements the microgrid group energy storage capacity optimization configuration method provided in the above embodiments.

[0093] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0094] The memory 501 is used to store computer programs that can run on the processor 502.

[0095] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0096] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0097] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0098] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0099] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for optimizing the energy storage capacity configuration of microgrid groups.

[0100] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described microgrid group energy storage capacity optimization configuration method.

[0101] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0102] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0103] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0105] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0106] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0108] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing configuration of energy storage capacity of micro-grid clusters, characterized in that, The method comprises the following steps: constructing a micro-grid group energy balance rule corresponding to a target micro-grid group; determining a target function corresponding to a total operation cost of the target micro-grid group within a day based on the micro-grid group energy balance rule, and establishing a plurality of optimization configuration constraint conditions corresponding to the target micro-grid group based on an operation characteristic of the target micro-grid group; constructing an energy storage capacity optimization configuration model corresponding to the target micro-grid group based on the target function and the plurality of optimization configuration constraint conditions, and solving the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration result, and optimizing the energy storage capacity of the target micro-grid group according to the energy storage capacity configuration result.

2. The method of claim 1, wherein, The micro-grid group energy balance rule comprises: determining whether there is a power deficiency sub-micro-grid satisfying a preset power deficiency condition or a power redundancy sub-micro-grid satisfying a preset power redundancy condition in the target micro-grid group; if there is a power deficiency sub-micro-grid satisfying the preset power deficiency condition in the target micro-grid group, controlling adjacent sub-micro-grids of the power deficiency sub-micro-grid to perform power transmission on the power deficiency sub-micro-grid, and determining whether the power deficiency sub-micro-grid after power transmission satisfies a preset power balance requirement, wherein when the power deficiency sub-micro-grid does not satisfy the preset power balance requirement, purchasing power from the target power grid through the power deficiency sub-micro-grid until the power deficiency sub-micro-grid satisfies the preset power balance requirement; if there is a power redundancy sub-micro-grid satisfying the preset power redundancy condition in the target micro-grid group, performing power transmission on adjacent sub-micro-grids of the power redundancy sub-micro-grid through the power redundancy sub-micro-grid, and determining whether the power redundancy sub-micro-grid after power transmission satisfies the preset power balance requirement, wherein when the power redundancy sub-micro-grid does not satisfy the preset power balance requirement, selling power to the target power grid through the power redundancy sub-micro-grid until the power redundancy sub-micro-grid satisfies the preset power balance requirement.

3. The method of claim 2, wherein, The determination of the target function corresponding to the total operation cost of the target micro-grid group within a day comprises: calculating an updated replacement cost and a remaining recycling value of a target energy storage device; determining an energy storage capacity unit investment cost, an energy storage power unit investment cost, an energy storage rated capacity and an energy storage rated power of each sub-micro-grid in the target micro-grid group; calculating an energy storage investment construction cost of the target micro-grid group according to the updated replacement cost, the remaining recycling value, the energy storage capacity unit investment cost, the energy storage power unit investment cost, the energy storage rated capacity and the energy storage rated power; calculating an energy storage operation and maintenance cost and a wind-solar operation and maintenance cost of the target micro-grid group based on an energy storage unit operation and maintenance cost and a DG unit operation and maintenance cost of each sub-micro-grid, respectively; calculating a power purchase cost, an inter-sub-micro-grid energy transmission cost, a peak clipping and valley filling benefit and a low-carbon benefit of the target micro-grid group; The target function of the energy storage capacity optimization configuration model is constructed according to the energy storage investment construction cost, the energy storage operation and maintenance cost, the wind and light operation and maintenance cost, the electricity purchase fee, the electric energy transmission fee between the sub-micro grids, the peak load shifting income, the low-carbon income, a preset prediction error penalty and a fluctuation penalty.

4. The method of claim 3, wherein, The multiple optimization configuration constraint conditions corresponding to the target micro grid group are established based on the operation characteristics of the target micro grid group, and the multiple optimization configuration constraint conditions include: The tie line power, the distributed power output, the energy storage discharge power, the energy storage charge power and the load power of the target micro grid group are determined to establish a power balance constraint condition in the multiple optimization configuration constraint conditions according to the tie line power, the distributed power output, the energy storage discharge power, the energy storage charge power and the load power; The distributed power theoretical output, the tie line power limit and the energy storage rated capacity of the target micro grid group are acquired to determine the multiple optimization configuration constraint conditions including the distributed power output constraint condition, the tie line power constraint condition, the energy storage charge and discharge state constraint condition, the energy storage power output constraint condition, the energy storage capacity constraint condition, the energy storage SOC constraint condition, the energy storage charge and discharge time constraint condition and the electric energy transmission constraint condition between micro grids based on the distributed power theoretical output, the tie line power limit and the energy storage rated capacity.

