Micro-grid dynamic power distribution method and device

Abstract

The embodiment of the invention discloses a micro-grid dynamic power distribution method and device. The method comprises the steps that real-time operation data and topological structure data of a micro-grid are acquired; performing difference calculation on the real-time operation data to obtain power change data; according to the topological structure data, analyzing the connection relationship between the nodes and the edges through a graph theory algorithm to obtain power transmission path data; constructing a fractional order reaction diffusion model, wherein a reaction term is determined based on the power change data; a diffusion term is determined based on power transmission path data, and the long-range diffusion behavior of the power in the non-uniform topology is described through a fractional order partial differential equation; solving the fractional order reaction diffusion model, and generating space-time dynamic distribution of micro-grid power distribution; and according to the space-time dynamic distribution, dynamically adjusting a charging and discharging strategy of the energy storage equipment and power grid topology configuration, and obtaining an optimized power distribution strategy. According to the invention, the operation efficiency and stability of the micro-grid are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power systems, in particular to a micro-grid dynamic power distribution method and device. BACKGROUND

[0002] With the widespread application of renewable energy, micro-grid as a flexible distributed energy system has gradually become an important part of energy management. Micro-grid usually includes renewable energy generation equipment (such as solar energy, wind energy), energy storage equipment (such as batteries) and various loads, with high dynamicity and complex topology. However, due to the intermittency of renewable energy and the fluctuation of load demand, the power distribution of micro-grid faces challenges, and traditional methods are difficult to effectively cope with non-uniform topology and long-range power diffusion behavior.

[0003] Therefore, there is an urgent need for a dynamic power distribution method that can comprehensively consider micro-grid operation data and topology structure to improve the efficiency and stability of the system. SUMMARY

[0004] The application provides a micro-grid dynamic power distribution method and device, which improves the operation efficiency and stability of the micro-grid.

[0005] The application provides the following solutions:

[0006] According to a first aspect, a micro-grid dynamic power distribution method is provided, the method comprising: obtaining real-time operation data and topology structure data of a micro-grid, the real-time operation data including output power of renewable energy generation equipment, energy storage equipment state and load demand, and the topology structure data including connection relationship of nodes and edges; performing difference calculation on the real-time operation data to obtain power change data; analyzing the connection relationship of the nodes and edges according to the topology structure data through a graph theory algorithm to obtain power transmission path data; constructing a fractional order reaction diffusion model, wherein the reaction term is determined based on the power change data; the diffusion term is determined based on the power transmission path data, and the long-range diffusion behavior of power in non-uniform topology is described by a fractional order partial differential equation; solving the fractional order reaction diffusion model to generate a spatio-temporal dynamic distribution of micro-grid power distribution; dynamically adjusting the charging and discharging strategy of the energy storage equipment and the power grid topology configuration according to the spatio-temporal dynamic distribution to obtain an optimized power distribution strategy.

[0007] According to an implementable manner in the embodiments of the present application, the power transmission path data is obtained by analyzing the connection relationship of the nodes and edges according to the topological structure data through a graph theory algorithm, including: constructing a weighted directed graph model of the microgrid based on the topological structure data, wherein the nodes represent power generation equipment, energy storage equipment and loads, and the edges represent power transmission lines, and the weight of the edge is determined based on the impedance and power loss of the line; applying the Dijkstra algorithm to the weighted directed graph to calculate the shortest power transmission path from the power generation equipment to the load node; and determining the priority order of power distribution according to the shortest power transmission path, and generating a topological matrix of the power transmission path.

[0008] According to an implementable manner in the embodiments of the present application, the reaction term is determined based on the power change data, including: constructing a fractional order kinetics model of the reaction term by using Caputo fractional order derivative definition; determining the parameters of the fractional order kinetics model by numerical fitting according to the power change data, and generating a specific fractional order expression of the reaction term.

[0009] According to an implementable manner in the embodiments of the present application, the diffusion term is determined based on the power transmission path data, and the long-range diffusion behavior of power in a non-uniform topology is described by a fractional order partial differential equation, including: constructing a fractional order partial differential equation of the diffusion term by using Riesz fractional order derivative definition; determining the diffusion coefficient of the fractional order partial differential equation according to the topological matrix; and generating a mathematical expression of the diffusion term in combination with the fractional order partial differential equation.

[0010] According to an implementable manner in the embodiments of the present application, solving the fractional order reaction-diffusion model to generate the spatiotemporal dynamic distribution of the microgrid power distribution includes: discretizing the fractional order reaction-diffusion model into a numerical calculation format, and using a finite difference method to process the fractional order derivative in the time domain and the space domain; applying a fast Fourier transform algorithm to accelerate the numerical solution of the fractional order reaction-diffusion model, and generating the spatiotemporal dynamic distribution of the power distribution.

[0011] According to an implementable manner in the embodiments of the present application, when solving the fractional order reaction-diffusion model, the grid resolution of the numerical solution is adaptively adjusted according to the number of nodes and the topological complexity of the microgrid.

[0012] According to an implementable manner in the embodiments of the present application, the dynamic adjustment of the charging and discharging strategy of the energy storage device and the power grid topology configuration according to the spatiotemporal dynamic distribution comprises: analyzing the spatiotemporal dynamic distribution, extracting the power distribution peak value and fluctuation trend of each node in the microgrid; based on the power distribution peak value and fluctuation trend, optimizing the charging and discharging time window of the energy storage device, determining the charging and discharging rate of each energy storage device; according to the power distribution value in the spatiotemporal dynamic distribution, dynamically adjusting the power transmission path through the optimization algorithm of minimizing power loss, updating the power grid topology configuration.

[0013] According to a second aspect, a microgrid dynamic power distribution device is provided, comprising: a power grid data acquisition unit configured to acquire real-time operation data and topology structure data of a microgrid, the real-time operation data comprising output power of renewable energy power generation equipment, energy storage device state and load demand, and the topology structure data comprising connection relationship of nodes and edges; a power grid data analysis unit configured to perform difference calculation on the real-time operation data to obtain power change data; according to the topology structure data, analyze the connection relationship of the nodes and edges through a graph theory algorithm to obtain power transmission path data; a reaction diffusion model construction unit configured to construct a fractional order reaction diffusion model, wherein the reaction term is determined based on the power change data; the diffusion term is determined based on the power transmission path data, and the long-range diffusion behavior of power in a non-uniform topology is described by a fractional order partial differential equation; a spatiotemporal dynamic analysis unit configured to solve the fractional order reaction diffusion model to generate a spatiotemporal dynamic distribution of microgrid power distribution; an optimization strategy generation unit configured to dynamically adjust the charging and discharging strategy of the energy storage device and the power grid topology configuration according to the spatiotemporal dynamic distribution to obtain an optimized power distribution strategy.

