Flexible interconnection micro-grid group optimization scheduling method and device and computer equipment

Abstract

The invention relates to a flexible interconnection micro-grid group optimization scheduling method. The method comprises the following steps: determining a coupling boundary constraint condition between two adjacent microgrids in a flexible interconnection microgrid group; according to the objective function, the optimization constraint condition and the coupling boundary constraint condition of the flexible interconnection micro-grid group, constructing an optimization scheduling model of the flexible interconnection micro-grid group; decomposing the optimal scheduling model into a plurality of sub-optimal scheduling models, and performing distributed solving processing on the sub-optimal scheduling models of two adjacent microgrids to obtain sub-optimal scheduling results corresponding to two connected microgrids; and obtaining an optimal scheduling result of the flexible interconnection micro-grid group according to the sub-optimal scheduling result of each micro-grid. By adopting the method, the group scheduling optimization efficiency and safety of the flexible interconnected micro-grid can be improved.

Description

Technical Field

[0001] The present application relates to the field of electrical automation technology, and in particular to a method, device, computer equipment, storage medium, and computer program product for optimizing and scheduling a flexible interconnected microgrid group. Background Art

[0002] With the global energy transition, the penetration of distributed energy continues to rise. Microgrids, as a key energy consumption method, have been widely developed and applied. However, due to their inherent capacity limitations and the volatility of renewable energy, individual microgrids struggle to effectively address the growing penetration of renewable energy. Therefore, interconnecting multiple microgrids to form a microgrid cluster, through energy synergy and coordinated optimization, has become an effective way to improve system reliability, cost-effectiveness, and renewable energy consumption capacity.

[0003] However, current methods for optimizing microgrid group scheduling require collecting global information from all microgrids in the group. This can lead to incomplete leakage of sensitive data during information exchange, resulting in heavy communication overhead and high privacy risks. Therefore, a method for optimizing microgrid group scheduling that balances communication efficiency and privacy protection is urgently needed. Summary of the Invention

[0004] Based on this, it is necessary to provide a flexible interconnected microgrid group optimization scheduling method, device, computer equipment, computer-readable storage medium and computer program product to address the above technical problems, which can improve the scheduling optimization efficiency and safety of flexible interconnected microgrid groups.

[0005] In a first aspect, the present application provides a method for optimizing and scheduling a flexible interconnected microgrid group. The method comprises:

[0006] Determine the coupling boundary constraints between two adjacent microgrids in a flexible interconnected microgrid group;

[0007] An optimization scheduling model for the flexible interconnected microgrid group is constructed according to the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group;

[0008] Decomposing the optimization scheduling model into multiple sub-optimization scheduling models, performing distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtaining sub-optimization scheduling results corresponding to the two adjacent microgrids;

[0009] According to the sub-optimal scheduling results of each microgrid, the optimized scheduling result of the flexible interconnected microgrid group is obtained.

[0010] In one embodiment, determining a coupling boundary constraint condition between two adjacent microgrids in a flexible interconnected microgrid group includes:

[0011] Obtaining coupling boundary variables between each microgrid and its adjacent microgrids based on a DC bus voltage of each microgrid in the flexible interconnected microgrid group and a DC active power between each microgrid and its adjacent microgrids;

[0012] Based on the coupling boundary variables, a coupling boundary constraint condition between two adjacent microgrids is obtained.

[0013] In one embodiment, before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the method further includes:

[0014] Obtaining the voltage constraint condition according to an upper voltage limit and a lower voltage limit of the DC bus voltage of the flexible interconnected microgrid group;

[0015] Obtaining the state of charge constraint condition according to the state of charge upper limit value and the state of charge lower limit value of the microgrid; the state of charge upper limit value and the state of charge lower limit value are obtained according to the energy storage power of the microgrid;

[0016] Obtaining the DC regional branch power flow constraint condition according to the active power balance information of the microgrid and the voltage relationship information between two adjacent microgrids;

[0017] Obtaining a transmission capacity constraint condition of the DC regional tie line according to the maximum transmission power of the tributary line of the flexible interconnected microgrid group;

[0018] Obtaining a constraint condition of the flexible interconnected device according to the active power and reactive power of the flexible interconnected device of the flexible interconnected microgrid group;

[0019] Obtaining the AC area constraint condition according to the active power and reactive power on the low-voltage side of the transformer of the flexible interconnected microgrid group;

[0020] The voltage constraint condition, the state of charge constraint condition, the DC area branch power flow constraint condition, the DC area tie line transmission capacity constraint condition, the flexible interconnection device constraint condition and the AC area constraint condition are set as the optimization constraint conditions.

[0021] In one embodiment, before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the method further includes:

[0022] Obtaining a total operating cost of the flexible interconnected microgrid group based on the charge and discharge power of the microgrids in the flexible interconnected microgrid group, the power sold to the upper power grid, and the transmission and exchange power between two adjacent microgrids;

[0023] The total operating cost is minimized to obtain an objective function of the flexible interconnected microgrid group.

[0024] In one embodiment, a distributed solution process is performed on the sub-optimal scheduling models of two adjacent microgrids to obtain sub-optimal scheduling results corresponding to the two adjacent microgrids, including:

[0025] Based on the dual variables of each microgrid, a sub-objective function of each microgrid is obtained;

[0026] Based on the sub-objective function, the sub-optimal scheduling models of the two adjacent microgrids are solved in parallel to obtain the decision variables corresponding to the two adjacent microgrids respectively;

[0027] updating the dual variables of the two adjacent microgrids according to the decision variables corresponding to the two adjacent microgrids;

[0028] If it is detected that each of the microgrids meets the preset convergence condition, then based on the decision variables, sub-optimal scheduling results corresponding to the two connected microgrids are obtained;

[0029] If it is detected that each of the microgrids does not meet the preset convergence condition, the process jumps to the step of obtaining the sub-objective function of each of the microgrids based on the dual variables of each of the microgrids.

