Direct-current micro-grid network mode switching control method and device based on graph theory

Abstract

The invention relates to the technical field of power grids, in particular to a direct-current micro-grid network mode switching control method and device based on a graph theory, and the method comprises the steps: constructing an attraction domain estimation function of a direct-current micro-grid, so as to generate a switching stability judgment result of at least one path between all operation modes of the direct-current micro-grid; based on the switching stability judgment result and the basic directed graph model, constructing a final directed graph model, and solving all stable switching paths for switching from the current operation mode to an upper-layer energy management specified target operation mode; and based on all the stable switching paths, solving a final switching path which meets a preset condition and is used for switching from the current operation mode to an upper-layer energy management specified target operation mode. According to the path planning method based on the graph theory and the neural Lyapunov method, the mode switching problem is converted into the reachable path searching problem by constructing the directed graph model considering the stability constraint, and the optimal path between mode switching is effectively solved.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a graph theory-based method and apparatus for mode switching control of DC microgrid networks. Background Technology

[0002] With the rapid development of distributed energy and power electronics technology, DC microgrids have been widely used in scenarios such as data centers, commercial buildings, and lunar power supply due to their high efficiency and flexibility.

[0003] In related technologies, to achieve coordinated operation and power management of multi-node DC microgrid networks, upper-level energy management systems often adapt to changing load demands and energy conditions by scheduling different operating modes. Each operating mode corresponds to a specific voltage range and control strategy. By switching operating modes, flexible adjustment of the bus voltage can be achieved, maintaining the stable operation of the DC microgrid system.

[0004] However, traditional operating mode scheduling technologies are primarily designed for single-node DC microgrid systems and are difficult to apply directly to practical networked systems, especially in multi-node DC microgrid networks. Switching stability depends not only on the stability of each individual mode but also on the strong coupling effects and dynamic interactions between different nodes in the network. Even if all individual modes remain stable, an inappropriate switching strategy can still cause the system trajectory to deviate from its attraction domain, leading to voltage instability or even large-scale power outages. Furthermore, the nonlinear dynamic characteristics during mode switching and the interactions between multiple modes further complicate the stability analysis of operating mode switching. A stable control strategy for operating mode switching in large-scale, multi-node DC microgrids remains lacking and urgently needs to be addressed. Summary of the Invention

[0005] This application provides a graph theory-based DC microgrid network mode switching control method and device to address the issues in related technologies, such as the fact that the switching stability of multi-node DC microgrid networks is easily affected by the strong coupling effect and dynamic interaction between different nodes in the network, and that unreasonable switching strategies may still cause the system trajectory to deviate from the attraction domain, leading to voltage instability or even large-scale power outages. Furthermore, the nonlinear dynamic characteristics during mode switching and the interaction between multiple modes make the analysis of the stability of the operating mode switching more complex, and there is still a lack of stable control strategies for the operation mode switching of large-scale, multi-node DC microgrids.

[0006] The first aspect of this application provides a graph theory-based method for mode switching control of a DC microgrid network, comprising the following steps: constructing a basic directed graph model of a DC microgrid satisfying a target scale, and constructing an attraction domain estimation function for the DC microgrid, so as to generate a switching stability judgment result for at least one path between all pairs of operating modes of the DC microgrid based on the attraction domain estimation function; constructing a final directed graph model of the DC microgrid based on the switching stability judgment result and the basic directed graph model, and solving for all stable switching paths from the current operating mode to the target operating mode specified by the upper-level energy management; and solving for the final switching path from the current operating mode to the target operating mode specified by the upper-level energy management that satisfies preset conditions based on all stable switching paths.

[0007] Optionally, in one embodiment of this application, the step of constructing the attraction domain estimation function of the DC microgrid to generate a switching stability judgment result for at least one path among all operating mode pairs of the DC microgrid based on the attraction domain estimation function includes: constructing an approximate Lyapunov function corresponding to the operating mode switching process of the DC microgrid; constructing a Lyapunov risk function corresponding to the operating mode switching process based on the positive definiteness and negative definiteness of the approximate Lyapunov function in the operating mode switching process; and constructing the attraction domain estimation function based on the Lyapunov risk function to determine the stability judgment basis used to generate the switching stability judgment result.

[0008] Optionally, in one embodiment of this application, the expression for the Lyapunov risk function is:

[0009] in, For Lyapunov risk value / outcome, the first item The second term is the positive definite penalty function for the Lyapunov function. This is the negative definite penalty function of the Lyapunov function. and These represent the total number of training iterations and the current number of training iterations, respectively.

[0010] Optionally, in one embodiment of this application, the step of solving for the final switching path that satisfies preset conditions for switching from the current operating mode to the upper-level energy management designated target operating mode based on all stable switching paths includes: defining the switching target of the DC microgrid based on the operating characteristics of the DC microgrid; converting the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management designated target operating mode according to the switching target; and constructing a single-objective function for solving the multi-objective optimization problem, so as to solve for the final switching path of switching from the current operating mode to the upper-level energy management designated target operating mode according to the single-objective function.

[0011] Optionally, in one embodiment of this application, the step of converting the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management designated target operating mode according to the switching objective includes: constructing a stability objective, a path length objective, and an economic objective for switching from the current operating mode to the upper-level energy management designated target operating mode; combining the stability objective, the path length objective, and the economic objective to determine the multi-objective optimization problem, so as to determine the final switching path by solving the multi-objective optimization problem.

[0012] Optionally, in one embodiment of this application, the expression for the economic objective is:

[0013] in, For economic goals, Vertex corresponding to the running mode The path between them For the instantaneous cost of mode switching, The economic cost of the time spent in the pattern; The expression for the multi-objective optimization problem is:

[0014]

[0015] in, For reachable paths, For the set of reachable paths, This refers to the voltage fluctuation amplitude. Voltage fluctuation threshold The maximum path length. To maximize cost.

[0016] A second aspect of this application provides a graph theory-based DC microgrid network mode switching control device, comprising: a judgment module, configured to construct a basic directed graph model of a DC microgrid satisfying a target scale, and construct an attraction domain estimation function of the DC microgrid, so as to generate a switching stability judgment result for at least one path between all operating mode pairs of the DC microgrid based on the attraction domain estimation function; a construction module, configured to construct a final directed graph model of the DC microgrid based on the switching stability judgment result and the basic directed graph model, and solve for all stable switching paths from the current operating mode to the upper-level energy management specified target operating mode; and a solution module, configured to solve for the final switching path from the current operating mode to the upper-level energy management specified target operating mode that satisfies preset conditions based on all stable switching paths.