[0014] According to a third aspect, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any one of the above first aspect.

[0015] According to a fourth aspect, an electronic device is provided, comprising: one or more processors; and a memory associated with the one or more processors, the memory being configured to store program instructions that, when executed by the one or more processors, perform the steps of the method of any one of the above first aspect.

[0016] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0017] The application realizes the space-time dynamic optimization of power distribution by acquiring real-time operation data and topology structure data, combining graph theory algorithm and fractional order reaction diffusion model. The method can accurately describe the long-range diffusion behavior of power in non-uniform topology, dynamically adjust the charging and discharging strategy of energy storage equipment and the configuration of power grid topology, effectively reduce power loss, and improve the operation efficiency and stability of the microgrid.

[0018] Of course, implementing any product of the present application does not necessarily require all the advantages described above to be achieved at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 System architecture diagram applicable to the embodiments of the present application;

[0021] Figure 2 Flowchart of the microgrid dynamic power distribution method provided by the embodiments of the present application;

[0022] Figure 3 Structure block diagram of the microgrid dynamic power distribution device provided by the embodiments of the present application;

[0023] Figure 4 Schematic block diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0025] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0026] It should be understood that the term "and / or" as used herein merely describes an associated relationship, that is, there can be three cases, for example, A and / or B, which can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0027] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted as meaning "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".

[0028] There are currently some micro-grid power distribution techniques, for example, optimizing the power distribution of the micro-grid by linear programming or mixed integer linear programming. However, the linear optimization method usually assumes that the system operating state and topology structure is static or approximately linear, and cannot effectively capture the intermittent fluctuations of renewable energy generation (such as wind energy, solar energy) and the dynamic changes of load demand, resulting in that the power distribution scheme may deviate from the optimal state in actual operation. This method is usually based on a simplified topology model, and it is difficult to handle the long-range power diffusion behavior in the non-uniform topology structure (such as the complex connection relationship of nodes and edges) in the micro-grid, which may cause uneven power distribution or line overload.

[0029] In view of this, the present application provides a new idea. In order to facilitate the understanding of the present application, first, the system architecture based on the present application is described. Figure 1 An exemplary system architecture to which embodiments of the present application can be applied is shown, as shown in Figure 1 The system architecture can include a user device and a micro-grid dynamic power distribution device located at the server end.

[0030] The user can input real-time operating data and topology structure data through the user device, and the user device sends it to the micro-grid dynamic power distribution device at the server end. The micro-grid dynamic power distribution device can use the method provided in the embodiments of the present application to obtain the power distribution strategy.

[0031] The user equipment can include, but is not limited to, a smart mobile terminal, a smart home device, a wearable device, a PC (Personal Computer), and the like. The smart mobile terminal can include a mobile phone, a tablet computer, a notebook computer, a PDA (Personal Digital Assistant), an Internet car, and the like. The smart home device can include a smart television, a smart refrigerator, and the like. The wearable device can include a smart watch, smart glasses, a virtual reality device, an augmented reality device, a mixed reality device, and the like.

[0032] The micro-grid dynamic power distribution device can be set as an independent server, a server group, or a cloud server. The cloud server, also known as a cloud computing server or a cloud host, is a host product in a cloud computing service system, which solves the defects of large management difficulty and weak service scalability in traditional physical hosts and VPS (Virtual Private Server) services. In addition to the architecture shown in the figure, the micro-grid dynamic power distribution device can also be set in a computer terminal with strong computing power. Figure 1

[0033] It should be understood that Figure 1 The user equipment and the micro-grid dynamic power distribution device in the figure are only illustrative. According to the implementation needs, there can be any number of user equipment and micro-grid dynamic power distribution devices.

[0034] Figure 2 The micro-grid dynamic power distribution method flowchart provided by the embodiment of the present application can be executed by the micro-grid dynamic power distribution device in the system shown in the figure. As shown in the figure, the method can include the following steps: Figure 1 Figure 2

[0035] Step 201: Obtain real-time operation data and topology structure data of the micro-grid, the real-time operation data including output power of renewable energy power generation equipment, energy storage equipment state and load demand, and the topology structure data including connection relationship of nodes and edges.

[0036] Step 202: Perform difference calculation on the real-time operation data to obtain power change data; and analyze the connection relationship of nodes and edges according to the topology structure data through a graph theory algorithm to obtain power transmission path data.

[0037] Step 203: Construct a fractional order reaction diffusion model, wherein the reaction term is determined based on the power change data of the operation data; and the diffusion term is determined based on the power transmission path data in the topology structure data, and the long-range diffusion behavior of power in a non-uniform topology is described by a fractional order partial differential equation. ​​​

[0038] Step 204: Solving the fractional-order reaction-diffusion model to generate the spatiotemporal dynamic distribution of microgrid power distribution.

[0039] Step 205: Dynamically adjusting the charging and discharging strategies of energy storage devices and the configuration of power grid topology based on the spatiotemporal dynamic distribution to obtain an optimized power distribution strategy.

[0040] As can be seen from the above process, by obtaining real-time operation data and topology structure data, combining graph theory algorithm and fractional-order reaction-diffusion model, the spatiotemporal dynamic optimization of power distribution is realized. This method can accurately describe the long-range diffusion behavior of power in non-uniform topology, dynamically adjust the charging and discharging strategies of energy storage devices and the configuration of power grid topology, effectively reduce power loss, and improve the operation efficiency and stability of microgrid.

[0041] The following will describe in detail the steps in the above process and the effects that can be further produced by combining embodiments. First, the above step 201, i.e., "obtaining real-time operation data and topology structure data of microgrid, the real-time operation data including output power of renewable energy generation device, state of energy storage device and load demand, and the topology structure data including connection relationship of nodes and edges", will be described in detail.

[0042] Real-time operation data mainly covers the running state information of core devices in microgrid, including the output power of renewable energy generation device, the state of energy storage device and the load demand. These data reflect the dynamic operation characteristics of microgrid, providing necessary basis for analyzing power supply and demand balance and system stability. Topology structure data describes the physical and electrical connection relationship of microgrid, including the connection relationship of nodes and edges, which represents the network structure between power generation devices, energy storage devices, loads and power transmission lines. The combination of these two types of data lays a foundation for building an accurate power distribution model.

