Unified distributed dynamic optimization control method and device for microgrid in autonomous transformer area

Abstract

The invention relates to a unified distributed dynamic optimization control method and device for a micro-grid in an autonomous station area, and the method comprises the steps: embedding a frequency regulator at a virtual synchronous converter, estimating the average frequency of the autonomous micro-grid through the frequency regulator, comparing the average frequency with a rated frequency to generate an error term, and calculating the optimal value of the error term; inputting the error term into an integrator to obtain a first power correction term; embedding a benefit optimizer at the virtual synchronous converter, generating a neighborhood error through the benefit optimizer, and inputting the neighborhood error into an integrator to obtain a second power correction term; and adding the first power correction item and the second power correction item with a reference power item to obtain a power setting item, and taking the power setting item as the input of the virtual synchronous converter. According to the invention, stable voltage and frequency support can be provided, and global optimal control is realized.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization control technology, and in particular to a unified distributed dynamic optimization control method and device for autonomous distribution area microgrids. Background Technology

[0002] With the development of microgrid and active distribution network technologies, more autonomous distribution microgrids with distributed generation will emerge in the future distribution network. Autonomous microgrids generally consist of distributed generation, energy storage systems, etc. Distributed generation (including fuel cells, micro gas turbines, diesel engines, etc.) provides stable voltage and frequency for the system. The system operates in master-slave or peer-to-peer mode, and achieves stable, economical, and reliable operation through optimized control between "source, storage, and load" within the distribution microgrid.

[0003] Existing optimization control methods for autonomous microgrids are mostly centralized, peer-to-peer, and voltage and frequency control based on multi-agent systems. However, all three methods have certain drawbacks. Centralized control relies heavily on the microgrid central controller (MGCC) and communication network. If the MGCC or communication fails, the system will have difficulty maintaining normal operation. Due to real-time requirements, distributed control based on multi-agent systems is still difficult to adjust voltage and frequency in real time. Peer-to-peer control has low communication requirements and can theoretically improve the reliability of the microgrid, reduce system costs, and achieve plug-and-play functionality. However, this method is difficult to make a comprehensive global response in the first instance, resulting in long transient transition times and reduced system stability, rationality, and economy.

[0004] In addition to ensuring the stable operation of autonomous microgrids, reasonable power allocation also needs to be considered. Reasonable power allocation can effectively improve the utilization rate of renewable energy, reduce system operating costs, and enhance overall efficiency. Currently, power optimization in autonomous microgrid systems mainly relies on energy management systems (EMS), employing a centralized dispatching and distributed control approach. This consists of an upper-level central energy management system and lower-level distributed power sources, loads, and other local device controllers, requiring bidirectional communication between the two layers. Traditional energy optimization methods cannot achieve real-time, flexible control, lack "plug-and-play" functionality, and suffer from poor reliability and scalability. Furthermore, the integration of microgrid operation control and energy management is not tight enough. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a unified distributed dynamic optimization control method and device for autonomous microgrids, which can provide stable voltage and frequency support and achieve global optimal control.

[0006] The technical solution adopted by this invention to solve its technical problem is: to provide a unified distributed dynamic optimization control method for autonomous microgrids, comprising the following steps:

[0007] A frequency regulator is embedded in the virtual synchronous converter. The average frequency of the autonomous microgrid is estimated by the frequency regulator, and the average frequency is compared with the rated frequency to generate an error term. The error term is input into an integrator to obtain the first power correction term.

[0008] A benefit optimizer is embedded in the virtual synchronous converter. The benefit optimizer generates a neighborhood error and inputs the neighborhood error into an integrator to obtain a second power correction term.

[0009] The first power correction term and the second power correction term are added to the reference power term to obtain the power setting term, and the power setting term is used as the input of the virtual synchronous converter.

[0010] The frequency regulator uses a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid.

[0011] The dynamic consensus protocol is represented as follows: in, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0012] The benefit optimizer generates neighborhood error based on local incremental cost information and the incremental cost information of its neighbors.