[0017] Optionally, in one embodiment of this application, the judgment module includes: a first construction unit, configured to construct an approximate Lyapunov function corresponding to the operation mode switching process of the DC microgrid; a second construction unit, configured to construct a Lyapunov risk function corresponding to the operation mode switching process based on the positive definiteness and negative definiteness of the approximate Lyapunov function in the operation mode switching process; and a determination unit, configured to construct the attraction domain estimation function based on the Lyapunov risk function, so as to determine the stability judgment basis for generating the switching stability judgment result through the attraction domain estimation function.

[0018] Optionally, in one embodiment of this application, the expression for the Lyapunov risk function is:

[0019] in, For Lyapunov risk value / outcome, the first item The second term is the positive definite penalty function for the Lyapunov function. This is the negative definite penalty function of the Lyapunov function. and These represent the total number of training iterations and the current number of training iterations, respectively.

[0020] Optionally, in one embodiment of this application, the solving module includes: a definition unit, configured to define a switching objective of the DC microgrid based on the operating characteristics of the DC microgrid; a conversion unit, configured to convert the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management specified target operating mode according to the switching objective; and a solving unit, configured to construct a single-objective function for solving the multi-objective optimization problem, so as to solve the final switching path of switching from the current operating mode to the upper-level energy management specified target operating mode according to the single-objective function.

[0021] Optionally, in one embodiment of this application, the conversion unit includes: a construction subunit, configured to construct a stability objective, a path length objective, and an economic objective for switching from the current operating mode to the upper-level energy management specified target operating mode; and a determination subunit, configured to combine the stability objective, the path length objective, and the economic objective to determine the multi-objective optimization problem, so as to determine the final switching path by solving the multi-objective optimization problem.

[0022] Optionally, in one embodiment of this application, the expression for the economic objective is:

[0023] in, For economic goals, Vertex corresponding to the running mode The path between them For the instantaneous cost of mode switching, The economic cost of the time spent in the pattern; The expression for the multi-objective optimization problem is:

[0024]

[0025] in, For reachable paths, For the set of reachable paths, This refers to the voltage fluctuation amplitude. Voltage fluctuation threshold The maximum path length. To maximize cost.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the graph theory-based DC microgrid network mode switching control method as described in the above embodiments.

[0027] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the graph theory-based DC microgrid network mode switching control method described above.

[0028] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the graph theory-based DC microgrid network mode switching control method described above.

[0029] This application's embodiments can construct an attraction domain estimation function for a DC microgrid, generate stability judgment results for path switching between all operating mode pairs, and thus construct a final directed graph model of the DC microgrid. It then solves for all stable switching paths from the current operating mode to the target operating mode specified by upper-level energy management, ultimately obtaining the final switching path that meets certain conditions. This realizes a path planning method based on graph theory and the Lyapunov neural network method. By constructing a directed graph model considering stability constraints, the stability problem of inter-mode switching under upper-level energy management scheduling of the microgrid is transformed into a reachable path planning problem in graph theory. It completes the search for stable paths from the current mode to the target mode specified by upper-level energy management, and uses the A* heuristic search algorithm to solve for all stable switching paths (reachable paths) in a large-scale multi-node DC microgrid system. Simultaneously, the solution process considers indicators such as voltage fluctuation, path length, and economic efficiency. Through multi-objective optimization, a linear scalarization method is used to determine the optimal switching path, ensuring that the stable switching path that best meets the switching objectives of the DC microgrid is obtained. This provides solid theoretical support and practical guidance for the stable operation of multi-node DC microgrid networks. This addresses the issue that the switching stability of multi-node DC microgrid networks is susceptible to strong coupling effects and dynamic interactions between different nodes in the network. Inappropriate switching strategies may still cause the system trajectory to deviate from the attraction domain, leading to voltage instability or even large-scale power outages. Furthermore, the nonlinear dynamic characteristics during mode switching and the interactions between multiple modes make the analysis of the stability of the operating mode switching more complex. There has always been a lack of stable control strategies for the switching of operating modes in large-scale, multi-node DC microgrids.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a graph theory-based DC microgrid network mode switching control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of a neural network fitting a Lyapunov function according to an embodiment of this application; Figure 3 This is a schematic diagram of a directed graph according to an embodiment of this application; Figure 4 This is a flowchart of an A* heuristic search algorithm according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a graph theory-based DC microgrid network mode switching control device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Graph theory-based DC microgrid network mode switching control device; 100-Judgment module, 200-Construction module and 300-Solution module; 601-Memory, 602-Processor and 603-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following describes a graph-based DC microgrid network mode switching control method and apparatus according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, this application provides a graph-based DC microgrid network mode switching control method. In this method, an attraction domain estimation function for the DC microgrid can be constructed, and the switching stability judgment results of paths between all operating mode pairs can be generated. This allows for the construction of a final directed graph model of the DC microgrid, solving for all stable switching paths from the current operating mode to the target operating mode specified by the upper-level energy management, and ultimately obtaining the final switching path that meets certain conditions. This leads to a path planning method based on graph theory and the Lyapunov neural network. By constructing a directed graph model that considers stability constraints, the stability problem of inter-mode switching under the upper-level energy management scheduling of a microgrid is transformed into a reachable path planning problem in graph theory. This method completes the search for stable paths from the current mode to the target mode specified by the upper-level energy management. The A* heuristic search algorithm is used to solve all stable switching paths (reachable paths) in a large-scale multi-node DC microgrid system. During the solution process, factors such as voltage fluctuation, path length, and economic efficiency are considered. Through multi-objective optimization, the optimal switching path is determined using a linear scalarization method, ensuring that the stable switching path that best meets the switching objectives of the DC microgrid is obtained. This provides solid theoretical support and practical guidance for the stable operation of multi-node DC microgrid networks. This addresses the issue that the switching stability of multi-node DC microgrid networks is susceptible to strong coupling effects and dynamic interactions between different nodes in the network. Inappropriate switching strategies may still cause the system trajectory to deviate from the attraction domain, leading to voltage instability or even large-scale power outages. Furthermore, the nonlinear dynamic characteristics during mode switching and the interactions between multiple modes make the analysis of the stability of the operating mode switching more complex. There has always been a lack of stable control strategies for the switching of operating modes in large-scale, multi-node DC microgrids.