[0030] In one embodiment, before determining the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group, the method further includes:

[0031] Acquire multiple microgrids that are geographically adjacent;

[0032] The multiple microgrids are connected in a flexible interconnection manner to obtain the flexible interconnected microgrid group.

[0033] In a second aspect, the present application also provides a flexible interconnected microgrid group optimization and scheduling device. The device includes:

[0034] A constraint determination module is used to determine the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group;

[0035] a model construction module, configured to construct an optimization scheduling model for the flexible interconnected microgrid group based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints comprising at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group;

[0036] A model solving module is used to decompose the optimization scheduling model into multiple sub-optimization scheduling models, perform distributed solution processing on two adjacent sub-optimization scheduling models, and obtain sub-optimization scheduling results corresponding to the two adjacent sub-optimization scheduling models;

[0037] The result determination module is used to obtain the optimized scheduling result of the flexible interconnected microgrid group according to the sub-optimal scheduling results of each sub-optimal scheduling model.

[0038] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0039] Determine the coupling boundary constraints between two adjacent microgrids in a flexible interconnected microgrid group;

[0040] An optimization scheduling model for the flexible interconnected microgrid group is constructed according to the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group;

[0041] Decomposing the optimization scheduling model into multiple sub-optimization scheduling models, performing distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtaining sub-optimization scheduling results corresponding to the two adjacent microgrids;

[0042] According to the sub-optimal scheduling results of each microgrid, the optimized scheduling result of the flexible interconnected microgrid group is obtained.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0044] Determine the coupling boundary constraints between two adjacent microgrids in a flexible interconnected microgrid group;

[0045] An optimization scheduling model for the flexible interconnected microgrid group is constructed according to the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group;

[0046] Decomposing the optimization scheduling model into multiple sub-optimization scheduling models, performing distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtaining sub-optimization scheduling results corresponding to the two adjacent microgrids;

[0047] According to the sub-optimal scheduling results of each microgrid, the optimized scheduling result of the flexible interconnected microgrid group is obtained.

[0048] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0049] Determine the coupling boundary constraints between two adjacent microgrids in a flexible interconnected microgrid group;

[0050] An optimization scheduling model for the flexible interconnected microgrid group is constructed according to the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group;

[0051] Decomposing the optimization scheduling model into multiple sub-optimization scheduling models, performing distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtaining sub-optimization scheduling results corresponding to the two adjacent microgrids;

[0052] According to the sub-optimal scheduling results of each microgrid, the optimized scheduling result of the flexible interconnected microgrid group is obtained.

[0053] The above-described flexible interconnected microgrid group optimization scheduling method, apparatus, computer device, storage medium, and computer program product determine the coupling boundary constraints between two adjacent microgrids in the flexible interconnected microgrid group; construct an optimization scheduling model for the flexible interconnected microgrid group based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of the flexible interconnected microgrid group's voltage constraints, state of charge constraints, DC region branch flow constraints, DC region tie line transmission capacity constraints, flexible interconnection device constraints, and AC region constraints; decompose the optimization scheduling model into multiple sub-optimization scheduling models, perform distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtain sub-optimization scheduling results corresponding to the two connected microgrids; and obtain the optimization scheduling results of the flexible interconnected microgrid group based on the sub-optimization scheduling results of each microgrid. Using this method, each microgrid in the flexible interconnected microgrid group only needs to exchange the coupling variable states between adjacent microgrids, operating data completely locally, thereby achieving distributed optimization scheduling. During the entire process, there is no need for global information exchange, which not only protects the data privacy of each microgrid, but also reduces the dependence on data transmission hardware. Each microgrid can solve the problem in parallel during each iteration without the need for a coordination center, thus achieving distributed solution, greatly improving the scheduling optimization efficiency and security of the flexible interconnected microgrid group. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a diagram of an application environment of a flexible interconnected microgrid group optimization scheduling method in one embodiment;

[0055] Figure 2 1. A flow chart of a method for optimizing scheduling of a flexible interconnected microgrid group according to an embodiment;

[0056] Figure 3 A schematic flow chart of steps for setting optimization constraints in one embodiment;

[0057] Figure 4 A schematic diagram of linearization of constraint conditions of a flexible interconnect device according to one embodiment;

[0058] Figure 5 1. A flow chart illustrating steps for performing distributed solution processing on sub-optimal scheduling models of two adjacent microgrids in one embodiment;

[0059] Figure 6 A schematic diagram of a distributed solution process in one embodiment;

[0060] Figure 7 A schematic flow chart of a method for optimizing scheduling of a flexible interconnected microgrid group in another embodiment;

[0061] Figure 81 is a flow chart of a method for optimizing scheduling of a flexible interconnected microgrid group in another embodiment;

[0062] Figure 9 This is a structural block diagram of a flexible interconnected microgrid group optimization and scheduling device in one embodiment;

[0063] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0066] The flexible interconnected microgrid group optimization scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, a computer 101 communicates with a microgrid 102 via a network. A data storage system can store data that the computer device 101 needs to process. The data storage system can be integrated on a server, or placed on a cloud or other network server.

[0067] The computer device 101 may be a terminal or a server, or a system including a terminal and a server, and implemented through the interaction between the terminal and the server. The computer device may also be equipped with a microgrid.

[0068] The terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, portable wearable devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers.

[0069] In one embodiment, Figure 2 As shown in the figure, a flexible interconnected microgrid group optimization scheduling method is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0070] Step S201: determining a coupling boundary constraint condition between two adjacent microgrids in a flexible interconnected microgrid group.

[0071] A microgrid is a localized power system capable of autonomously generating, storing, and using electricity. A flexible interconnected microgrid cluster is a system that allows for a high degree of collaboration and scheduling among multiple microgrids. It possesses a high degree of flexibility and controllability, and can dynamically optimize energy distribution and operations based on demand.

[0072] The coupling boundary constraint refers to the condition information used to constrain the variable coupling between adjacent microgrids.