[0013] The generation of neighborhood error based on local incremental cost information and its neighboring incremental cost information specifically involves: Where, δλ i (t) represents the neighborhood error, a ij λ is an element of the communication topology adjacency matrix. i (t) represents the incremental cost of the distributed power source corresponding to the virtual synchronous converter i, N i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0014] The technical solution adopted by this invention to solve its technical problem is: to provide a unified distributed dynamic optimization control device for autonomous microgrids, comprising:

[0015] The first power correction module is used to embed a frequency regulator at the virtual synchronous converter, estimate the average frequency of the autonomous microgrid through the frequency regulator, compare the average frequency with the rated frequency to generate an error term, and input the error term into an integrator to obtain the first power correction term.

[0016] The second power correction module is used to embed a benefit optimizer at the virtual synchronous converter, generate a neighborhood error through the benefit optimizer, and input the neighborhood error into an integrator to obtain a second power correction term;

[0017] The control module is used to add the first power correction term and the second power correction term to the reference power term to obtain a power setting term, and use the power setting term as the input of the virtual synchronous converter.

[0018] The frequency regulator employs a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid, including an average frequency estimation unit. Estimate the average frequency of the autonomous microgrid, where, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0019] The benefit optimizer generates neighborhood error based on local incremental cost information and its neighboring incremental cost information, including an incremental cost error calculation unit, and employs... Generate neighborhood error, where δλ i (t) represents the neighborhood error, a ij λ is an element of the communication topology adjacency matrix. i (t) represents the incremental cost of the distributed power source corresponding to the virtual synchronous converter i, N i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0020] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned unified distributed dynamic optimization control method for autonomous microgrids.

[0021] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-mentioned unified distributed dynamic optimization control method for autonomous microgrids are implemented.

[0022] Beneficial effects

[0023] Due to the adoption of the above-mentioned technical solutions, this invention has the following advantages and positive effects compared with the prior art: In terms of frequency control, this invention uses a distributed dynamic consensus algorithm to estimate the average frequency of the system by embedding a frequency regulator, and then corrects the frequency deviation caused by the virtual synchronous machine control without oscillation, so as to solve the problem of power distribution accuracy and voltage oscillation caused by local measurement errors; In terms of benefit optimization, by embedding a benefit optimizer, the output power of different devices is adjusted with the goal of maximizing social benefits, so as to ensure the optimal operation of the microgrid and thus achieve global optimal control. Attached Figure Description

[0024] Figure 1 This is a framework diagram of the unified distributed dynamic optimization control method for autonomous microgrids according to the first embodiment of the present invention;

[0025] Figure 2 This is a flowchart of the unified distributed dynamic optimization control method for autonomous microgrids according to the first embodiment of the present invention. Detailed Implementation

[0026] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0027] The first embodiment of the present invention relates to a unified distributed dynamic optimization control method for autonomous microgrids, such as... Figure 1 As shown, this method models the communication topology of each device in the microgrid and constructs a coordinated control framework based on dynamic consistency. In terms of frequency control, a frequency regulator is embedded, and a distributed dynamic consistency algorithm is used to estimate the average frequency of the system. Then, the frequency deviation caused by the virtual synchronous machine control is corrected without oscillation. In terms of benefit optimization, a benefit optimizer is embedded, and the output power of different devices is adjusted with the goal of maximizing social benefits to ensure the optimal operation of the microgrid.

[0028] To achieve distributed control and optimization of the microgrid, this embodiment models the communication topology between microgrid controllers. The topology model is represented by a graph, denoted as Graph G. Graph G consists of two sets, (V, E), where V is a finite, non-empty set of nodes, and E is a set of edges representing the controllers and links, respectively. Node v i The set of all neighboring nodes is represented as N i , where all nodes v i Directly connected nodes all belong to N. i In a simple graph, the adjacency matrix A = [a ij ] is a matrix with zero diagonal elements. If node j∈N i Then the off-diagonal element a ij The value is 1. On the other hand, the diagonal in-degree matrix is ​​D = diag(d ii ), where d ii Represents node v i The in-degree is defined as the sum of the elements in the i-th row of the adjacency matrix:

[0029]

[0030] Define a Laplace matrix L = DA, where the sum of all its row elements is zero. Node v i The out-degree is defined as the sum of the elements in the i-th column of the adjacency matrix. If the graph is undirected, then the in-degree and out-degree of each node are equal, and in this case, the adjacency matrix A and the Laplace matrix L are symmetric.