[0035] Specifically, Figure 1 This is a flowchart of a graph theory-based DC microgrid network mode switching control method provided in an embodiment of this application.

[0036] like Figure 1 As shown, the graph theory-based DC microgrid network mode switching control method includes the following steps: In step S101, a basic directed graph model of a DC microgrid that meets the target scale is constructed, and an attraction domain estimation function of the DC microgrid is constructed. Based on the attraction domain estimation function, a switching stability judgment result of at least one path between all operating mode pairs of the DC microgrid is generated.

[0037] Those skilled in the art will understand that a multi-mode DC microgrid (hereinafter referred to as a DC microgrid) can be understood here as a multi-node DC microgrid network with different system-level operating modes and multiple physical sub-networks (hereinafter referred to as sub-networks) at the system level. The different system-level operating modes of the DC microgrid (corresponding to the operating modes of the DC microgrid at the system level) can be implemented by the physical sub-networks of the DC microgrid executing different operating modes. That is, the different system-level operating modes of the DC microgrid can be implemented by the different sub-networks of the DC microgrid executing different operating modes, and the system-level operating mode of the DC microgrid is essentially a combination of the operating modes of multiple sub-networks.

[0038] In practical applications, to achieve coordinated operation and power management of DC microgrids, the upper-level energy management system often schedules each sub-network to execute different operating modes to adapt to constantly changing load demands and energy conditions. Each system-level operating mode of a DC microgrid corresponds to a specific voltage range and control strategy. By switching between different system-level operating modes, flexible adjustment of the bus voltage can be achieved, maintaining the stable operation of the DC microgrid system. For simplicity, the DC microgrid system will be referred to simply as the "system" in the following explanation. The DC microgrid system and the DC microgrid are essentially the same; one is a term used from a system perspective, and the other from a grid perspective.

[0039] In multi-node DC microgrid networks, switching stability depends not only on the stability of each independent operating mode, but also on the strong coupling effect and dynamic interaction between different nodes in the network. Even if all individual operating modes remain stable, an unreasonable switching strategy may still cause the DC microgrid system trajectory to deviate from the attraction domain, leading to voltage instability or even large-scale power outages. Therefore, how to switch operating modes more stably and smoothly has become a key research focus for DC microgrids.

[0040] In practical applications, the scale of the DC microgrid is also an important influencing factor in the stability study of the operation mode switching process during the execution of the DC microgrid system operation mode switching.

[0041] Because switching the operating mode of a DC microgrid at the system level—that is, switching the operating mode of the entire DC microgrid—requires changing the local control operating mode of multiple nodes. Small-scale DC microgrids have fewer nodes, so fewer nodes need to be studied during the operating mode switching process. However, the stability during the operating mode switching process of large-scale DC microgrids remains a complex challenge.

[0042] In some embodiments, this application may, but is not limited to, abstractly represent a DC microgrid that meets the target size by constructing a basic directed graph model of the DC microgrid that meets the target size. For ease of explanation, in the following embodiments, this application will refer to the DC microgrid that meets the target size as DC microgrid, that is, DC microgrid in the following embodiment description will refer to the DC microgrid that meets the target size.

[0043] Here, the target scale can be understood as a pre-defined set of multi-dimensional quantitative indicators used to determine whether a DC microgrid meets the large-scale standard. These include, but are not limited to, the following dimensions: capacity (e.g., setting a threshold of rated power or total installed capacity ≥ 10MW (specific values ​​need to be considered in conjunction with the scenario); coverage (e.g., the size of the service area or the size of the user group); access resources (i.e., the number or complexity of distributed power sources such as photovoltaic / wind power, energy storage systems, and loads connected, such as the need to connect multiple power clusters and diverse loads); and functional requirements (e.g., whether it needs to support large-scale application scenarios, such as continuous operation of isolated grids, interconnection of multiple microgrids, and acceptance of a high proportion of renewable energy).

[0044] It should be noted that the specific target scale can be set or adjusted by those skilled in the art according to the actual application scenario. The examples in this application are merely illustrative and do not constitute specific limitations. Furthermore, the target scale in this application is not a fixed value but a standard. Only when the actual capacity, coverage, and access resources of the DC microgrid all meet this standard can it be recognized as a large-scale DC microgrid.

[0045] Firstly, the embodiments of this application can construct a basic directed graph model for a DC microgrid that meets the target scale: containing a set of system operating modes. Taking a DC microgrid with discrete operating modes as an example, the embodiments of this application may, but are not limited to, define the operating modes of the system as vertices of a graph, that is, abstract each operating mode as a directed graph. An independent vertex .

[0046] It should be noted that since the operating mode essentially refers to the set of local control operating modes of each physical sub-network, in all directed graphs (models) of this application, the operating mode and the physical sub-network are represented by the same point. To avoid conceptual confusion, in the following description of embodiments, this application will refer to a point as a node and a vertex, respectively. When referred to as a node, it specifically refers to the physical sub-network in the DC microgrid system, and when referred to as a vertex, it specifically refers to the abstract representation of the operating mode in the directed graph model.

[0047] Then, embodiments of this application can construct an attraction domain estimation function for a DC microgrid, thereby generating a switching stability judgment result for at least one path among all operating mode pairs of the DC microgrid based on the attraction domain estimation function.

[0048] Here, the attraction domain refers to the set of all initial operating states of a DC microgrid system that, after being disturbed, can still return to a stable operating equilibrium point through its own control or characteristics. The attraction domain estimation function here refers to the function used to quantify and determine the boundary and range of the attraction domain for the stable operating equilibrium point of the DC microgrid.

[0049] Based on the attraction domain estimation function, the embodiments of this application can estimate the switching stability judgment result of at least one path between all operating mode pairs of DC microgrid, that is, estimate whether at least one path between all operating mode pairs will cause the DC microgrid to leave the current attraction domain. If it leaves the domain, it indicates that the path is unstable.

[0050] Here, "operation mode pair" refers to the switching relationship between any two operating modes of a DC microgrid. It should be noted that the operation mode pair is directional; for example, the operation mode pair... Indicates the operating mode Switch to running mode Operating mode Indicates the operating mode Switch to running mode .

[0051] At least one path between operating modes refers to the reachable path between any two operating modes of a DC microgrid, that is, the path that enables switching between the two operating modes. Since operating modes can be switched between multiple modes, the operating mode can be switched to another operating mode first and then to the final target operating mode (the target operating mode refers to the desired operating mode of the DC microgrid system pre-set by the upper management system of the DC microgrid system).