[0073] Specifically, the computer device collects parameter information from each microgrid, including, but not limited to, load power data, network topology, line parameters, and predicted output of renewable energy generation units. Based on the geographic locations of each microgrid, the computer device constructs a flexible interconnected microgrid cluster consisting of multiple microgrids. The computer device also sets coupling boundary constraints between adjacent microgrids to obtain coupling boundary constraints between two adjacent microgrids in the flexible interconnected microgrid cluster.

[0074] Step S202: construct an optimization scheduling model for the flexible interconnected microgrid group based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of the voltage constraints, state of charge constraints, DC area branch flow constraints, DC area tie line transmission capacity constraints, flexible interconnection device constraints, and AC area constraints of the flexible interconnected microgrid group.

[0075] The voltage constraint condition refers to the constraint information that requires the voltage amplitude of all nodes in the microgrid to be maintained within the allowable range.

[0076] Among them, the state of charge constraint condition refers to the constraint information for the energy storage system (such as battery) in the microgrid, which limits its state of charge (the proportion of charge and discharge power) to a reasonable range.

[0077] Among them, the DC regional branch power flow constraint condition refers to the constraint information that limits the transmission power of each branch (such as cables and lines) within the DC power grid to meet physical limits (such as thermal stability limits).

[0078] Among them, the DC regional interconnection line transmission capacity constraint refers to the constraint information that limits the transmission power of DC interconnection lines (such as back-to-back converter stations and DC lines) connecting different AC areas in the flexible interconnected microgrid group to not exceed their rated capacity.

[0079] Among them, the flexible interconnection device constraint conditions refer to the constraint information that limits the operating parameters of flexible interconnection equipment (such as VSC converter stations, unified power flow controllers UPFC, solid-state transformers, etc.).

[0080] Among them, the AC regional constraints refer to the constraint information of the traditional power system that the AC power grid needs to meet.

[0081] Specifically, the computer equipment can set the goal of optimizing the scheduling of the flexible Internet group and then obtain the objective function; it can also set one or more constraints for optimizing the scheduling of the flexible Internet group, such as voltage constraints, state of charge constraints, DC area branch flow constraints, DC area interconnection line transmission capacity constraints, flexible interconnection device constraints and AC area constraints, etc., which together constitute the optimization constraints of the flexible interconnected microgrid group; then, the objective function, optimization constraints and coupling boundary constraints are combined to construct an optimization scheduling model for the flexible interconnected microgrid group.

[0082] In step S203, the optimization scheduling model is decomposed into multiple sub-optimization scheduling models, and distributed solution processing is performed on the sub-optimization scheduling models of two adjacent microgrids to obtain the sub-optimization scheduling results corresponding to the two connected microgrids.

[0083] Among them, the optimization scheduling model refers to a decision-making tool that coordinates distributed power sources, energy storage, loads and interconnected power exchanges within the system through modeling and optimization algorithms, and achieves the optimal operation of a flexible interconnected microgrid group based on objective functions and constraints.

[0084] Specifically, the standard form of the optimization scheduling model of the flexible interconnected microgrid group is as follows:

[0085] minf(x)+g(z)

[0086] stAx+Bz=c

[0087] Where x∈R m ; z∈R q ;A∈R p×m ; B∈R p×q ;c∈R m .

[0088] Computer equipment can use synchronous ADMM (Synchronous Alternating Direction Method of Multipliers) to solve the optimal scheduling model of a flexible interconnected microgrid group. ADMM combines the advantages of the dual decomposition method (Dual Decomposition) and the augmented Lagrangian method (Augmented Lagrangian Method), decomposing the original problem into multiple sub-problems for alternating solution. In order to ensure that the sub-optimization scheduling problem after decomposition of the optimal scheduling model of the flexible interconnected microgrid group is equivalent to the original optimal scheduling problem, the coupling variables involved in solving the adjacent microgrid sub-problems must be consistent. Therefore, the optimization problem of the optimal scheduling model can be decomposed into multiple local sub-optimization problems to obtain multiple sub-optimization scheduling models, and the two adjacent sub-optimization scheduling models can be associated through coupling boundary constraints. For example, one microgrid can correspond to one sub-optimization scheduling model, so that the scheduling optimization of the microgrid can be achieved through the sub-optimization scheduling results of the sub-optimization scheduling model. The sub-optimization scheduling model is as follows:

[0089]

[0090] Where, f i (x i ) represents the objective function of sub-optimal scheduling model i; h i (x i ) represents the equality constraint of sub-optimal scheduling model i; g i (x i ) represents the inequality constraint of sub-optimal scheduling model i; Represents the decision variables in the optimization scheduling model.

[0091] The synchronous ADMM ensures that the global constraints are met by alternately updating the optimization variables and Lagrange multipliers of each sub-problem, and finally solves the optimal scheduling plan for the two connected microgrids. The computer equipment then obtains the sub-optimal scheduling results corresponding to the two connected microgrids.

[0092] Step S204: obtaining an optimized scheduling result of the flexible interconnected microgrid group based on the sub-optimal scheduling results of each microgrid.

[0093] Specifically, the computer device can combine the sub-optimization scheduling results corresponding to all microgrids and output the overall optimization scheduling result of the flexible interconnected microgrid group.

[0094] In the above-mentioned flexible interconnected microgrid group optimization scheduling method, coupling boundary constraints between two adjacent microgrids in the flexible interconnected microgrid group are determined; an optimization scheduling model for the flexible interconnected microgrid group is constructed based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of the flexible interconnected microgrid group's voltage constraints, state of charge constraints, DC region branch flow constraints, DC region tie line transmission capacity constraints, flexible interconnection device constraints, and AC region constraints; the optimization scheduling model is decomposed into multiple sub-optimization scheduling models, and the sub-optimization scheduling models of two adjacent microgrids are solved in a distributed manner to obtain sub-optimization scheduling results corresponding to the two connected microgrids; and the optimization scheduling results of the flexible interconnected microgrid group are obtained based on the sub-optimization scheduling results of each microgrid. Using this method, each microgrid in the flexible interconnected microgrid group only needs to exchange the coupling variable states between adjacent microgrids, operating data completely locally, thereby achieving distributed optimization scheduling. During the entire process, there is no need for global information exchange, which not only protects the data privacy of each microgrid, but also reduces the dependence on data transmission hardware. Each microgrid can solve the problem in parallel during each iteration without the need for a coordination center, thus achieving distributed solution, greatly improving the scheduling optimization efficiency and security of the flexible interconnected microgrid group.