[0031] Secondly, based on the distributed dynamic consensus algorithm, a distributed dynamic optimization and control framework is constructed for estimating the global average value among nodes and for optimal control. Let x i Let represent the local measurement variable of node i, and This is an estimate of the global average. x i It can represent physical quantities such as voltage, frequency, and power. Node i is only connected to its adjacent nodes. Communication is then established. Next, each node processes the estimates of its neighbors and its own local measurement x in the following manner. i To update itself Estimated value:

[0032]

[0033] Among them, a ij These are the elements of the adjacency matrix. The above equation can be rewritten in matrix form:

[0034]

[0035] Wherein, the local measurement vector X = [x1, x2, ..., xn ] T Global estimation vector By differentiating the above equation, dynamic consistency can be demonstrated in the frequency domain:

[0036]

[0037] Where X(s) and They are X and The Laplace transform of . n ∈R n×n It is the identity matrix, and H obs It is the transfer function. If L is balanced, then the estimates of all nodes will converge to a common value, which is the average of all elements in X, i.e.:

[0038]

[0039] Where, Q∈R n×n X is an average matrix where all elements are equal to 1 / n. ss It is a vector X∈R n×1 The steady-state value of . is a column vector with all elements equal to 1.

[0040] like Figure 2 As shown, the unified distributed dynamic optimization control method for autonomous microgrids in this embodiment specifically includes the following steps:

[0041] Step 1: Embed a frequency regulator at the virtual synchronous converter, estimate the average frequency of the autonomous microgrid through the frequency regulator, compare the average frequency with the rated frequency to generate an error term, and input the error term into the integrator to obtain the first power correction term.

[0042] In a VSG-based autonomous distribution microgrid, all generating units and controllable loads are connected to the bus via virtual synchronous converters. A virtual synchronous motor control scheme is used to maintain the stability of the bus voltage and frequency, and to proportionally distribute load power among distributed generation sources (DGs). The virtual electromotive force of each generating unit... and angular frequency ω i It can be represented as:

[0043]

[0044] Among them, P seti and Q refi These are the reference active power and reactive power setting values, respectively. refi and v oi These are the rated voltage and the output voltage, respectively. P i and Q i K represents average active power and reactive power.ii and K vi These are the voltage control gain and the voltage droop coefficient, respectively. K fi Here, denoted by , is the frequency droop coefficient, and J is the virtual inertia.

[0045] In this embodiment, a frequency regulator is embedded at the virtual synchronous converter i. This frequency regulator uses a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid, where the dynamic consensus protocol can be expressed as:

[0046]

[0047] in, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0048] Then, the estimated average frequency is compared with the rated frequency ω. n The comparison generates an error term, which is then fed into the integrator Hi(s) to obtain the first power correction term.

[0049]

[0050] The first power correction term This will force the frequency of the autonomous microgrid to tend towards ω. n Furthermore, the frequency estimate for each unit will converge to ω. n Convergence speed is affected by a variety of factors, such as communication topology, integrator coefficients, and converter response characteristics.

[0051] Step 2: Embed a benefit optimizer at the virtual synchronous converter, generate a neighborhood error through the benefit optimizer, and input the neighborhood error into the integrator to obtain the second power correction term.

[0052] Traditionally, the active power distribution of each power generation unit is proportional to its capacity, i.e., P1 / S1 = P2 / S2 = ... = P n / S n However, different types of distributed generation (DGs) exist in microgrids, which may have different generation benefits. Similarly, different loads in a self-governing microgrid also have different consumption benefits. Therefore, traditional capacity-based power allocation methods lead to higher operating costs and poorer social benefits.