[0052] At this point, there is at least one path between the pairs of operating modes. That is, at least one path means that there may be multiple paths between the pairs of operating modes when a transition switching mode is introduced (the transition switching mode here refers to switching to another operating mode first during the intermediate process of switching between two operating modes, and the other operating mode plays a transitional role at this time).

[0053] The stability judgment result of the path switching can be understood here as the judgment result of whether the path traversed when switching from one operating mode (one vertex A) to another operating mode (another vertex B) is stable.

[0054] If the path is within the range estimated by the attraction domain estimation function, then the path is stable, meaning that the path taken when switching from one operating mode (a vertex A) to another operating mode (another vertex B) is stable.

[0055] It should be noted that when switching from one operating mode to another, it may not require passing through any vertices, meaning that the switch can be direct and the process remains stable. Alternatively, it may require passing through multiple vertices, meaning that a transitional operating mode needs to be introduced, switching to different operating modes sequentially before finally switching to the target operating mode. In this process, each switching of operating modes remains stable.

[0056] Therefore, the switching stability judgment result of at least one path includes the switching stability judgment result of at least one edge. That is, the switching stability judgment result of at least one path may include only one edge (when switching from one operating mode to another, the switching can be done directly and the switching process remains stable), or it may include the switching stability judgment result of multiple edges (when switching from one operating mode to another, different operating modes need to be switched to in sequence before finally switching to the target operating mode. In this case, the switching process of each operating mode remains stable and each edge traversed is stable).

[0057] Thus, the embodiments of this application have completed the construction of the basic directed graph model of the DC microgrid and obtained the switching stability judgment result of at least one path between all operating mode pairs of the DC microgrid.

[0058] Optionally, in one embodiment of this application, an attraction domain estimation function for the DC microgrid is constructed to generate a switching stability judgment result for at least one path among all operating mode pairs of the DC microgrid based on the attraction domain estimation function, including: The approximate Lyapunov function corresponding to the operation mode switching process of constructing a DC microgrid; Based on the positive and negative definiteness of the approximate Lyapunov function in the operation mode switching process, a Lyapunov risk function corresponding to the operation mode switching process is constructed. Based on the Lyapunov hazard function, an attraction domain estimation function is constructed to determine the stability judgment criteria used to generate the handover stability judgment results. The expression for the Lyapunov hazard function is as follows:

[0059] in, For Lyapunov risk value / outcome, the first item The second term is the positive definite penalty function for the Lyapunov function. This is the negative definite penalty function of the Lyapunov function. and These represent the total number of training iterations and the current number of training iterations, respectively.

[0060] In actual implementation, when constructing the attraction domain estimation function, this application may, but is not limited to, first constructing an approximate Lyapunov function corresponding to the DC microgrid during the operation mode switching process, and then constructing a Lyapunov risk function corresponding to the DC microgrid operation mode switching process based on the positive definiteness and negative definiteness of the approximate Lyapunov function during the DC microgrid operation mode switching process, and finally determining the attraction domain estimation function through the Lyapunov risk function.

[0061] Lyapunov functions are a primary analytical method in Lyapunov stability theory, which itself is a rigorous mathematical approach used to analyze and assess the stability of dynamic systems, especially nonlinear systems. The core idea of ​​Lyapunov stability theory can be understood as follows: if the total "energy" of a system decreases over time (i.e., the system gradually approaches equilibrium), then the system is stable; conversely, if the energy increases continuously, the system is unstable.

[0062] In short, in order to measure the stability between operating mode switching, this application may, but is not limited to, construct a switching stability criterion for multi-operating mode networked DC microgrids based on Lyapunov functions, thereby reducing the conservatism of the attraction domain estimation.

[0063] Figure 2 This is a schematic diagram illustrating the fitting of a Lyapunov function to a neural network according to an embodiment of this application. Figure 2 As shown, firstly, this application embodiment can construct a neural network to approximate the Lyapunov function corresponding to the operation mode switching process of the DC microgrid. That is, the Lyapunov function corresponding to the operation mode switching process of the DC microgrid is approximated by a neural network. In this application embodiment, it can be, but is not limited to, simply referred to as the approximate Lyapunov function, which is essentially a neural network. The expression of the approximate Lyapunov function can be, but is not limited to, as follows:

[0064] in, This is the state vector, which is the input to the neural network; It is a scalar function value, and it is the output of the neural network; For activation function, These are the training parameters for the neural network. .

[0065] Then, in this embodiment of the application, the neural network can be trained using a training set to learn the Lyapunov function, thereby constructing the Lyapunov risk function based on the positive definiteness and negative definiteness of the approximate Lyapunov function during the operation mode switching process.

[0066] In this embodiment, the core data of the training set may, but is not limited to, state vectors randomly sampled from the state space of a DC microgrid system. The specific generation process may, but is not limited to, be represented as follows: Sampling space: All training samples are from a predefined high-dimensional state space. Extracted from the middle. This region is defined as... ,in It is the radius, which defines the state space range in which its stability is of concern; Sampling method: Training samples are independently and identically distributed from... Randomly selected from; Data point format: Each training sample is a state vector ,in, The dimension of the state space. This represents the state of an interconnected microgrid system at a certain moment, where it deviates from a predetermined equilibrium point.

[0067] The Lyapunov risk function in the embodiments of this application can be, but is not limited to, expressed as follows:

[0068] in, For Lyapunov risk value / outcome, the first item This is a positive definiteness penalty function for Lyapunov functions, used to penalize approximate Lyapunov functions that do not satisfy positive definiteness. The second term... This is the negative definiteness penalty function for Lyapunov functions, used to penalize approximate Lyapunov functions that do not satisfy negative definiteness. and These represent the total number of training iterations and the current number of training iterations, respectively.

[0069] Minimize using stochastic gradient descent The network parameters of the neural network can then be obtained. The final result is the final network parameters of the neural network.

[0070] Next, embodiments of this application can perform training set augmentation on the neural network, using an SMT solver to search for points that violate Lyapunov conditions:

[0071] These "counterexamples" are then added to the training set, and the neural network is retrained until no more counterexamples can be found.

[0072] Finally, embodiments of this application can construct an attraction domain estimation function to estimate the attraction domain of a DC microgrid, wherein the attraction domain estimation function can be, but is not limited to, expressed as:

[0073] in, It is a valid Lyapunov function. It is a pre-defined effective area. This is the maximum safety threshold.