[0095] In one embodiment, the above step S201, determining the coupling boundary constraint condition between two adjacent microgrids in the flexible interconnected microgrid group, specifically includes the following contents: obtaining the coupling boundary variable between each microgrid and its adjacent microgrid based on the DC bus voltage of each microgrid in the flexible interconnected microgrid group and the DC active power between each microgrid and its adjacent microgrid; and obtaining the coupling boundary constraint condition between the two adjacent microgrids based on the coupling boundary variable.

[0096] Specifically, the computer device can set the coupling boundary variables between each microgrid and its adjacent microgrids based on the DC bus voltage of each microgrid in the flexible interconnected microgrid group and the DC active power transmitted between each microgrid and its adjacent microgrids; and then the computer device can set the coupling boundary variables as the coupling boundary constraint conditions between two adjacent microgrids.

[0097] In practical applications, the coupled boundary variables can be expressed by the following formula:

[0098]

[0099] Where, represents the coupling boundary variable between microgrids i and j; represents the active power exchange value transferred between microgrid i and microgrid j determined during the local optimization process; represents the active power exchange value transmitted between microgrid j and microgrid i determined during the local optimization process; U ii 、U ij are the DC bus voltage of microgrid i and the DC bus voltage of microgrid j determined during the local optimization process of microgrid i, respectively; is the fixed reference value between microgrids i and j at the k+1th iteration, and is taken as the average value of the coupling boundary variables obtained at the tth iteration between microgrids i and j.

[0100] In this embodiment, the coupling boundary variable between two adjacent microgrids is obtained by using the DC bus voltage of each microgrid and the DC active power between each microgrid and its adjacent microgrid. The coupling boundary variable is then set as the coupling boundary constraint condition between the two adjacent microgrids. This effectively obtains the coupling boundary constraint condition and provides the most core processing basis for the subsequent optimized scheduling of the flexible interconnected microgrid group.

[0101] In one embodiment, Figure 3 As shown, in the above step S202, before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the following is also included:

[0102] Step S301: obtaining a voltage constraint condition according to an upper voltage limit and a lower voltage limit of a DC bus voltage of the flexible interconnected microgrid group.

[0103] Specifically, the computer device can ensure that the DC bus voltage of the microgrid does not exceed the voltage upper limit (i.e., maximum value) and the voltage lower limit (i.e., minimum value) by setting the voltage constraint condition. The voltage constraint condition can be expressed as the following formula:

[0104] U min ≤U i,t ≤U max

[0105] Where U min 、U max They are the lower and upper voltage limits allowed by the DC bus voltage of the microgrid; U i,t represents the DC bus voltage of microgrid i.

[0106] Step S302 , obtaining a state of charge constraint condition according to the state of charge upper limit value and the state of charge lower limit value of the microgrid; the state of charge upper limit value and the state of charge lower limit value are obtained according to the energy storage power of the microgrid.

[0107] It should be noted that the State of Charge (SOC) constraint is crucial to ensuring the long-term health of energy storage devices and system stability. Excessively low or high SOC can damage energy storage devices. Therefore, it is necessary to comprehensively consider the charge and discharge power limits of energy storage power and the reasonable range of SOC.

[0108] Specifically, the computer can obtain the upper and lower limits of the state of charge based on the energy storage power of the microgrid; and then set the state of charge constraint conditions based on the upper and lower limits of the state of charge of the microgrid. The state of charge constraint conditions can be expressed as the following formula:

[0109] SOC min ≤SOC i,t ≤SOC max

[0110]

[0111] SOC i,0 =SOC i,24

[0112]

[0113]

[0114] Where, SOC i,t is the state of charge of the internal energy storage of microgrid i at time t; SOC max , SOC min are the maximum and minimum values of the energy storage charge state respectively; η ch ,η dis are energy storage charging and discharging efficiency; E BES,i represents the rated capacity of the internal energy storage of microgrid i; are binary variables, When it is 1, it means the energy storage is in charging state. When it is 1, it means that the energy storage is in the discharge state; They represent the maximum power of energy storage charging and discharging respectively.

[0115] Furthermore, the computer device can set energy storage power constraints to limit the upper and lower limits of the charging and discharging power of the energy storage device, ensuring that the energy storage device operates within a safe range and preventing overcharging or over-discharging.

[0116] Step S303 : obtaining the DC area branch power flow constraint conditions based on the active power balance information of the microgrid and the voltage relationship information between two adjacent microgrids.

[0117] In practical applications, for flexible interconnected microgrids, the DC regional branch power flow constraints can be expressed by the following power flow equation:

[0118]

[0119] In the formula, the first formula in the DC area branch flow constraint condition represents the active power balance of microgrid i, and the second formula in the DC area branch flow constraint condition represents the voltage relationship between adjacent microgrids. i represents the active power injection of microgrid i; U i Represent the voltage of the parent node and child node i respectively; is the DC load of microgrid i; Predict the photovoltaic output of microgrid i; is the DC output power of the flexible interconnected device on microgrid i, and is stipulated to be a positive value from the AC area to the DC area.

[0120] Due to the non-convex nature of the original branch power flow constraints in the first and second formulas of the DC region branch power flow constraints, it is not feasible to directly apply convex optimization methods. To effectively solve the problem, the higher-order terms in the equation are ignored and a linear approximation is performed, resulting in the following linear branch power flow constraints:

[0121]

[0122]

[0123] When constructing the model subsequently, linear branch flow constraints can be used to replace DC area branch flow constraints for solution.