[0053] To maximize the social benefits of VSG-based autonomous microgrids, both the generation and load sides need to be considered. The generation-side benefits can be defined for generation units, and the demand-side benefits can be defined for electricity loads as follows:

[0054]

[0055] Among them, W i,G W represents the efficiency of a power generation unit. i,D This indicates the benefit of electricity load. (P) i Let represent the output power of the i-th virtual synchronous converter, where i ∈ VSG_DG ∪ VSG_CL. i (.) and U i (.) represent the cost function and utility function of the corresponding unit, respectively, where p is the agreed energy price. The cost function and utility function are modeled as follows:

[0056]

[0057] Where, α i β i γ i It is by DG i Cost coefficient determined by type. ε i and η i These are parameters for different electrical loads. Therefore, the goal of autonomous microgrid optimization is to maximize social benefits, namely:

[0058]

[0059] By substituting the cost function and utility function into the expressions for the benefits on the generation side and the demand side, the optimization problem is reformulated as a minimization problem:

[0060]

[0061] Among them, P con P represents the constant load in MG. loss P represents the total power loss. imin and P imax These represent the local consumption / generation constraints for each unit. To find the optimal solution to the above equation, we can first ignore the inequality constraints and define the Lagrange multiplier μ and the Lagrange function for minimization:

[0062]

[0063] The optimal solution can be obtained by solving the following linear equation:

[0064]

[0065] Among them, dCi (P i ) / dP i =λ i DG i The incremental cost. The above formula follows the principle of equal incremental cost, maximizing the social benefits of microgrids.

[0066] To employ the proposed distributed optimization framework, this implementation also embeds a benefit optimizer at the virtual synchronous converter i. This benefit optimizer asymptotically eliminates the deviation in incremental costs between units. A neighborhood error δλ is generated for the benefit optimizer i based on local incremental cost information and the incremental cost information of its neighbors. i :

[0067]

[0068] Then, the neighborhood error δλ i via integrator G i (s), generating the second power correction term To maximize the social benefits of microgrids, namely:

[0069]

[0070] As shown in the above equation, if the local incremental cost is less than or greater than the average value of its neighbors, the benefit optimizer will correspondingly increase or decrease its reference power setting. This, in turn, increases the output active power and incremental cost of the virtual synchronous converter i. Ultimately, in steady state, all neighborhood errors δλ i All costs will tend to zero, and all incremental costs will converge to the same value, which means that social benefits have been maximized.

[0071] Step 3: Add the first power correction term and the second power correction term to the reference power term to obtain the power setting term, and use the power setting term as the input of the virtual synchronous converter.

[0072] Under steady state, the input of the virtual synchronous converter—the power setting term P seti The value consists of two parts, namely the reference power term P. refi and power correction term δP seti The frequency regulator and the efficiency optimizer each generate two power correction terms. and The sum of these two items constitutes the power correction term δP. seti Power correction term δP seti Added to the reference power term P refi This ensures that the output power of each unit converges to the optimal value, while restoring the steady-state value of the global frequency of the autonomous system.

[0073] It is not difficult to see that, in terms of frequency control, this invention uses a distributed dynamic consensus algorithm to estimate the system average frequency by embedding a frequency regulator, and then corrects the frequency deviation caused by the virtual synchronous machine control without oscillation, so as to solve the problem of power distribution accuracy and voltage oscillation caused by local measurement errors; in terms of benefit optimization, by embedding a benefit optimizer, the output power of different devices is adjusted with the goal of maximizing social benefits, so as to ensure the optimal operation of the microgrid and thus achieve global optimal control.

[0074] The second embodiment of the present invention relates to a unified distributed dynamic optimization control device for an autonomous microgrid, comprising:

[0075] The first power correction module is used to embed a frequency regulator at the virtual synchronous converter, estimate the average frequency of the autonomous microgrid through the frequency regulator, compare the average frequency with the rated frequency to generate an error term, and input the error term into an integrator to obtain the first power correction term.

[0076] The second power correction module is used to embed a benefit optimizer at the virtual synchronous converter, generate a neighborhood error through the benefit optimizer, and input the neighborhood error into an integrator to obtain a second power correction term;

[0077] The control module is used to add the first power correction term and the second power correction term to the reference power term to obtain a power setting term, and use the power setting term as the input of the virtual synchronous converter.