[0043] The acquisition of real-time operation data involves the state monitoring of multiple devices. The output power of renewable energy generation devices, such as the real-time power generation of solar photovoltaic panels or wind turbines, is significantly affected by external factors such as weather, light or wind speed. Through sensors and data acquisition systems, the power output data of these devices can be recorded in real time. The state of energy storage devices usually includes the state of charge, remaining capacity and charging and discharging capacity of the battery, which reflects the available resources of energy storage devices in power regulation. Load demand refers to the real-time power demand of various electrical devices in microgrid, which may change due to time, user behavior or industrial activities. The process of obtaining these data usually relies on smart meters, SCADA systems or other Internet of Things devices to ensure high-frequency updating and accuracy of data.

[0044] Topology data acquisition focuses on the network structure analysis of the microgrid. Nodes typically represent key devices or connection points in the microgrid, such as power generation devices, energy storage devices, load points, or substations. Edges represent the power transmission lines between these nodes, embodying the physical path of electrical energy from the power generation end to the load end. Topology data not only includes the basic connection relationship of nodes and edges, but also may involve the electrical characteristics of the lines, such as impedance, power loss, or transmission capacity. These data can be obtained through the topology analysis tools of the power grid management system or geographic information system, used to build the network model of the microgrid. The accuracy of topology data is crucial for analyzing power transmission paths and optimizing power distribution.

[0045] The following describes the step 202, i.e., "differential calculation on the real-time operation data to obtain power change data; according to the topology data, analyze the connection relationship of nodes and edges through graph theory algorithm to obtain power transmission path data", in detail in combination with embodiments.

[0046] First, the purpose of differential calculation on real-time operation data is to quantify the dynamic change trend of power in the microgrid. Real-time operation data includes the output power of renewable energy power generation devices, the state of energy storage devices, and load demand. These data are usually collected in the form of time series, reflecting the power supply and demand state of the microgrid at different times. Differential calculation calculates the power change rate or change amount by comparing the data of adjacent time points, such as sudden increase of power generation, fluctuation of load demand, or change of energy storage device charging and discharging rate. These power change data can capture the dynamic characteristics of microgrid operation, providing a basis for the construction of subsequent reaction terms. For example, when the solar power generation power rapidly decreases due to cloud cover, differential calculation can quantify this change, helping the system to adjust the power distribution strategy in time.

[0047] The specific implementation of differential calculation is usually based on numerical methods, such as using time step to perform first-order or high-order differential calculation on power data to generate time series data of power change. These data not only reflect the instantaneous change of power, but also reveal the trend and periodic characteristics of the change, such as daily peak of load demand or intermittent fluctuation of renewable energy. Through analysis of these data, the system can identify potential bottlenecks or imbalance points in power distribution, providing a basis for optimizing energy storage device and power grid topology configuration.

[0048] Secondly, the connection relationship of nodes and edges is analyzed based on the topology data through graph theory algorithm, aiming to generate power transmission path data of the microgrid. The topology data describes the connection relationship of nodes (such as power generation equipment, energy storage equipment, and load points) and edges (such as power transmission lines) in the microgrid, forming a network model of the microgrid. The graph theory algorithm determines the optimal transmission path of electric energy from the power generation end to the load end by analyzing this network model. The graph theory algorithm can use Dijkstra algorithm or Floyd-Warshall algorithm, which can calculate the shortest or optimal path based on the weight of the edge (such as line impedance, power loss or transmission capacity). The generated power transmission path data is represented in the form of a topology matrix or a path list, clearly showing the priority order of power distribution and the selection of transmission lines.

[0049] As an implementable way, according to the topology data, the connection relationship of the nodes and edges is analyzed by the graph theory algorithm to obtain the power transmission path data, including: based on the topology data, a weighted directed graph model of the microgrid is constructed, wherein the nodes represent power generation equipment, energy storage equipment and load, and the edges represent power transmission lines, and the weight of the edge is based on the impedance and power loss of the line; Dijkstra algorithm is applied to the weighted directed graph to calculate the shortest power transmission path from the power generation equipment to the load node; according to the shortest power transmission path, the priority order of power distribution is determined, and a topology matrix of the power transmission path is generated.

[0050] Firstly, the weighted directed graph model of the microgrid is constructed based on the topology data, which is the basis for analyzing the power transmission path. The topology data describes the connection relationship of nodes and edges in the microgrid, where nodes represent power generation equipment, energy storage equipment and load points, and edges represent power transmission lines connecting these nodes. The weighted directed graph model abstracts the microgrid as a mathematical graph structure, where each node corresponds to a physical entity in the microgrid, and each edge corresponds to a power transmission line. The weight of the edge is determined based on the impedance and power loss of the line, where impedance reflects the electrical characteristics of the line, and power loss is directly related to transmission efficiency. This model can accurately represent the topological complexity of the microgrid and provide a structured data foundation for subsequent path optimization.

[0051] After constructing the weighted directed graph, Dijkstra's algorithm is applied to calculate the shortest power transmission paths from the generation devices to the load nodes. Dijkstra's algorithm is a classic graph theory algorithm suitable for finding the shortest paths from a single source node to other nodes in a weighted directed graph. In this invention, the generation devices are considered as source nodes and the load nodes as target nodes, and the algorithm calculates the optimal paths for power transmission based on the edge weights. The definition of the shortest path not only considers the physical distance but also integrates the line impedance and power loss, ensuring the minimization of losses during power transmission. For example, in a multi-node microgrid, the algorithm may choose a path with lower impedance and smaller loss, avoiding overloaded or inefficient lines, thereby improving the efficiency of power distribution.

[0052] According to the calculated shortest power transmission paths, the priority order of power distribution is further determined, and a topology matrix of the power transmission paths is generated. The priority order reflects the order of power flow from different generation devices to load nodes, and usually the path with low loss and high transmission efficiency is preferred. The topology matrix is a mathematical representation form used to store the connection relationship and weight information of the power transmission paths. Each item of the matrix may represent the path length, impedance or other electrical characteristics between nodes, providing key inputs for the subsequent fractional-order reaction-diffusion model. The generation of the topology matrix enables the network structure of the microgrid to be analyzed and processed in a standardized form, facilitating the dynamic adjustment of power distribution strategies.

[0053] The application of graph theory algorithms fully considers the non-uniform topological characteristics of the microgrid. For example, in a complex microgrid, some nodes may be connected to other nodes through multiple lines, and the electrical characteristics of each line are different. The graph theory algorithm optimizes the power transmission path by analyzing these connection relationships, reduces power loss and avoids line overload. In addition, the power transmission path data not only provides spatial basis for power distribution, but also provides key information for the construction of subsequent diffusion terms, enabling the fractional-order reaction-diffusion model to accurately describe the long-range diffusion behavior of power in non-uniform topology.