[0124] Step S304: obtaining the transmission capacity constraint condition of the DC regional tie line according to the maximum transmission power of the tributary line of the flexible interconnected microgrid group.

[0125] Specifically, computer equipment can set transmission capacity constraints on DC regional interconnection lines to specify the maximum active power (i.e., maximum transmission power) allowed to be transmitted by tributary lines at any time, thereby preventing equipment damage and system stability degradation caused by interconnection line overload.

[0126]

[0127] Where, Indicates the maximum transmission power allowed by the DC area tie line.

[0128] Step S305 : obtaining flexible interconnected device constraint conditions according to the active power and reactive power of the flexible interconnected devices of the flexible interconnected microgrid group.

[0129] The flexible interconnection device constraint condition refers to the constraint information on the capacity of the flexible interconnection device during operation of the flexible interconnection device.

[0130] Specifically, during the operation of the flexible interconnection device, the port power must meet the capacity constraint to avoid damage to the device due to overload and ensure that the device can improve stable power transmission and regulation functions within its safe operating range. The flexible interconnection device constraint can be expressed by the following formula:

[0131]

[0132] Where, are respectively the active power and reactive power flowing into or out of the AC port of the flexible interconnection device, and the inflow power is specified to be a positive value; The capacity of the flexible interconnected device configured for microgrid i. The flexible interconnected device constraint ensures that the power of the flexible interconnected device does not exceed the limit.

[0133] Figure 4 This is a schematic diagram of the linearization of the constraints of the flexible interconnection device. To facilitate the solution, the constraints of the flexible interconnection device are linearized as shown in the following formula:

[0134]

[0135] Assuming that the transmission loss of the flexible interconnect device is ignored, the following formula can be obtained:

[0136]

[0137] Where, The active power flowing into or out of the DC port of the flexible interconnection device. The outflow power is specified as a positive value.

[0138] Step S306: Obtain AC area constraints based on the active power and reactive power on the low-voltage side of the transformer in the flexible interconnected microgrid group.

[0139] Among them, the AC area constraints are used to ensure that the power supply and demand between each node in the AC area are equal, ensuring the actual power flow load demand of each part of the system.

[0140] Specifically, the AC area constraints can be expressed by the active power and reactive power on the low-voltage side of the transformer:

[0141]

[0142] Where, P i,t , Q i,t are the active power and reactive power on the low-voltage side of the transformer, respectively.

[0143] In addition, computer equipment can also set transformer capacity constraints based on the active and reactive power on the low-voltage side of the transformer. Transformer capacity constraints limit the maximum amount of power a transformer can handle during power transmission, ensuring that the transformer does not exceed its rated capacity and ensuring system stability and reliability. Transformer capacity constraints can be expressed as the following formula:

[0144]

[0145] Where, is the rated capacity of the transformer.

[0146] Step S307 : setting voltage constraints, state of charge constraints, energy storage power constraints, linear branch power flow constraints, DC area tie line transmission capacity constraints, flexible interconnection device constraints, and AC area constraints as optimization constraints.

[0147] Specifically, the computer equipment can integrate voltage constraints, state of charge constraints, DC area branch flow constraints, DC area interconnection line transmission capacity constraints, flexible interconnection device constraints, AC area constraints and transformer capacity constraints to obtain the optimization constraints of the flexible interconnected microgrid group.

[0148] In this embodiment, by comprehensively setting the optimization constraints of the flexible interconnected microgrid group based on multiple constraint information such as voltage constraints, state of charge constraints, energy storage power constraints, linear branch flow constraints, DC area tie line transmission capacity constraints, flexible interconnection device constraints, and AC area constraints, the constraints in the optimization process can be considered more comprehensively, thereby improving the optimization scheduling effect of the flexible interconnected microgrid group.

[0149] In one embodiment, in one embodiment, as Figure 3 As shown, in the above step S202, before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the following further includes: obtaining the total operating cost of the flexible interconnected microgrid group according to the charging and discharging power of the microgrids in the flexible interconnected microgrid group, the power sold to the superior power grid, and the transmission and exchange power between two adjacent microgrids; and minimizing the total operating cost to obtain the objective function of the flexible interconnected microgrid group.

[0150] Among them, the charging and discharging power refers to the power input (charging) or output (discharging) value of the energy storage system (such as batteries and supercapacitors) inside the microgrid.

[0151] Among them, the power sold to the upper-level power grid refers to the net active power transmitted by the microgrid to the upper-level large power grid (such as the distribution network or transmission network) through the public connection point (PCC).

[0152] Among them, the transmission exchange power refers to the active power transmitted between two adjacent microgrids through flexible interconnection devices (such as DC / AC converters and DC buses).

[0153] Specifically, the computer device can first calculate the operating cost of each microgrid based on its charging and discharging power, the power sold to the higher-level grid, the transmission and exchange power between adjacent microgrids, and the penalty coefficient for tie-line transmission losses. Then, by summing the operating costs of all microgrids in the flexible interconnected microgrid group, the total operating cost of the flexible interconnected microgrid group can be obtained. For the flexible interconnected microgrid group, the goal can be to minimize the total operating cost of the system microgrid group. The computer device can then determine the objective function for the flexible interconnected microgrid group.

[0154] In practical applications, the operating costs of a single microgrid are mainly reflected in the cost of energy storage charging and discharging, the cost of purchasing and selling electricity to higher-level suppliers, and the cost of exchange power loss. The operating costs of a single microgrid can be calculated using the following formula:

[0155]

[0156] Where C soc Represents the operation and maintenance cost coefficient of energy storage charging and discharging; C bs Indicates the price of electricity purchased or sold from the superior power grid; C loss represents the penalty coefficient of tie line transmission loss; r ij Represents the resistance of the tie line between microgrids ij.