[0078] The frequency regulator employs a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid, including an average frequency estimation unit. Estimate the average frequency of the autonomous microgrid, where, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0079] The benefit optimizer generates neighborhood error based on local incremental cost information and its neighboring incremental cost information, including an incremental cost error calculation unit, and employs... Generate neighborhood error, where δλ i (t) represents the neighborhood error, a ij λ is an element of the communication topology adjacency matrix. i (t) represents the incremental cost of the distributed power source corresponding to the virtual synchronous converter i, N iThis is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

[0080] The third embodiment of the present invention relates to 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 computer program to implement the steps of the unified distributed dynamic optimization control method for autonomous microgrids of the first embodiment.

[0081] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the unified distributed dynamic optimization control method for autonomous microgrids of the first embodiment.

[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A unified distributed dynamic optimization control method for autonomous microgrids, characterized in that, Includes the following steps: A frequency regulator is embedded in the virtual synchronous converter. The average frequency of the autonomous microgrid is estimated by the frequency regulator, and the average frequency is compared with the rated frequency to generate an error term. The error term is input into an integrator to obtain the first power correction term. A benefit optimizer is embedded in the virtual synchronous converter. The benefit optimizer generates a neighborhood error and inputs the neighborhood error into an integrator to obtain a second power correction term. The first power correction term and the second power correction term are added to the reference power term to obtain the power setting term, and the power setting term is used as the input of the virtual synchronous converter.

2. The unified distributed dynamic optimization control method for autonomous microgrids according to claim 1, characterized in that, The frequency regulator uses a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid.

3. The unified distributed dynamic optimization control method for autonomous microgrids according to claim 2, characterized in that, The dynamic consensus protocol is represented as follows: in, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

4. The unified distributed dynamic optimization control method for autonomous microgrids according to claim 1, characterized in that, The benefit optimizer generates neighborhood error based on local incremental cost information and the incremental cost information of its neighbors.

5. The unified distributed dynamic optimization control method for autonomous microgrids according to claim 4, characterized in that, The generation of neighborhood error based on local incremental cost information and its neighboring incremental cost information specifically involves: Where, δλ i (t) represents the neighborhood error, a ij λ is an element of the communication topology adjacency matrix. i (t) represents the incremental cost of the distributed power source corresponding to the virtual synchronous converter i, N i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

6. A unified distributed dynamic optimization control device for an autonomous microgrid, characterized in that, include: The first power correction module is used to embed a frequency regulator at the virtual synchronous converter, estimate the average frequency of the autonomous microgrid through the frequency regulator, compare the average frequency with the rated frequency to generate an error term, and input the error term into an integrator to obtain the first power correction term. The second power correction module is used to embed a benefit optimizer at the virtual synchronous converter, generate a neighborhood error through the benefit optimizer, and input the neighborhood error into an integrator to obtain a second power correction term; The control module is used to add the first power correction term and the second power correction term to the reference power term to obtain a power setting term, and use the power setting term as the input of the virtual synchronous converter.

7. The unified distributed dynamic optimization control device for autonomous microgrids according to claim 6, characterized in that, The frequency regulator employs a dynamic consensus protocol to estimate the average frequency of the autonomous microgrid, including an average frequency estimation unit. Estimate the average frequency of the autonomous microgrid, where, The average frequency of the autonomous microgrid estimated by the frequency regulator embedded at virtual synchronous converter i, ω i C is the output frequency of the virtual synchronous converter i. E For frequency-adjusted coupling gain, a ij N represents the elements of the communication topology adjacency matrix. i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

8. The unified distributed dynamic optimization control device for autonomous microgrids according to claim 6, characterized in that, The benefit optimizer generates neighborhood error based on local incremental cost information and its neighboring incremental cost information, including an incremental cost error calculation unit, and employs... Generate neighborhood error, where δλ i (t) represents the neighborhood error, a ij λ is an element of the communication topology adjacency matrix. i (t) represents the incremental cost of the distributed power source corresponding to the virtual synchronous converter i, N i This is the set of virtual synchronous converters adjacent to virtual synchronous converter i.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the unified distributed dynamic optimization control method for autonomous microgrids as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the unified distributed dynamic optimization control method for autonomous microgrids as described in any one of claims 1-5.

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