[0054] The following describes the above step 203, i.e., "constructing a fractional-order reaction-diffusion model, wherein the reaction term is determined based on the power change data, and the diffusion term is determined based on the power transmission path data, describing the long-range diffusion behavior of power in non-uniform topology through a fractional-order partial differential equation", in detail in combination with embodiments.

[0055] Fractional reaction-diffusion modeling is a mathematical modeling approach used to describe the dynamic evolution of matter or energy in complex systems. It combines reaction terms and diffusion terms, and introduces fractional derivatives to capture non-local and non-linear characteristics. This model takes into account the dynamic characteristics of power variation and the influence of non-uniform topology of microgrid on power transmission. The reaction term and the diffusion term represent the local variation and global diffusion behavior of power, respectively. The reaction term is determined based on the power variation data of real-time operation data, capturing the dynamic fluctuations of power supply and demand. The diffusion term is determined based on the power transmission path data in the topology structure data, using fractional partial differential equations to describe the long-range diffusion behavior of power in complex networks. This modeling approach can effectively handle the non-linear characteristics and topological complexity of microgrids, providing a scientific theoretical basis for optimizing power distribution.

[0056] The construction of the reaction term is based on the power variation data of real-time operation data, aiming to describe the local dynamic evolution of power in the microgrid. As an implementable way, the reaction term is determined based on the power variation data of the operation data, including: using Caputo fractional derivative definition to construct a fractional-order dynamic model of the reaction term; according to the power variation data, the parameters of the fractional-order dynamic model are determined by numerical fitting to generate a specific fractional-order expression of the reaction term.

[0057] Caputo fractional derivative is a commonly used fractional calculus tool, especially suitable for describing dynamic systems with memory effect. Unlike traditional integer-order derivatives, Caputo fractional derivative can capture the historical dependence of system state, i.e., the current power variation depends not only on instantaneous data but also on data in the past period of time. This feature is particularly important in microgrids, as the intermittency of renewable energy and the fluctuation of load demand often have long-term correlation. By introducing Caputo fractional derivative, the dynamic model of the reaction term can more accurately describe the non-linear characteristics of power variation, such as periodicity or abruptness of power fluctuations, thereby improving the adaptability of the model to complex dynamic scenarios.

[0058] Determining the parameters of the fractional-order dynamic model by numerical fitting according to the power variation data is a key step in generating the specific expression of the reaction term. The numerical fitting process usually involves matching the power variation data with the fractional-order dynamic model, and determining the key parameters in the model, such as the order of fractional derivative and the coefficients of reaction term, through optimization algorithms (such as least squares method or Bayesian estimation). These parameters reflect the dynamic characteristics of power variation, such as fluctuation frequency, amplitude or decay rate. The fitting process needs to ensure that the model can accurately reproduce the trend of power variation data while maintaining computational efficiency. The final generated fractional-order expression of the reaction term is a mathematical formula that explicitly describes the dynamic evolution law of power variation, providing a basis for the overall solution of the fractional reaction-diffusion model.

[0059] Specifically, assume the real-time operation data of the microgrid includes a power time series P(t), representing the power value (such as renewable energy power generation or load demand) at a certain time t. The power change data is obtained by difference calculation:

[0060]

[0061] where Δt is the time step, and ΔP(t) represents the power change rate, reflecting the dynamic fluctuations of power.

[0062] The reaction term is defined based on Caputo fractional derivative, describing the nonlinear dynamic behavior of power change. Assuming that the reaction term R(P(t)) represents the local dynamics of power change, it can be represented by the following fractional dynamics equation:

[0063]

[0064] where k1, k2 are the coefficients of the reaction term, representing the weights of the linear term and the fractional term, respectively; is the Caputo fractional derivative, defined as:

[0065]

[0066] where τ is the gamma function, and α is the order of the fractional derivative, is the first-order derivative of power.

[0067] According to the power change data ΔP(t), the model parameters k1, k2, α are determined by numerical fitting. Assuming that the time series data The objective is to minimize the error between the model prediction and the actual data. Define the error function:

[0068]

[0069] Use optimization algorithms (such as gradient descent or least squares) to solve:

[0070]

[0071] is the Caputo fractional derivative Numerical methods (such as L1 discretization) can be used to approximate:

[0072]

[0073] Through iterative optimization, the parameters α * are obtained.

[0074] The diffusion term is constructed based on power transmission path data in the topology data, for describing long-range diffusion behavior of power in the heterogeneous topology of the microgrid. As an implementable way, the diffusion term is determined based on the power transmission path data in the topology data, and the long-range diffusion behavior of power in the heterogeneous topology is described by a fractional order partial differential equation, comprising: constructing a fractional order partial differential equation of the diffusion term by using a Riesz fractional derivative definition; determining a diffusion coefficient of the fractional order partial differential equation according to the topology matrix; and generating a mathematical expression of the diffusion term in combination with the fractional order partial differential equation.

[0075] Firstly, the diffusion term is constructed based on power transmission path data in the topology data, for describing spatial propagation behavior of power in the microgrid network. The fractional order partial differential equation of the diffusion term is constructed by using a Riesz fractional derivative definition, which is the core step of the technical feature. The Riesz fractional derivative is a mathematical tool suitable for describing spatial non-local effects, which can capture the propagation behavior of power across multiple nodes in the microgrid. Unlike traditional integer order derivatives, the Riesz fractional derivative represents the non-locality of power diffusion through a fractional order operator, i.e. the power distribution of a node is not only affected by neighboring nodes, but also indirectly affected by distant nodes. This feature is particularly suitable for the heterogeneous topology of the microgrid, because power transmission may pass through multiple paths or cross multiple nodes, forming a long-range diffusion effect.

[0076] The general form of the fractional order partial differential equation is:

[0077]

[0078] where D is the diffusion coefficient, is the Riesz fractional derivative, β is the fractional order, and P(x, t) represents the power distribution at position x and time t.

[0079] Determining the diffusion coefficient of the fractional order partial differential equation according to the topology matrix is the key to ensuring that the model is consistent with the physical characteristics of the microgrid. The topology matrix provides a quantitative description of the connection relationship between nodes, including the weights of edges such as line impedance and power loss. The diffusion coefficient D is not a fixed value, but is dynamically adjusted according to the topology matrix. For example, for paths with lower impedance and smaller loss, the diffusion coefficient may be larger, reflecting the higher efficiency of power propagation; for high impedance paths, the diffusion coefficient is smaller, reflecting the limited propagation. This dynamic adjustment enables the diffusion term to accurately reflect the topological characteristics of the microgrid, ensuring that the model is consistent with the actual power transmission behavior. The determination of the diffusion coefficient is usually completed by weight analysis of the topology matrix, such as by matrix element normalization or weighted average calculation.