5. The method of claim 4, wherein, The energy storage capacity optimization configuration model is solved to obtain an energy storage capacity configuration result, and the energy storage capacity optimization configuration model is converted into a linear model, and the linear model is solved by a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result includes a target energy storage power capacity and a target energy storage rated capacity corresponding to the target micro grid group. The rule module is configured to construct a micro grid group electric energy balance rule corresponding to the target micro grid group; 6. A micro-grid cluster energy storage capacity optimization configuration device, characterized in that, The establishment module is configured to determine a target function corresponding to the total operation cost of the target micro grid group in a day based on the micro grid group electric energy balance rule, and establish multiple optimization configuration constraint conditions corresponding to the target micro grid group based on the operation characteristics of the target micro grid group; The optimization configuration module is configured to construct an energy storage capacity optimization configuration model corresponding to the target micro grid group based on the target function and the multiple optimization configuration constraint conditions, solve the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration result, and optimize the energy storage capacity of the target micro grid group according to the energy storage capacity configuration result. The rule module includes: A judgment unit is configured to judge whether there is a power deficiency sub-micro grid satisfying a preset power deficiency condition or a power redundancy sub-micro grid satisfying a preset power redundancy condition in the target micro grid group.

7. The apparatus of claim 6, wherein, ​ ​ a power deficiency unit, configured to control adjacent sub-microgrids of a power deficiency sub-microgrid to perform power transmission to the power deficiency sub-microgrid if the power deficiency sub-microgrid in the target microgrid group meets the preset power deficiency condition, and to determine whether the power deficiency sub-microgrid meets a preset power balance requirement after the power transmission, wherein, when the power deficiency sub-microgrid does not meet the preset power balance requirement, the power deficiency sub-microgrid purchases power from the target grid until the power deficiency sub-microgrid meets the preset power balance requirement; a power redundancy unit, configured to perform power transmission to adjacent sub-microgrids of a power redundancy sub-microgrid by the power redundancy sub-microgrid if the power redundancy sub-microgrid in the target microgrid group meets the preset power redundancy condition, and to determine whether the power redundancy sub-microgrid meets the preset power balance requirement after the power transmission, wherein, when the power redundancy sub-microgrid does not meet the preset power balance requirement, the power redundancy sub-microgrid sells power to the target grid until the power redundancy sub-microgrid meets the preset power balance requirement.

8. The apparatus of claim 7, wherein, The establishing module comprises: a first calculation unit, configured to calculate an updated replacement cost and a remaining recovery value of a target energy storage device; a first determination unit, configured to determine an energy storage capacity unit investment cost, an energy storage power unit investment cost, an energy storage rated capacity, and an energy storage rated power of each sub-microgrid in the target microgrid group; a second calculation unit, configured to calculate an energy storage investment construction cost of the target microgrid group according to the updated replacement cost, the remaining recovery value, the energy storage capacity unit investment cost, the energy storage power unit investment cost, the energy storage rated capacity, and the energy storage rated power; a third calculation unit, configured to calculate an energy storage operation and maintenance cost and a wind-solar operation and maintenance cost of the target microgrid group respectively based on an energy storage unit operation and maintenance cost and a DG unit operation and maintenance cost of each sub-microgrid; a fourth calculation unit, configured to calculate a power purchase cost, an inter-sub-microgrid power transmission cost, a peak clipping and valley filling benefit, and a low-carbon benefit of the target microgrid group; a construction unit, configured to construct an objective function of the energy storage capacity optimization configuration model according to the energy storage investment construction cost, the energy storage operation and maintenance cost, the wind-solar operation and maintenance cost, the power purchase cost, the inter-sub-microgrid power transmission cost, the peak clipping and valley filling benefit, the low-carbon benefit, a preset prediction error penalty, and a fluctuation penalty.

9. The apparatus of claim 8, wherein, The establishing module further comprises: a second determination unit, configured to determine a tie line power, a distributed power output, an energy storage discharge power, an energy storage charge power, and a load power of the target microgrid group, so as to establish a power balance constraint condition in the plurality of optimization configuration constraint conditions according to the tie line power, the distributed power output, the energy storage discharge power, the energy storage charge power, and the load power. The acquisition unit is configured to acquire theoretical output of distributed power supply, tie-line power limit and energy storage rated capacity of the target micro-grid group, and determine distributed power supply output constraint condition, tie-line power constraint condition, energy storage charge and discharge state constraint condition, energy storage power output constraint condition, energy storage capacity constraint condition, energy storage SOC constraint condition, energy storage charge and discharge time constraint condition and inter-micro-grid electric energy transmission constraint condition in the plurality of optimization configuration constraint conditions based on the theoretical output of distributed power supply, the tie-line power limit and the energy storage rated capacity.

10. The apparatus of claim 9, wherein, The optimization configuration module comprises: The solving unit is configured to convert the energy storage capacity optimization configuration model into a linear model, and solve the linear model by using a preset Gurobi solver to obtain the energy storage capacity configuration result, wherein the energy storage capacity configuration result comprises target energy storage power capacity and target energy storage rated capacity corresponding to the target micro-grid group.

11. An electronic device, comprising: Comprise: The memory, the processor and the computer program stored on the memory and executable on the processor, the processor executes the program to realize the micro-grid group energy storage capacity optimization configuration method of any one of claims 1-5.

12. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the micro-grid group energy storage capacity optimization configuration method of any one of claims 1-5.

13. A computer program product comprising a computer program, characterized in that, The computer program is executed to realize the micro-grid group energy storage capacity optimization configuration method of any one of claims 1-5.

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