[0157] The objective function of the flexible interconnected microgrid group can be expressed by the following formula:

[0158]

[0159] Where: are the charging and discharging power of microgrid i; P i,t Indicates the power purchased from the upper-level power grid. A positive value indicates that power is purchased from the upper-level power grid, and a negative value indicates that power is sold to the upper-level power grid. represents the exchange power transmitted between microgrids ij.

[0160] In this embodiment, the operating cost of a single microgrid is obtained by the charging and discharging power of the microgrid, the power sold to the superior power grid, and the transmission and exchange power between two adjacent microgrids. Then, the total operating cost of the flexible interconnected microgrid group is calculated by combining the operating costs of all microgrids. The minimization of the total operating cost is set as the objective function of the flexible interconnected microgrid group, which realizes the reasonable acquisition of the objective function and lays the foundation for the subsequent optimal scheduling of the flexible interconnected microgrid group.

[0161] In one embodiment, Figure 5 As shown, in the above step S203, the optimization scheduling model is decomposed into multiple sub-optimization scheduling models, and the sub-optimization scheduling models of two adjacent microgrids are subjected to distributed solution processing to obtain the sub-optimization scheduling results corresponding to the two connected microgrids, which specifically include the following contents:

[0162] Step S501: obtaining the sub-objective function of each microgrid based on the dual variables of each microgrid.

[0163] Figure 6 The figure is a flow chart of the distributed solution. Specifically, each microgrid controller establishes the regional liberalization problem. The augmented Lagrangian function corresponding to the objective function of the sub-optimal scheduling model of each microgrid is:

[0164]

[0165] Where k is the number of iterations; is the dual variable of microgrid i; ρ is the penalty parameter of ADMM algorithm.

[0166] Step S502 : Based on the sub-objective function, the sub-optimal scheduling models of the two adjacent microgrids are solved in parallel to obtain the decision variables corresponding to the two adjacent microgrids.

[0167] Specifically, at the k+1th iteration, the two adjacent microgrids solve the sub-optimal scheduling model in their respective regions and solve the decision variables in the regions in parallel. The decision variables minimize the augmented Lagrangian function. The solution of the decision variables can be expressed by the following formula:

[0168]

[0169] Step S503: updating the dual variables of the two adjacent microgrids according to the decision variables corresponding to the two adjacent microgrids.

[0170] Specifically, based on the decision variables obtained in step S502, the two adjacent microgrids update the dual variables of their respective regions. The dual variable update process is shown in the following formula:

[0171]

[0172] In step S504, if it is detected that each microgrid meets the preset convergence condition, the sub-optimal scheduling results corresponding to the two connected microgrids are obtained based on the decision variables.

[0173] Specifically, each microgrid calculates the residual and determines whether it has converged according to the following formula. The criterion for convergence of the algorithm iteration is: if each microgrid and its adjacent microgrids meet the convergence requirements, the iteration stops and the optimization result is obtained; otherwise, the iteration continues.

[0174]

[0175] Where, δ pri , δ dual are the convergence errors corresponding to the primal and dual residuals, respectively.

[0176] Step S505 : If it is detected that each microgrid does not meet the preset convergence condition, the process jumps to the step of obtaining the sub-objective function of each microgrid based on the dual variables of each microgrid.

[0177] Specifically, if it is detected that each microgrid does not meet the convergence requirement, the process proceeds to step S501 to continue iteration.

[0178] In this embodiment, when solving sub-optimal scheduling between two adjacent microgrids, both perform iterative calculations through information exchange. The information exchanged only involves the states of the coupled variables between the adjacent regions, relying entirely on local operating data. This enables distributed optimization scheduling. Throughout this process, no global information exchange is required, protecting the data privacy of each region and reducing reliance on data transmission hardware. Each region can solve the problem in parallel during each iteration, eliminating the need for a coordination center, thus achieving distributed solution.

[0179] In one embodiment, before determining the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group in step S201, the method further includes: obtaining a plurality of geographically adjacent microgrids; and connecting the plurality of microgrids through a flexible interconnection method to obtain a flexible interconnected microgrid group.

[0180] Specifically, computer equipment can also obtain the geographical location information of the microgrid (such as coordinate location); determine multiple microgrids with adjacent geographical locations, and then connect the multiple microgrids with adjacent geographical locations through flexible interconnection technology to form a microgrid group, thereby obtaining a flexible interconnected microgrid group. Then, the microgrid can use the flexible interconnected microgrid group to carry out energy coordination and collaborative optimization, thereby improving its own reliability, economy and renewable energy absorption capacity.

[0181] In this embodiment, through the autonomy of each microgrid and the coordination and complementarity between adjacent microgrids, the operational reliability and economy of the microgrid group are improved, the absorption capacity of renewable energy is enhanced, and the privacy and independence of each microgrid are protected, providing technical support for the efficient, safe and reliable operation of the microgrid group.

[0182] In one embodiment, Figure 7 As shown in the figure, another flexible interconnected microgrid group optimization scheduling method is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0183] Step S701 : obtaining coupling boundary variables between each microgrid and its adjacent microgrids based on the DC bus voltage of each microgrid in the flexible interconnected microgrid group and the DC active power between each microgrid and its adjacent microgrids.

[0184] Step S702: obtaining coupling boundary constraint conditions between two adjacent microgrids based on coupling boundary variables.

[0185] Step S703: Construct an optimization scheduling model for the flexible interconnected microgrid group based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of the voltage constraints, state of charge constraints, DC area branch flow constraints, DC area tie line transmission capacity constraints, flexible interconnection device constraints, and AC area constraints of the flexible interconnected microgrid group.

[0186] Step S704: decompose the optimization scheduling model into multiple sub-optimization scheduling models.

[0187] Step S705: obtaining the sub-objective function of each microgrid based on the dual variables of each microgrid.

[0188] Step S706 : Based on the sub-objective function, the sub-optimal scheduling models of the two adjacent microgrids are solved in parallel to obtain the decision variables corresponding to the two adjacent microgrids.

[0189] Step S707: updating the dual variables of the two adjacent microgrids according to the decision variables corresponding to the two adjacent microgrids.