[0080] The mathematical expression of the diffusion term is generated by combining the fractional partial differential equation, which is the process of integrating the above steps into a specific model. The specific form of the diffusion term is: where the Riesz fractional derivative can be calculated by numerical methods such as discretization or Fourier transform, and the diffusion coefficient D is determined by the topology matrix. For example, if the topology matrix analysis shows that the weight of a certain path is high, the diffusion coefficient D increases accordingly, and the generated diffusion term expression can reflect the rapid propagation of power on this path.

[0081] Specifically, assuming that the microgrid has N nodes (including power generation equipment, energy storage equipment and loads), the topology matrix has elements a ij representing the weight of the power transmission path from node i to node j. The weight is usually based on the impedance Z ij and power loss L ij of the line, defined as:

[0082]

[0083] where κ is the normalization coefficient, used to balance the influence of impedance and power loss. If there is no direct connection between nodes i and j, a ij = 0.

[0084] Define the local diffusion coefficient D ij for each node pair (i, j), the initial value can be set as:

[0085]

[0086] This formula normalizes the weight a ij , ensuring that the diffusion coefficient is proportional to the relative transmission efficiency of the path.

[0087] This expression, combined with the reaction term, constitutes a complete fractional reaction-diffusion model for describing the spatiotemporal distribution of power. The reaction term R(P(t)) as part of the fractional reaction-diffusion model, combined with the diffusion term, constitutes a complete power distribution model:

[0088]

[0089] where D is the diffusion coefficient, is the Riesz fractional derivative, describing the spatial diffusion behavior of power. The reaction term R(P(t)) provides the driving force for local power change.

[0090] In order to adapt to the non-uniform topology characteristics of the microgrid, the diffusion coefficient D ijThe diffusion coefficient needs to be dynamically adjusted according to the topology matrix. Assuming that the power transmission priority of the microgrid is determined by the shortest path, the diffusion coefficient can be further optimized by the eigenvalue of the topology matrix or the path weight distribution. The specific method is as follows:

[0091] Calculate the degree matrix D of the topology matrix deg , where the diagonal elements represent the total connection strength of node i;

[0092] Construct the Laplacian matrix L=D deg -A, whose eigenvalues reflect the connectivity of the network;

[0093] According to the largest eigenvalue λ max of the Laplacian matrix, adjust the diffusion coefficient:

[0094]

[0095] This adjustment makes the diffusion coefficient associated with the global topological characteristics (such as connectivity) of the network, ensuring that the power diffusion reflects the structural characteristics of the entire microgrid.

[0096] Apply the diffusion coefficient D ij to the diffusion term of the fractional partial differential equation. Assuming that the power distribution P(x,t) is represented on a discretized spatial grid, the diffusion term is:

[0097]

[0098] where β is the order of the Riesz fractional derivative, |x j -x i | represents the topological distance between nodes (which can be derived from the topology matrix).

[0099] The step 204, i.e., "solving the fractional reaction-diffusion model to generate the spatiotemporal dynamic distribution of microgrid power allocation", will be described in detail below in combination with embodiments.

[0100] The solving process first needs to discretize the fractional-order reaction-diffusion model into a format suitable for numerical calculation. Since the model contains fractional-order derivatives, direct solving is complex, so the finite difference method is used to discretize the fractional-order derivatives in the time domain and the spatial domain. In order to improve the solving efficiency, the present application applies the fast Fourier transform algorithm to accelerate the numerical solution of the fractional-order reaction-diffusion model. Fast Fourier transform is particularly suitable for processing non-local operators in the spatial domain, and can significantly reduce the computational complexity of fractional-order derivatives. For example, in the spatial domain, the convolution operation involved in the Riesz fractional-order derivative can be transformed into frequency domain calculation through fast Fourier transform, thereby greatly reducing the calculation time. The solving result is the spatio-temporal dynamic distribution of power allocation, usually represented in matrix or grid form, describing the power value of each node at different time points. This distribution not only reflects the balance between supply and demand of power, but also reveals the propagation path and fluctuation trend of power in the microgrid network.

[0101] As an implementable way, solving the fractional-order reaction-diffusion model to generate the spatio-temporal dynamic distribution of microgrid power allocation includes: discretizing the fractional-order reaction-diffusion model into a numerical calculation format, and using the finite difference method to process the fractional-order derivatives in the time domain and the spatial domain; applying the fast Fourier transform algorithm to accelerate the numerical solution of the fractional-order reaction-diffusion model, and generating the spatio-temporal dynamic distribution of power allocation.

[0102] Specifically, first, the solving of the fractional-order reaction-diffusion model needs to convert the continuous mathematical model into a discrete format suitable for computer calculation. The discretization process divides the time domain and the spatial domain into discrete grids, such as time step Δt, and spatial nodes are divided based on the topology structure of the microgrid. The discretized model can represent the power P(x i ,t j ) in matrix form, where x i represents the node position in the microgrid, t j represents the time point, so as to facilitate numerical solution.

[0103] In the discretization process, the finite difference method is used to process the fractional-order derivatives in the time domain and the spatial domain. For Caputo fractional-order derivative in the time domain, L1 method is commonly used for discretization. L1 method converts Caputo derivative into weighted sum form by approximating integral form, calculating the historical dependence of power change. For example, for Caputo derivative of order α, the discretization formula can be expressed as:

[0104]

[0105] For Riesz fractional derivatives in spatial domain, discretization is based on a topological matrix that considers connection weights and distances between nodes to approximate the spatial diffusion behavior of power. This finite difference method preserves the non-local property of fractional derivatives and ensures that the model accurately describes the dynamic evolution of power in microgrids.

[0106] To improve the solving efficiency, the application adopts a fast Fourier transform algorithm to accelerate the numerical solution of the fractional reaction-diffusion model. The calculation of fractional derivatives involves convolution operations, especially for Riesz derivatives in the spatial domain, which have high computational complexity. Fast Fourier transform significantly reduces the computational load by converting spatial domain convolution operations into frequency domain multiplication operations. For example, the discrete form of Riesz fractional derivative can be implemented by fast Fourier transform:

[0107]

[0108] where and are the Fourier transform and inverse transform, respectively, and ω is the frequency domain variable. This method converts non-local operators in the spatial domain to local operations in the frequency domain, greatly improving the solving speed while maintaining the calculation accuracy.