[0074] Using this attraction domain estimation function, embodiments of this application can determine the stability judgment criteria for generating the switching stability judgment result of at least one path during the DC microgrid operation mode switching process. These stability judgment criteria can be, but are not limited to, expressed as follows:

[0075] in, For operating mode The neural Lyapunov function, Define the running mode The boundary of the attraction domain.

[0076] That is, for any operating mode , by operating mode Switch to running mode During the process, operating mode equilibrium point Conditions must be met This ensures that the process of switching operating modes is stable.

[0077] Simply put, it can be understood as the operating mode. Switch to running mode hour, equilibrium point Never leave the field of attraction Since the equilibrium point always lies within the attraction domain, stability can be maintained during operation mode switching, thus ensuring stability when switching between operation modes. Switch to running mode System stability at that time.

[0078] It should be noted that since switching between multiple operating modes may be required during the process, each switch must meet certain conditions. That is, each switch in the process of changing from the current operating mode to the target operating mode must meet certain conditions. Only in this way can we ensure that the transition from the current operating mode to the target operating mode is stable.

[0079] In this case, for any operating mode The evaluation function for its switching stability can be, but is not limited to, expressed as:

[0080] in, Indicates from arrive The switching stability assessment result is stable. This indicates that the result of the switching stability assessment is unstable.

[0081] Step S102: Based on the switching stability judgment results and the basic directed graph model, construct the final directed graph model of the DC microgrid, and solve all stable switching paths from the current operating mode to the target operating mode specified by the upper energy management.

[0082] In other embodiments, after the switching stability criterion of the completed path is established and the switching stability judgment result of at least one path is obtained, this application can combine the stability judgment result and the basic directed graph model to construct the final directed graph model of the DC microgrid, thereby solving all stable switching paths from the current operating mode to the target operating mode specified by the upper energy management.

[0083] Here, the target operating mode specified by the upper-level energy management is the same as the target operating mode mentioned earlier. It can be understood as the desired operating mode of the DC microgrid system pre-set by the upper-level management system of the DC microgrid system, that is, a certain operating mode that the upper-level management system hopes to achieve by switching from the current operating mode.

[0084] Specifically, embodiments of this application may, but are not limited to, use the stability criterion for switching operating modes as the edge constraints of the directed graph model, that is, embodiments of this application may, based on... and formula The stability judgment result of each operating mode is evaluated when switching to another operating mode. When the switching from one operating mode to another is a stable switching, a directed edge is added from the operating mode (vertex) to the other operating mode (another vertex). The resulting directed graph model retains the stable switching relationship between multiple operating modes of each physical sub-network in the DC microgrid, that is, it retains all stable switching paths between each operating mode and another operating mode.

[0085] The set of operating modes of DC microgrid systems still includes individual Discrete operating modes, and abstract each operating mode as an independent vertex in a directed graph, as an example: First, based on all physical sub-network nodes in the DC microgrid system, traverse all ordered operating mode pairs. (equivalent to vertex pairs) Similarly, by switching the stability criterion, directed edges are selectively added to the directed graph, thereby dynamically constructing the directed graph. Here, the ordered execution mode can be understood as a sequence of execution modes with a directional order. Point to running mode .

[0086] In this embodiment of the application, a directed edge can be represented by its corresponding vertex pair, i.e., a directed edge. It can be written as Specifically, if the following conditions are met... ,but At this point, directed edges can be added to the graph. Otherwise, skip this running mode pair.

[0087] In this case, embodiments of this application can introduce intermediate operating modes by introducing path vertices in the path from one operating mode (one vertex) to another operating mode (another vertex) as intermediate operating modes to construct a stable switching path from one operating mode (one vertex) to another operating mode.

[0088] Furthermore, in directed graphs In this application embodiment, the voltage waveform between the two operating modes corresponding to each directed edge can be used as the basis for each directed edge, but is not limited to this. Assign edge weights , used to characterize from Switch to The cost.

[0089] It should be noted that the specific edge weights and assignment rules can be flexibly defined by those skilled in the art according to specific application requirements. The embodiments in this application are merely illustrative and do not impose specific limitations. In this embodiment, the edge weight can be defined as the intensity of voltage fluctuations during the operation mode switching process. The larger the weight value, the more severe the voltage fluctuations and the more significant the impact on system stability.

[0090] Figure 3 This is a schematic diagram of a directed graph according to an embodiment of this application. Figure 3 As shown, Figure 3 To illustrate a concrete example of a directed graph: in a graph containing In a DC microgrid network with 1 node, the system has 、 、 Three operating modes (corresponding to vertex sets) The edge set determined by the switching stability criterion (the set of all directed edges that can have switching stability) is as follows: Edge weight set Each weight can be defined, but is not limited to:

[0091] in, Represents a node Voltage fluctuation index during operating mode switching （ ） , This represents the total number of nodes in the DC microgrid network. This represents the maximum voltage fluctuation index among all nodes.

[0092] Therefore, the embodiments of this application can generate directed graph models. Furthermore, the directed graph model fully preserves the stable switching relationship between each operating mode, and the voltage fluctuation characteristics of each operating mode switch can be quantitatively characterized by edge weights.

[0093] Furthermore, based on the constructed directed graph, embodiments of this application can identify all reachable switching paths (stable switching paths) between any two operating modes, forming a set of reachable paths. In practical applications, this means that the set of reachable paths can be used. The search engine determines the final switching path from the current mode to the target mode.

[0094] For large-scale DC microgrid systems with multiple operating modes, the corresponding directed graph structures are often quite complex. Traditional search algorithms may face combinatorial explosion challenges when searching for reachable switching paths in directed graphs of large-scale microgrid operating modes. Therefore, this application embodiment may, but is not limited to, improve the A* heuristic search algorithm to construct an A* heuristic search algorithm suitable for this application embodiment. This algorithm intelligently guides the search direction through a heuristic function, avoiding traversing invalid branches and achieving the search from the current mode. To target mode Efficient path planning, searching from the current pattern To target mode All reachable switching paths.