[0190] It is determined whether each microgrid meets the preset convergence condition. If so, step S708 is executed; if not, the process jumps to the above step S705.

[0191] Step S708: Based on the decision variables, obtain the sub-optimal scheduling results corresponding to the two connected microgrids.

[0192] Step S709: Obtain the optimized scheduling result of the flexible interconnected microgrid group based on the sub-optimal scheduling results of each microgrid.

[0193] The above-described flexible interconnected microgrid cluster optimization and scheduling method achieves the following beneficial effects: Each microgrid in the cluster only needs to exchange the states of coupled variables between adjacent microgrids, operating data completely locally, thereby achieving distributed optimization and scheduling. Throughout the entire process, no global information exchange is required, which protects the data privacy of each microgrid and reduces reliance on data transmission hardware. Each microgrid can solve the problem in parallel during each iteration, eliminating the need for a coordination center. This enables distributed solution, significantly improving the efficiency and security of the flexible interconnected microgrid cluster's scheduling optimization.

[0194] In order to more clearly illustrate the flexible interconnected microgrid group optimization scheduling method provided by the embodiment of the present disclosure, the flexible interconnected microgrid group optimization scheduling method is specifically described below with a specific embodiment. Figure 8 As shown in the figure, a flexible interconnected microgrid group optimization scheduling method is provided, which can be applied to Figure 1 Computer equipment in the system, including the following:

[0195] Step 1: Obtain parameter information such as microgrid network topology, line parameters, load power, and predicted output of renewable energy generation units;

[0196] Step 2: Decoupling of the flexible interconnected microgrid group and selection of boundary constraint variables;

[0197] Step 3: Build an optimization dispatch model for flexible interconnected microgrid groups;

[0198] Step 4: Use the optimization method based on the synchronous alternating direction multiplier method (ADMM) to iteratively solve the problem.

[0199] In this embodiment, through the autonomy of each microgrid and the coordination and complementarity between adjacent microgrids, the operational reliability and economy of the microgrid group are improved, the absorption capacity of renewable energy is enhanced, and the privacy and independence of each microgrid are protected, providing technical support for the efficient, safe and reliable operation of the microgrid group.

[0200] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0201] Based on the same inventive concept, embodiments of the present application also provide a flexible interconnected microgrid group optimization and scheduling device for implementing the aforementioned flexible interconnected microgrid group optimization and scheduling method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the flexible interconnected microgrid group optimization and scheduling device provided below can be found in the above-mentioned limitations of the flexible interconnected microgrid group optimization and scheduling method, and will not be repeated here.

[0202] In one embodiment, Figure 9 As shown, a flexible interconnected microgrid group optimization scheduling device 900 is provided, including: a constraint determination module 901, a model construction module 902, a model solution module 903 and a result determination module 904, wherein:

[0203] The constraint determination module 901 is used to determine the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group.

[0204] The model construction module 902 is used to construct an optimization scheduling model of the flexible interconnected microgrid group based on the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of the voltage constraints, state of charge constraints, DC area branch flow constraints, DC area tie line transmission capacity constraints, flexible interconnection device constraints and AC area constraints of the flexible interconnected microgrid group.

[0205] The model solving module 903 is used to decompose the optimization scheduling model into multiple sub-optimization scheduling models, perform distributed solution processing on two adjacent sub-optimization scheduling models, and obtain sub-optimization scheduling results corresponding to the two adjacent sub-optimization scheduling models.

[0206] The result determination module 904 is used to obtain the optimized scheduling result of the flexible interconnected microgrid group according to the sub-optimal scheduling results of each of the sub-optimal scheduling models.

[0207] In one embodiment, the constraint determination module 901 is further used to obtain coupling boundary variables between each microgrid and its adjacent microgrids based on the DC bus voltage of each microgrid in the flexible interconnected microgrid group and the DC active power between each microgrid and its adjacent microgrid; and obtain coupling boundary constraint conditions between two adjacent microgrids based on the coupling boundary variables.

[0208] In one embodiment, the flexible interconnected microgrid group optimization and scheduling device 900 also includes an optimization constraint setting module, which is used to obtain the voltage constraint condition according to the voltage upper limit value and the voltage lower limit value of the DC bus voltage of the flexible interconnected microgrid group; obtain the state of charge constraint condition according to the state of charge upper limit value and the state of charge lower limit value of the microgrid; the state of charge upper limit value and the state of charge lower limit value are obtained according to the energy storage power of the microgrid; obtain the DC regional branch flow constraint condition according to the active power balance information of the microgrid and the voltage relationship information between two adjacent microgrids; obtain the DC regional branch flow constraint condition according to the flexible interconnected microgrid group. The maximum transmission power of the tributary line of the flexible interconnected microgrid group is used to obtain the transmission capacity constraint of the DC area tie line; the flexible interconnection device constraint is obtained according to the active power and reactive power of the flexible interconnection device of the flexible interconnected microgrid group; the AC area constraint is obtained according to the active power and reactive power of the low-voltage side of the transformer of the flexible interconnected microgrid group; the voltage constraint, the state of charge constraint, the DC area branch flow constraint, the DC area tie line transmission capacity constraint, the flexible interconnection device constraint and the AC area constraint are set as the optimization constraint.

[0209] In one embodiment, the flexible interconnected microgrid group optimization and scheduling device 900 also includes an objective function determination module, which is used to obtain the total operating cost of the flexible interconnected microgrid group based on the charging and discharging power of the microgrids in the flexible interconnected microgrid group, the power sold to the superior power grid, and the transmission and exchange power between two adjacent microgrids; and minimize the total operating cost to obtain the objective function of the flexible interconnected microgrid group.