[0109] The solution is the spatiotemporal dynamic distribution of power allocation, usually represented as a two-dimensional matrix, recording the power values of each node in the microgrid at different time points. This distribution reflects the dynamic fluctuations of power over time and the propagation path in space, such as identifying peak areas of power demand or bottleneck points of transmission lines. The spatiotemporal dynamic distribution provides key information for optimizing microgrid operation, such as guiding energy storage devices to discharge during peak times or charge during off-peak times, adjusting power transmission paths to reduce losses. Through efficient numerical solution, the application can generate accurate distribution data in real time to adapt to the dynamic changes of the microgrid.

[0110] Preferably, when solving the fractional reaction-diffusion model, the application adaptively adjusts the grid resolution of the numerical solution according to the number of nodes and the complexity of the topology of the microgrid, thereby ensuring the calculation accuracy of the spatiotemporal dynamic distribution.

[0111] Solving fractional-order reaction-diffusion models involves discretizing continuous partial differential equations into numerical schemes, usually dealing with fractional derivatives in time and space domains through finite difference methods. Grid resolution refers to the fineness of time steps and spatial node divisions, directly affecting the accuracy and computational complexity of the solution. In microgrids, the number of nodes and topology complexity vary greatly, for example, small microgrids may have only a few nodes and simple connections, while large microgrids may contain dozens of nodes and complex multi-path topologies. Fixed grid resolution may lead to waste of computational resources for small grids or insufficient accuracy for large grids. Therefore, adaptive adjustment of grid resolution can optimize the solution process according to the actual microgrid characteristics.

[0112] The specific implementation of adaptive adjustment is based on the evaluation of the number of nodes and topology complexity of the microgrid. The number of nodes directly determines the scale of spatial domain discretization, for example, a microgrid with more nodes requires denser spatial grids to capture the subtle changes of power among nodes. Topology complexity is determined through topology matrix analysis, for example, by calculating the eigenvalues of the Laplacian matrix or the weight distribution of edges, to evaluate the connectivity and non-uniformity of the network. For high complexity topologies, finer grids may be needed to accurately describe the long-range diffusion behavior of power; for simple topologies, the grid resolution can be appropriately relaxed to reduce computational load. The adjustment of time domain step size considers the dynamic characteristics of power changes, for example, smaller step size is used during periods of intense power fluctuations to improve accuracy.

[0113] The process of adaptive adjustment of grid resolution is usually implemented in combination with numerical optimization algorithms. For example, a target function can be defined to consider both computational accuracy and computational time, dynamically optimizing the time step Δt and spatial grid interval Δx. Specific methods may include:

[0114] According to the number of nodes N, the spatial grid interval is initially set Ensure that each node has enough discrete points for representation;

[0115] According to the eigenvalues of the topology matrix (such as the largest eigenvalue λ max ), adjust the spatial grid density, for example, increase the number of grid points when λ max is large (indicating high connectivity);

[0116] For time step, according to the fluctuation frequency of power change data (obtained through Fourier analysis), decrease Δt during high-frequency fluctuations and increase Δt during stable periods.

[0117] The following describes the step 205, i.e. "according to the spatio-temporal dynamic distribution, dynamically adjusting the charging and discharging strategy of the energy storage device and the configuration of the power grid topology, obtaining the optimized power distribution strategy", in detail with examples.

[0118] By analyzing the spatiotemporal dynamic distribution, key features in the microgrid operation can be identified, such as high-load areas, power fluctuation trends, or potential line overload points. This information provides a data foundation for optimizing the charging and discharging strategies of energy storage devices and the configuration of the power grid topology, enabling the adjustment strategies to accurately respond to the actual operating state of the microgrid.

[0119] Dynamic adjustment of the charging and discharging strategies of energy storage devices is based on the analysis results of spatiotemporal dynamic distribution, optimizing the operation of energy storage devices to balance power supply and demand. The spatiotemporal dynamic distribution can reveal the peak and trough periods of power demand, for example, when solar power generation decreases or load demand surges, the energy storage device needs to be quickly discharged to make up for the power gap; when the power generation is sufficient or the load is low, it is preferred to charge to reserve energy. By analyzing the power peaks and fluctuation trends in the distribution, the charging and discharging time window and rate of energy storage devices can be determined. For example, if the power demand of a certain node surges in a certain period, the system can dispatch the energy storage device to operate at a high discharge rate, while avoiding overcharging or overdischarging to extend the service life of the device. This dynamic adjustment ensures that the energy storage device maximizes its effectiveness in the microgrid, smooths power fluctuations, and improves system stability.

[0120] For example, assume a microgrid containing 5 nodes, with node 1 being a solar power generation device, node 2 being an energy storage device (battery), and nodes 3-5 being load points. The fractional-order reaction-diffusion model generates a spatiotemporal dynamic distribution, showing that at 10:00-11:00 am, the load demand of node 3 peaks at 500kW, and the fluctuation trend indicates that the power demand increases sharply at 10:30, while the solar power generation decreases to 200kW due to cloud cover. Based on this, the optimization algorithm analyzes the distribution data to determine the charging and discharging strategy of the energy storage device:

[0121] Charging and discharging time window: from 10:00-10:30, node 2 battery operates at a low discharge rate (50kW) to supplement part of the power gap; from 10:30-11:00, during the load peak, the battery switches to a high discharge rate (300kW) to ensure supply and demand balance.

[0122] Charging strategy: after 11:00, the load demand decreases to 200kW, and the solar power generation recovers to 400kW, the battery enters the charging mode at a rate of 150kW to reserve energy.

[0123] By monitoring the spatiotemporal dynamic distribution in real time, the system dynamically adjusts the charging and discharging rate and time window of the battery, avoiding overcharging or overdischarging while minimizing power loss. This process is achieved through real-time data collection and fast optimization algorithms, ensuring that the microgrid remains efficient and stable in dynamic changes.

[0124] The dynamic adjustment of the grid topology configuration is based on the spatiotemporal dynamic distribution to optimize the power transmission path for minimizing power loss. The spatiotemporal dynamic distribution provides the path information of power flow among nodes, which, in combination with the topology matrix, can identify the path with high transmission efficiency or low loss. Optimization algorithms, such as linear programming or heuristic algorithms based on minimizing power loss, can be used to reconfigure the power transmission path. For example, if the distribution shows that there is an overload risk in a certain path, the system can switch to a backup path or adjust the power allocation priority. The adjustment of the topology configuration can involve changing the connection weight between nodes or enabling redundant lines, thereby optimizing the flow of electrical energy in the non-uniform topology, reducing transmission loss, and improving the robustness of the grid.