[0095] Specifically, the A* heuristic search algorithm in this application combines the optimality guarantee of Dijkstra's algorithm with the efficiency of greedy best-first search. It can balance the actual cost and heuristic estimation through the evaluation function, ensuring both efficiency and reliability in large-scale graph search. Figure 4 Here is a flowchart of the A* heuristic search algorithm according to one embodiment of this application, as follows: Figure 4 As shown, the specific implementation details of the A* heuristic search algorithm in the embodiments of this application can be, but are not limited to, as follows: (a) Function Design: Evaluation function design:

[0096] in, The actual cost accumulated from the initial mode to the current mode. This is a heuristic cost estimate for moving from the current pattern to the target pattern.

[0097] Actual cost function design:

[0098] Heuristic function design:

[0099] in, The voltage deviation distance is calculated based on the normalized difference of rated voltage between modes. To control for policy variability, the Euclidean distance of the model parameters is used as a metric. It is the reciprocal of the topological similarity. For the weighting coefficients, satisfying , The specific settings can be flexibly configured by those skilled in the art based on the actual situation, and the embodiments in this application do not impose specific limitations.

[0100] (ii) Initialization: Create two collections: (1) Open list: Modes to be explored, initially only containing the starting mode. ; (2) Close list: Explored modes, initially empty.

[0101] Then calculate Evaluation value: .

[0102] (iii) Loop Search: When the open list is not empty: (1) Select from the open list Minimal pattern ; (2) If The search ends, and the backtracking path is the optimal solution; (3) Otherwise ( ): Will Moved to the closed list; Traversal All adjacent patterns : like Skip from the close list; Calculate new ; like Not on the open list, or new If smaller, then update. , Then Add to the open list and record its parent pattern as .

[0103] (iv) Termination conditions: If the open list is empty and the target is not found, it means there is no path; otherwise, backtrack from... arrive The parent pattern chain is a stable path.

[0104] Step S103: Based on all stable switching paths, solve for the final switching path that satisfies the preset conditions for switching from the current operating mode to the target operating mode specified by the upper-level energy management.

[0105] As one possible approach, after constructing a directed graph model that preserves stable switching paths between all operating modes in a DC microgrid, this application can use all stable switching paths in the directed graph model to solve for the final switching path that satisfies preset conditions for switching from the current operating mode to the target operating mode specified by the upper-level energy management.

[0106] Here, the preset conditions can be understood as pre-defined conditions for determining the final switching path, such as using the shortest path among all stable switching paths as the final switching path. Specific preset conditions can be determined by those skilled in the art based on actual circumstances; the embodiments in this application are merely illustrative and do not impose specific limitations.

[0107] The following section provides a further explanation of how to solve the final switching path from the current operating mode to the upper-level energy management specified target operating mode that meets the preset conditions in the embodiments of this application.

[0108] Optionally, in one embodiment of this application, based on all stable switching paths, solving for the final switching path that satisfies preset conditions for switching from the current operating mode to the upper-level energy management designated target operating mode includes: defining the switching target of the DC microgrid based on the operating characteristics of the DC microgrid; converting the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management designated target operating mode according to the switching target; and constructing a single-objective function for solving the multi-objective optimization problem, so as to solve for the final switching path of switching from the current operating mode to the upper-level energy management designated target operating mode according to the single-objective function.

[0109] In some embodiments, for the voltage instability problem caused by the switching between operating modes under the upper-level energy management and scheduling of DC microgrids, the embodiments of this application can transform the stability problem of operating mode switching into a reachable path planning problem in graph theory based on the graph characteristics of the directed graph model, and realize the search for a stable path from the current mode to the target mode specified by the upper-level energy management through the graph model.

[0110] Specifically, in this application embodiment, an intermediate transitional operating mode can be introduced by the vertices traversed by the path between the two operating modes to construct a switching path that can be reached by the switching mode, thereby avoiding system instability caused by directly switching operating modes.

[0111] Furthermore, considering that there are usually multiple reachable switching paths between the current operating mode and the target operating mode, and that the switching costs of these paths vary due to differences in intermediate modes and switching characteristics, the embodiments of this application can select the final switching path according to the switching target in the operating mode switching process.

[0112] First, the embodiments of this application can define the switching target of the DC microgrid based on the operating characteristics of the DC microgrid.

[0113] Here, operational characteristics can be understood as the inherent behavior, key parameter performance, and core constraints of a DC microgrid under different operating conditions (such as grid-connected / islanded, load fluctuations, and power output changes). These include, but are not limited to, characteristics related to operating modes: such as the switching logic of grid-connected operation, islanded operation, and multi-microgrid interconnection, as well as the power balance mechanisms under each mode (such as energy storage charging and discharging regulation and distributed power output response); key state quantity characteristics: such as the regulation capability, fluctuation range, and recovery speed of DC bus voltage, and the transmission efficiency of current / power; interface and interaction characteristics: such as the output fluctuation of distributed power sources (photovoltaics, wind power), the charging and discharging rate and capacity limits of energy storage systems, the type of load (sensitive load / normal load) and its start-stop characteristics, and the interaction response speed with the main grid; and constraint and tolerance characteristics: the system's tolerance threshold to disturbances (such as the maximum allowable load change and power fluctuation range), and the operational constraints of components (such as converter power limits and energy storage SOC boundaries).

[0114] These operational characteristics determine the key focus, or switching objectives, of DC microgrids when switching operating modes. For example, voltage fluctuation characteristics support the goal of voltage stability during the switching process, power flow characteristics support the goal of optimal switching cost, and tolerance characteristics support the goal of no component damage during switching. Switching objectives must be set in accordance with the operational characteristics of the DC microgrid to be feasible. Here, switching objectives can be understood as the requirements, standards, or key considerations during the DC microgrid switching process, such as cost and stability.

[0115] By considering the switching objectives during the DC microgrid switching process, this application embodiment can transform the problem of solving the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management specified target operating mode. By defining a single objective function for this multi-objective optimization problem and solving it, the final switching path from the current operating mode to the upper-level energy management specified target operating mode can be obtained.

[0116] Optionally, in one embodiment of this application, the solution for the final switching path is transformed into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management designated target operating mode, based on the switching objective. This includes: constructing a stability objective, a path length objective, and an economic objective for switching from the current operating mode to the upper-level energy management designated target operating mode; combining the stability objective, the path length objective, and the economic objective to determine the multi-objective optimization problem, so as to determine the final switching path by solving the multi-objective optimization problem. The expression for the economic objective may, but is not limited to, be: , in, For economic goals, Vertex corresponding to the running mode The path between them The instantaneous cost of mode switching. The economic cost of the time spent in the pattern; The expression for a multi-objective optimization problem can be, but is not limited to, expressed as: , , in, For reachable paths, For the set of reachable paths, This refers to the voltage fluctuation amplitude. Voltage fluctuation threshold The maximum path length. To maximize cost.