[0210] In one embodiment, the model solving module 903 is further used to obtain the sub-objective function of each microgrid based on the dual variables of each microgrid; based on the sub-objective function, the sub-optimization scheduling model of the two adjacent microgrids is solved in parallel to obtain the decision variables corresponding to the two adjacent microgrids; according to the decision variables corresponding to the two adjacent microgrids, the dual variables of the two adjacent microgrids are updated; if it is detected that each microgrid meets the preset convergence condition, the sub-optimization scheduling results corresponding to the two connected microgrids are obtained based on the decision variables; if it is detected that each microgrid does not meet the preset convergence condition, the step of jumping to the step of obtaining the sub-objective function of each microgrid based on the dual variables of each microgrid is performed.

[0211] In one embodiment, the flexible interconnected microgrid group optimization and scheduling device 900 further includes a microgrid connection module for obtaining a plurality of geographically adjacent microgrids; and connecting the plurality of microgrids in a flexible interconnection manner to obtain the flexible interconnected microgrid group.

[0212] Each module in the flexible interconnected microgrid cluster optimization and scheduling device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0213] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as objective functions, optimization constraints, coupling boundary constraints, and optimization scheduling models. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a flexible interconnected microgrid group optimization scheduling method is implemented.

[0214] Those skilled in the art will understand that Figure 10The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0215] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0216] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0217] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0218] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0219] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0220] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A flexible interconnected microgrid group optimization scheduling method, characterized in that: The method comprises: Determine the coupling boundary constraints between two adjacent microgrids in a flexible interconnected microgrid group; An optimization scheduling model for the flexible interconnected microgrid group is constructed according to the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints include at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group; Decomposing the optimization scheduling model into multiple sub-optimization scheduling models, performing distributed solution processing on the sub-optimization scheduling models of two adjacent microgrids, and obtaining sub-optimization scheduling results corresponding to the two adjacent microgrids; According to the sub-optimal scheduling results of each microgrid, the optimized scheduling result of the flexible interconnected microgrid group is obtained.

2. The method according to claim 1, characterized in that The step of determining the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group includes: Obtaining coupling boundary variables between each microgrid and its adjacent microgrids based on a DC bus voltage of each microgrid in the flexible interconnected microgrid group and a DC active power between each microgrid and its adjacent microgrids; Based on the coupling boundary variables, a coupling boundary constraint condition between two adjacent microgrids is obtained.

3. The method according to claim 1, characterized in that Before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the method further includes: Obtaining the voltage constraint condition according to an upper voltage limit and a lower voltage limit of the DC bus voltage of the flexible interconnected microgrid group; Obtaining the state of charge constraint condition according to the state of charge upper limit value and the state of charge lower limit value of the microgrid; the state of charge upper limit value and the state of charge lower limit value are obtained according to the energy storage power of the microgrid; Obtaining the DC regional branch power flow constraint condition according to the active power balance information of the microgrid and the voltage relationship information between two adjacent microgrids; Obtaining a transmission capacity constraint condition of the DC regional tie line according to the maximum transmission power of the tributary line of the flexible interconnected microgrid group; Obtaining a constraint condition of the flexible interconnected device according to the active power and reactive power of the flexible interconnected device of the flexible interconnected microgrid group; Obtaining the AC area constraint condition according to the active power and reactive power on the low-voltage side of the transformer of the flexible interconnected microgrid group; The voltage constraint condition, the state of charge constraint condition, the DC area branch power flow constraint condition, the DC area tie line transmission capacity constraint condition, the flexible interconnection device constraint condition and the AC area constraint condition are set as the optimization constraint conditions.

4. The method according to claim 1, wherein Before constructing the optimization scheduling model of the flexible interconnected microgrid group according to the objective function, optimization constraints and coupling boundary constraints of the flexible interconnected microgrid group, the method further includes: Obtaining a total operating cost of the flexible interconnected microgrid group based on the charge and discharge power of the microgrids in the flexible interconnected microgrid group, the power sold to the upper power grid, and the transmission and exchange power between two adjacent microgrids; The total operating cost is minimized to obtain an objective function of the flexible interconnected microgrid group.

5. The method according to claim 1, wherein The distributed solution processing of the sub-optimal scheduling models of the two adjacent microgrids to obtain the sub-optimal scheduling results corresponding to the two connected microgrids includes: Based on the dual variables of each microgrid, a sub-objective function of each microgrid is obtained; Based on the sub-objective function, the sub-optimal scheduling models of the two adjacent microgrids are solved in parallel to obtain the decision variables corresponding to the two adjacent microgrids respectively; updating the dual variables of the two adjacent microgrids according to the decision variables corresponding to the two adjacent microgrids; If it is detected that each of the microgrids meets the preset convergence condition, then based on the decision variables, sub-optimal scheduling results corresponding to the two connected microgrids are obtained; If it is detected that each of the microgrids does not meet the preset convergence condition, the process jumps to the step of obtaining the sub-objective function of each of the microgrids based on the dual variables of each of the microgrids.

6. The method according to claim 1, characterized in that Before determining the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group, the following steps are also included: Acquire multiple microgrids that are geographically adjacent; The multiple microgrids are connected in a flexible interconnection manner to obtain the flexible interconnected microgrid group.

7. A flexible interconnected microgrid group optimization scheduling device, characterized in that: The device comprises: A constraint determination module is used to determine the coupling boundary constraint conditions between two adjacent microgrids in the flexible interconnected microgrid group; a model construction module, configured to construct an optimization scheduling model for the flexible interconnected microgrid group based on the objective function, optimization constraints, and coupling boundary constraints of the flexible interconnected microgrid group; the optimization constraints comprising at least one of a voltage constraint, a state of charge constraint, a DC region branch flow constraint, a DC region tie line transmission capacity constraint, a flexible interconnection device constraint, and an AC region constraint of the flexible interconnected microgrid group; A model solving module is used to decompose the optimization scheduling model into multiple sub-optimization scheduling models, perform distributed solution processing on two adjacent sub-optimization scheduling models, and obtain sub-optimization scheduling results corresponding to the two adjacent sub-optimization scheduling models; The result determination module is used to obtain the optimized scheduling result of the flexible interconnected microgrid group according to the sub-optimal scheduling results of each sub-optimal scheduling model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images