[0125] The optimized power allocation strategy integrates the results of the charging and discharging strategies of energy storage devices and the adjustment of the grid topology configuration to generate a comprehensive power allocation scheme. This strategy is guided by the spatiotemporal dynamic distribution to ensure that the power allocation not only meets the real-time supply and demand balance but also minimizes system loss. For example, in a microgrid containing multiple renewable energy generation devices and energy storage devices, the system can prioritize discharging energy storage devices during peak load and simultaneously select low-impedance paths for power transmission; during the off-peak period, it can prioritize charging and optimize the path to reduce standby loss. This comprehensive optimization strategy can adapt to the dynamic changes of the microgrid in real time, improving operational efficiency and economy.

[0126] The above-mentioned method provided by the embodiments of the present application can be applied to various application scenarios, including but not limited to: in an independent microgrid in a remote area, the solar and wind power generation fluctuates greatly, and the method can stabilize power supply and reduce power loss by optimizing the charging and discharging of energy storage devices and the power transmission path in real time. In a distributed microgrid in a city, in the face of frequent changes in load demand, such as peak electricity consumption in business districts, the system can dynamically adjust the discharging rate of energy storage and the topology configuration to ensure supply and demand balance and reduce line overload. In a microgrid in an industrial park, combined with multi-source power generation and complex topology, the method can optimize power allocation, preferentially select low-impedance paths for power transmission, improve system efficiency and reliability, and adapt to dynamic operation requirements.

[0127] The above describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or possible.

[0128] According to another aspect, embodiments of a microgrid dynamic power allocation apparatus are provided. Figure 3 A schematic block diagram of the microgrid dynamic power allocation apparatus according to one embodiment is shown. As shown inFigure 3 As shown in the figure, the device 300 comprises:

[0129] A power grid data acquisition unit 301 configured to acquire real-time operation data and topology data of a microgrid, the real-time operation data including output power of renewable energy power generation equipment, energy storage equipment state and load demand, and the topology data including connection relationship of nodes and edges.

[0130] A power grid data analysis unit 302 configured to perform difference calculation on the real-time operation data to obtain power change data; and analyze the connection relationship of the nodes and edges based on the topology data through a graph theory algorithm to obtain power transmission path data.

[0131] A reaction-diffusion model construction unit 303 configured to construct a fractional-order reaction-diffusion model, wherein a reaction term is determined based on the power change data; and a diffusion term is determined based on the power transmission path data, and a fractional-order partial differential equation is used to describe long-range diffusion behavior of power in a non-uniform topology.

[0132] A spatiotemporal dynamic analysis unit 304 configured to solve the fractional-order reaction-diffusion model to generate a spatiotemporal dynamic distribution of microgrid power distribution.

[0133] An optimization strategy generation unit 305 configured to dynamically adjust charge and discharge strategies of energy storage equipment and power grid topology configuration based on the spatiotemporal dynamic distribution to obtain an optimized power distribution strategy.

[0134] As an implementable way, when the power grid data analysis unit 302 analyzes the connection relationship of the nodes and edges based on the topology data through a graph theory algorithm to obtain power transmission path data, it can be configured to: construct a weighted directed graph model of the microgrid based on the topology data, wherein nodes represent power generation equipment, energy storage equipment and loads, and edges represent power transmission lines, and weights of the edges are determined based on impedance and power loss of the lines; apply a Dijkstra algorithm to the weighted directed graph to calculate the shortest power transmission path from the power generation equipment to the load nodes; and determine priority order of power distribution based on the shortest power transmission path to generate a topology matrix of the power transmission path.

[0135] As an implementable way, when the reaction-diffusion model construction unit 303 determines the reaction term based on the power change data, it can be configured to: adopt Caputo fractional derivative definition to construct a fractional-order dynamics model of the reaction term; and determine parameters of the fractional-order dynamics model through numerical fitting based on the power change data to generate a specific fractional-order expression of the reaction term.

[0136] As an implementable manner, the reaction-diffusion model construction unit 303 can be configured to: when the diffusion term is determined based on the power transmission path data, and the long-range diffusion behavior of power in the non-uniform topology is described by a fractional-order partial differential equation, construct a fractional-order partial differential equation of the diffusion term by using a Riesz fractional derivative definition; determine a diffusion coefficient of the fractional-order partial differential equation according to the topology matrix; and generate a mathematical expression of the diffusion term in combination with the fractional-order partial differential equation.

[0137] As an implementable manner, the space-time dynamic analysis unit 304 can be configured to: when solving the fractional-order reaction-diffusion model to generate a space-time dynamic distribution of power distribution of the micro-grid, discretize the fractional-order reaction-diffusion model into a numerical calculation format, and process fractional-order derivatives in the time domain and the space domain by using a finite difference method; apply a fast Fourier transform algorithm to accelerate numerical solving of the fractional-order reaction-diffusion model, and generate the space-time dynamic distribution of the power distribution.

[0138] As an implementable manner, the space-time dynamic analysis unit 304 can be configured to: when solving the fractional-order reaction-diffusion model, adaptively adjust a grid resolution of the numerical solving according to a number of nodes and a topology complexity of the micro-grid.

[0139] As an implementable manner, the optimization strategy generation unit 305 can be configured to: when dynamically adjusting a charge-discharge strategy of the energy storage device and a power grid topology configuration according to the space-time dynamic distribution, analyze the space-time dynamic distribution to extract a power distribution peak value and a fluctuation trend of each node in the micro-grid; optimize a charge-discharge time window of the energy storage device based on the power distribution peak value and the fluctuation trend, determine a charge and discharge rate of each energy storage device; and dynamically adjust a power transmission path by using an optimization algorithm of minimizing power loss according to a power distribution value in the space-time dynamic distribution, and update the power grid topology configuration.

[0140] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, the device embodiment is described more simply because it is basically similar to the method embodiment, and the relevant part can be referred to the part of the method embodiment. The device embodiment described above is only schematic, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0141] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0142] In addition, the embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method in any one of the preceding method embodiments.

[0143] An electronic device includes one or more processors, and a memory associated with the one or more processors, the memory being configured to store program instructions that, when executed by the one or more processors, perform the steps of the method in any one of the preceding method embodiments.

[0144] The present application also provides a computer program product, which includes a computer program that, when executed by a processor, realizes the steps of the method in any one of the preceding method embodiments.

[0145] Among them, Figure 4 An exemplary architecture of an electronic device is shown, which can specifically include a processor 410, a video display adapter 411, a disk drive 412, an input / output interface 413, a network interface 414, and a memory 420. The processor 410, the video display adapter 411, the disk drive 412, the input / output interface 413, the network interface 414, and the memory 420 can be connected by a communication bus 430.