[0117] In actual implementation, the switching objectives in this application include, but are not limited to, cost objectives, path length objectives, and economic objectives during the switching process. Here, the stability objective can be understood as the stability index requirements during the operation mode switch; the economic objective can be understood as the economic cost index requirements arising from the instantaneous cost control during the operation mode switch and the economic cost index requirements resulting from the dwell time in the operation mode; and the path length objective can be understood as the path length index requirements during the switch from the current operation mode to the target operation mode.

[0118] Based on this, embodiments of this application can construct stability objectives, path length objectives, and economic objectives for switching from the current operating mode to the upper-level energy management designated target operating mode. That is, these objectives are constructed as corresponding mathematical expressions. At this point, the multi-objective optimization problem is transformed into a comprehensive optimization problem involving cost objectives, path length objectives, and economic objectives; that is, selecting the most suitable final switching path while balancing these objectives.

[0119] In this application embodiment, the stability target may be, but is not limited to, using voltage fluctuations during the operation mode switching process as a benchmark. To quantitatively evaluate the overall voltage fluctuation characteristics of each path, this application embodiment may, but is not limited to, defining paths. The voltage fluctuation amplitude is the maximum value among all edge weights of the path. Therefore, the expression for the stability objective can be, but is not limited to, the following:

[0120] in, For path The maximum value among all edge weights is also the path weight. The final voltage fluctuation amplitude.

[0121] This formula can be used to calculate the operating mode pair. (by operating mode) (vertex Switch to run mode (vertex The voltage fluctuation amplitude of each stable switching path is defined. A smaller voltage fluctuation amplitude indicates better stability of the path, while a larger voltage fluctuation amplitude indicates worse stability. The voltage fluctuation amplitude is the maximum value among all edge weights of the path, which ensures that the voltage fluctuation situation that may be encountered when switching operating modes can be fully taken into account.

[0122] Furthermore, the expression for the path length target in the embodiments of this application may, but is not limited to, be expressed as: .

[0123] The expression for the economic cost objective in the embodiments of this application may be, but is not limited to, the following:

[0124] in, The instantaneous cost of mode switching. The economic cost of the time spent in the pattern.

[0125]

[0126]

[0127]

[0128] in, For nodes Power consumption, For real-time electricity prices, The unit time operating cost of the equipment, This refers to the equipment maintenance requirement index. To maintain the cost coefficient.

[0129] Considering the three objectives of cost, path length, and economics, the multi-objective optimization problem in this embodiment can be expressed, but is not limited to, as follows:

[0130]

[0131] in, Voltage fluctuation threshold The maximum path length. To maximize cost.

[0132] Furthermore, for this convex optimization problem, embodiments of this application may, but are not limited to, use a linear scalarization method to transform it into a single objective function for solution. The single objective function may, but is not limited to, be expressed as follows:

[0133] in, , All these are weighting coefficients, which can be determined based on priority, but are not limited to, in this application embodiment. The higher the target priority, the larger the corresponding weighting coefficient. In practical applications, the specific weighting coefficients can be set by those skilled in the art according to the actual situation. This application embodiment is only for illustrative purposes and does not impose specific limitations.

[0134] The graph theory-based DC microgrid network mode switching control method proposed in this application can construct the attraction domain estimation function of the DC microgrid, generate the switching stability judgment results of all operating mode pairs, and thus construct the final directed graph model of the DC microgrid. It then solves for all stable switching paths from the current operating mode to the upper-level energy management designated target operating mode, ultimately obtaining the final switching path that meets certain conditions. This realizes a path planning method based on graph theory and the neural Lyapunov method. By constructing a directed graph model considering stability constraints, the mode switching stability problem under the upper-level energy management scheduling of the microgrid is transformed into a reachable path planning problem in graph theory. It completes the search for stable paths from the current mode to the upper-level energy management designated target mode, and uses the A* heuristic search algorithm to solve for the optimal switching path in a large-scale multi-node DC microgrid system. During the solution process, voltage fluctuations, path length, and economic efficiency are considered. Through multi-objective optimization, a linear scalarization method is used to determine the optimal switching path, ensuring that the most stable switching path that satisfies the DC microgrid switching objectives is obtained. This provides solid theoretical support and practical guidance for the stable operation of multi-node DC microgrid networks. This addresses the issue that the switching stability of multi-node DC microgrid networks is susceptible to strong coupling effects and dynamic interactions between different nodes in the network. Inappropriate switching strategies may still cause the system trajectory to deviate from the attraction domain, leading to voltage instability or even large-scale power outages. Furthermore, the nonlinear dynamic characteristics during mode switching and the interactions between multiple modes make the analysis of the stability of the operating mode switching more complex. There has always been a lack of stable control strategies for the switching of operating modes in large-scale, multi-node DC microgrids.

[0135] Next, referring to the accompanying drawings, a graph-based DC microgrid network mode switching control device according to an embodiment of this application is described.

[0136] Figure 5 This is a schematic diagram of the structure of a graph-based DC microgrid network mode switching control device according to an embodiment of this application.

[0137] like Figure 5 As shown, the graph theory-based DC microgrid network mode switching control device 10 includes: a judgment module 100, a construction module 200, and a solution module 300.

[0138] The system includes a judgment module 100, which is used to construct a basic directed graph model of a DC microgrid that meets the target scale, and to construct an attraction domain estimation function for the DC microgrid. Based on the attraction domain estimation function, it generates a switching stability judgment result for at least one path between all pairs of operating modes of the DC microgrid. The construction module 200 is used to construct a final directed graph model of the DC microgrid based on the switching stability judgment result and the basic directed graph model, and to solve for all stable switching paths from the current operating mode to the target operating mode specified by the upper-level energy management. The solution module 300 is used to solve for the final switching path from the current operating mode to the target operating mode specified by the upper-level energy management that meets the preset conditions based on all stable switching paths.

[0139] Optionally, in one embodiment of this application, the judgment module 100 includes: a first construction unit, used to construct an approximate Lyapunov function corresponding to the operation mode switching process of the DC microgrid; a second construction unit, used to construct a Lyapunov risk function corresponding to the operation mode switching process based on the positive definiteness and negative definiteness of the approximate Lyapunov function in the operation mode switching process; and a determination unit, used to construct an attraction domain estimation function based on the Lyapunov risk function, so as to determine the stability judgment basis used to generate the switching stability judgment result through the attraction domain estimation function.