[0146] Among them, the processor 410 can be implemented by a general-purpose CPU, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., for executing related programs to realize the technical solutions provided by the present application.

[0147] The memory 420 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 420 can store an operating system 421 for controlling the operation of the electronic device 400, a basic input / output system (BIOS) 422 for controlling the low-level operation of the electronic device 400. In addition, a web browser 423, a data storage management system 424, and a micro-grid dynamic power distribution apparatus 425, etc. can also be stored. The above-mentioned micro-grid dynamic power distribution apparatus 425 can be an application program for specifically implementing the above-mentioned steps in the embodiments of the present application. In summary, when the technical solutions provided by the present application are implemented by software or firmware, the relevant program codes are stored in the memory 420 and executed by the processor 410.

[0148] The input / output interface 413 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input devices can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices can include a display, a speaker, a vibrator, an indicator light, etc.

[0149] The network interface 414 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0150] The bus 430 includes a path for transmitting information between various components (such as the processor 410, the video display adapter 411, the disk drive 412, the input / output interface 413, the network interface 414, and the memory 420) of the device.

[0151] It should be noted that although the above-mentioned device only shows the processor 410, the video display adapter 411, the disk drive 412, the input / output interface 413, the network interface 414, the memory 420, the bus 430, etc., in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above-mentioned device can also only contain the components necessary for implementing the solutions of the present application, and does not have to contain all the components shown in the figure.

[0152] Those skilled in the art can clearly understand the application by the description of the above embodiments that the application can be implemented by means of software and the necessary universal hardware platform. Based on such an understanding, the technical solutions of the application can be embodied in the form of a computer program product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and include a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments of the application.

[0153] The technical solutions provided by the application are described in detail above, and the principles and implementation manners of the application are described by applying specific examples. The above description of the embodiments is only used to help understand the methods and core ideas of the application. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the ideas of the application. In conclusion, the content of the specification should not be understood as a limitation of the application.

Claims

1. A method for dynamic power distribution in a microgrid, the method comprising: The method comprises: obtaining real-time operation data and topology structure data of the microgrid, wherein the real-time operation data comprises output power of renewable energy power generation equipment, state of energy storage equipment and load demand, and the topology structure data comprises connection relationship of nodes and edges; performing differential calculation on the real-time operation data to obtain power change data; and analyzing the connection relationship of the nodes and edges based on the topology structure data to obtain power transmission path data by using a graph theory algorithm; constructing a fractional order reaction-diffusion model, wherein a reaction term is determined based on the power change data, and a diffusion term is determined based on the power transmission path data, and the diffusion term describes long-range diffusion behavior of power in a non-uniform topology by using a fractional order partial differential equation; solving the fractional order reaction-diffusion model to generate a spatio-temporal dynamic distribution of microgrid power distribution; dynamically adjusting charging and discharging strategies of the energy storage equipment and grid topology configuration based on the spatio-temporal dynamic distribution to obtain an optimized power distribution strategy.

2. The microgrid dynamic power allocation method of claim 1, wherein, The method further comprises: based on the topology structure data, constructing a weighted directed graph model of the microgrid, wherein nodes represent power generation equipment, energy storage equipment and loads, and edges represent power transmission lines, and weights of the edges are determined based on impedance and power loss of the lines; applying a Dijkstra algorithm to the weighted directed graph model to calculate shortest power transmission paths from the power generation equipment to the load nodes; and determining priority order of power distribution based on the shortest power transmission paths to generate a topology matrix of the power transmission paths. The method further comprises: adopting a Caputo fractional order derivative definition to construct a fractional order dynamics model of the reaction term; and determining parameters of the fractional order dynamics model based on the power change data to generate a specific fractional order expression of the reaction term. The method further comprises: adopting a Riesz fractional order derivative definition to construct a fractional order partial differential equation of the diffusion term; determining diffusion coefficients of the fractional order partial differential equation based on the topology matrix; and combining the fractional order partial differential equation to generate a mathematical expression of the diffusion term. The method further comprises: discretizing the fractional order reaction-diffusion model into a numerical calculation format, and processing fractional order derivatives in time domain and space domain by using a finite difference method; and applying a fast Fourier transform algorithm to accelerate numerical solution of the fractional order reaction-diffusion model to generate the spatio-temporal dynamic distribution of the power distribution.

3. The method of claim 1, wherein, The method further comprises: when solving the fractional order reaction-diffusion model, adaptively adjusting grid resolution of the numerical solution based on the number of nodes and topology complexity of the microgrid. The method further comprises: analyzing the spatio-temporal dynamic distribution to extract power distribution peak values and fluctuation trends of each node in the microgrid; ​ 4. The method of claim 2, wherein, ​ ​ ​ ​ 5. The method of claim 1, wherein, ​ ​ ​ 6. The microgrid dynamic power allocation method of claim 5, wherein, ​ 7. The microgrid dynamic power allocation method of claim 1, wherein, ​ ​ Based on the power allocation peak value and fluctuation trend, the charge and discharge time window of the energy storage device is optimized, and the charging and discharging rates of each energy storage device are determined; According to the power allocation value in the space-time dynamic distribution, the power transmission path is dynamically adjusted by an optimization algorithm for minimizing power loss, and the power grid topology configuration is updated.

8. A microgrid dynamic power allocation apparatus, characterized by, The device comprises: The power grid data acquisition unit is configured to acquire real-time operation data and topology structure data of the micro-grid, wherein the real-time operation data includes output power of renewable energy power generation equipment, energy storage device state and load demand, and the topology structure data includes connection relationship of nodes and edges; The power grid data analysis unit is configured to perform difference calculation on the real-time operation data to obtain power change data, and analyze the connection relationship of the nodes and edges based on the topology structure data to obtain power transmission path data by using a graph theory algorithm; The reaction diffusion model construction unit is configured to construct a fractional order reaction diffusion model, wherein a reaction term is determined based on the power change data, and a diffusion term is determined based on the power transmission path data, and a long-range diffusion behavior of power in a non-uniform topology is described by using a fractional order partial differential equation; The space-time dynamic analysis unit is configured to solve the fractional order reaction diffusion model to generate a space-time dynamic distribution of micro-grid power allocation; The optimization strategy generation unit is configured to dynamically adjust the charge and discharge strategy of the energy storage device and the power grid topology configuration based on the space-time dynamic distribution to obtain an optimized power allocation strategy.

9. An electronic device, comprising: comprise: one or more processors; and a memory associated with the one or more processors, the memory being configured to store program instructions that, when executed by the one or more processors, perform the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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