[0140] Optionally, in one embodiment of this application, the expression for the Lyapunov risk function is:

[0141] in, For Lyapunov risk value / outcome, the first item The second term is the positive definite penalty function for the Lyapunov function. This is the negative definite penalty function of the Lyapunov function. and These represent the total number of training iterations and the current number of training iterations, respectively.

[0142] Optionally, in one embodiment of this application, the solution module 300 includes: a definition unit, used to define the switching objective of the DC microgrid based on the operating characteristics of the DC microgrid; a conversion unit, used to convert the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management specified target operating mode according to the switching objective; and a solution unit, used to construct a single-objective function for solving the multi-objective optimization problem, so as to solve the final switching path of switching from the current operating mode to the upper-level energy management specified target operating mode according to the single-objective function.

[0143] Optionally, in one embodiment of this application, the conversion unit includes: a construction subunit, used to construct a stable objective, a path length objective, and an economic objective for switching from the current operating mode to the upper-level energy management specified target operating mode; and a determination subunit, used to combine the stable objective, the path length objective, and the economic objective to determine a multi-objective optimization problem, so as to determine the final switching path by solving the multi-objective optimization problem.

[0144] Optionally, in one embodiment of this application, the expression for the economic objective is:

[0145] in, For economic goals, Vertex corresponding to the running mode The path between them For the instantaneous cost of mode switching, The economic cost of the time spent in the pattern; The expression for the multi-objective optimization problem is:

[0146]

[0147] in, For reachable paths, For the set of reachable paths, This refers to the voltage fluctuation amplitude. Voltage fluctuation threshold The maximum path length. To maximize cost.

[0148] It should be noted that the foregoing explanation of the graph theory-based DC microgrid network mode switching control method embodiment also applies to the graph theory-based DC microgrid network mode switching control device of this embodiment, and will not be repeated here.

[0149] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0150] When the processor 602 executes the program, it implements the graph theory-based DC microgrid network mode switching control method provided in the above embodiments.

[0151] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0152] The memory 601 is used to store computer programs that can run on the processor 602.

[0153] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0154] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0155] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0156] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0157] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the graph theory-based DC microgrid network mode switching control method described above.

[0158] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the graph theory-based DC microgrid network mode switching control method provided in this application.

[0159] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0160] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0161] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0163] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0164] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0166] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A graph theory-based DC microgrid network mode switching control method, characterized in that, Includes the following steps: A basic directed graph model of a DC microgrid that meets the target scale is constructed, and an attraction domain estimation function of the DC microgrid is constructed. Based on the attraction domain estimation function, a switching stability judgment result of at least one path between all operating mode pairs of the DC microgrid is generated. Based on the switching stability judgment results and the basic directed graph model, the final directed graph model of the DC microgrid is constructed, and all stable switching paths from the current operating mode to the target operating mode specified by the upper energy management are solved. Based on all the stable switching paths, solve for the final switching path that satisfies the preset conditions for switching from the current operating mode to the target operating mode specified by the upper-level energy management.

2. The method according to claim 1, characterized in that, The process of constructing the attraction domain estimation function for the DC microgrid, and generating a switching stability judgment result for at least one path among all operating mode pairs of the DC microgrid based on the attraction domain estimation function, includes: Construct the approximate Lyapunov function corresponding to the operation mode switching process of the DC microgrid; Based on the positive definiteness and negative definiteness of the approximate Lyapunov function in the operation mode switching process, a Lyapunov risk function corresponding to the operation mode switching process is constructed. Based on the Lyapunov risk function, the attraction domain estimation function is constructed to determine the stability judgment criteria used to generate the switching stability judgment result.

3. The method according to claim 2, characterized in that, The expression for the Lyapunov risk function is: in, For Lyapunov risk value / outcome, the first item The second term is the positive definite penalty function for the Lyapunov function. This is the negative definite penalty function of the Lyapunov function. and These represent the total number of training iterations and the current number of training iterations, respectively.

4. The method according to claim 1, characterized in that, The step of finding the final switching path from the current operating mode to the target operating mode specified by the upper-level energy management system, based on all stable switching paths and satisfying preset conditions, includes: Based on the operating characteristics of the DC microgrid, the switching target of the DC microgrid is defined; Based on the switching objective, the solution of the final switching path is transformed into a multi-objective optimization problem of switching from the current operating mode to the operating mode specified by the upper-level energy management. A single-objective function is constructed to solve the multi-objective optimization problem, and the final switching path from the current operating mode to the upper-level energy management specified target operating mode is solved based on the single-objective function.

5. The method according to claim 4, characterized in that, The step of transforming the solution of the final switching path into a multi-objective optimization problem of switching from the current operating mode to the upper-level energy management specified target operating mode, based on the switching objective, includes: Construct a stability target, a path length target, and an economic target for switching from the current operating mode to the specified target operating mode of the upper-level energy management; The multi-objective optimization problem is determined by combining the stability objective, path length objective, and economic objective, and the final switching path is determined by solving the multi-objective optimization problem.

6. The method according to claim 5, characterized in that, The expression for the economic objective is: in, For economic goals, Vertex corresponding to the running mode The path between them For the instantaneous cost of mode switching, The economic cost of the time spent in the pattern; The expression for the multi-objective optimization problem is: in, For reachable paths, For the set of reachable paths, This refers to the voltage fluctuation amplitude. Voltage fluctuation threshold The maximum path length. To maximize cost.

7. A graph theory-based DC microgrid network mode switching control device, characterized in that, include: The judgment module is used to construct a basic directed graph model of a DC microgrid that meets the target scale, and to construct an attraction domain estimation function of the DC microgrid, so as to generate a switching stability judgment result of at least one path between all operating mode pairs of the DC microgrid based on the attraction domain estimation function. The construction module is used to construct the final directed graph model of the DC microgrid based on the switching stability judgment result and the basic directed graph model, and solve all stable switching paths from the current operating mode to the target operating mode specified by the upper energy management. The solution module is used to solve for the final switching path that meets preset conditions for switching from the current operating mode to the target operating mode specified by the upper-level energy management, based on all the stable switching paths.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the graph theory-based DC microgrid network mode switching control method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the graph theory-based DC microgrid network mode switching control method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the graph theory-based DC microgrid network mode switching control method as described in any one of claims 1-6.

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