A micro-grid load power allocation method and device based on graph theory

Abstract

The application relates to the technical field of micro-grid regulation, and discloses a micro-grid load power allocation method and device based on graph theory, which comprises the following steps: establishing a graph theory model for the topological relationship of each load of a micro-grid based on graph theory; performing consensus control on each load of the graph theory model based on a preset consensus algorithm to obtain a corresponding dynamic quantity control equation, and constructing a Jacobian matrix based on the dynamic quantity control equation, wherein the numerical values of each row and column in the Jacobian matrix are used to determine the load type; performing load classification and identification on the Jacobian matrix based on a preset determination mode to obtain the type of the corresponding load; obtaining the circuit parameters corresponding to different types of loads, and performing hierarchical power allocation on the corresponding loads through a preset reference power. The application takes the minimum generation cost and loss cost as the optimization target, focuses on optimizing and identifying the topological structure, can perform classification management and hierarchical power allocation on the loads after accurately identifying the load types, and realizes efficient signal transmission and maximization of cost saving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid regulation, and particularly relates to a micro-grid load power allocation method and device based on graph theory. BACKGROUND

[0002] The distributed power generation energy in the micro-grid has strong randomness, and the existing collaborative control method cannot effectively solve the problem of distributed economic power distribution, and it is difficult to achieve power balance limitation without centralized management equipment. At the same time, there are many special loads in the micro-grid, such as large voltage and small current, low voltage and large current, and special loads. The existing micro-grid partitioning technology can only roughly determine the power conversion efficiency, and cannot accurately identify and determine the load that needs to be specified.

[0003] The existing micro-grid topology structure also has many limitations. The limited range of the ordinary topology structure is too large, and it is difficult to involve all systems including power supply, load, transmission network, terminal position, and the structure is complex and expensive to design. The expandability is not strong, and it is difficult to upgrade. The complex structure will cause delay in fault judgment of the micro-grid, and then affect the stability and safety of the system. In addition, the centralized topology structure is extremely dependent on the central management system. When the access amount in the power grid increases, the data amount and energy flow of the central control management system increase sharply, and the calculation speed often cannot meet the real-time requirements of power dispatching, the time delay is long, the stability is not strong, and if a single module fails and needs to be operated in island mode, it cannot access the main grid, and its supply capacity is difficult to follow. And the distributed topology structure has many iterative algorithms, and the calculation time is long, there is time delay, the robustness is not strong, and the problem solving ability is not flexible for power failure, fault, short circuit and other problems. In summary, there are too many uncertainties in the micro-grid, such as uncertain energy output power, uncertain load use, and uncertain price. At the same time, some special high-precision loads need the micro-grid to be stably operated to ensure power supply in time. The above problems will have a certain impact on the determination and optimization of the micro-grid topology structure, which is not conducive to the optimal allocation of power. SUMMARY

[0004] Therefore, the present application provides a micro-grid load power allocation method and device based on graph theory, which adopts optimal control identification and graph theory topology structure, takes the minimum generation cost and loss cost as the optimization target, and focuses on optimizing and identifying the topology structure. After accurately identifying the load type, the classification management and hierarchical power allocation of the load are realized, the efficient signal transmission and the maximization of cost saving are realized, and the technical problems in the above background are solved.

[0005] In a first aspect, an embodiment of the present application provides a micro-grid load power allocation method based on graph theory, which comprises:

[0006] A graph theory model is established based on the graph theory of the topology relationship of each load in the micro-grid;

[0007] The preset consensus algorithm is used for consensus control on each load of the graph theory model, a corresponding dynamic quantity control equation is obtained, and a Jacobi matrix is constructed based on the dynamic quantity control equation, and each row and column value in the Jacobi matrix is used for determining a load type;

[0008] The preset determination mode is used for load classification identification on the Jacobi matrix, and a type of the corresponding load is obtained.

[0009] Corresponding circuit parameters of different types of loads are obtained, and hierarchical power allocation of the corresponding load is performed through a preset reference power.

[0010] The micro-grid load power allocation method based on the graph theory establishes a graph theory model for a topology structure of the micro-grid, takes the minimum generation cost and loss cost as an optimization target, focuses on optimizing and identifying the topology structure, finds a balance between the power supply and the load, and obtains a better and more robust allocation mode.

[0011] In an optional implementation, the process of obtaining the corresponding dynamic quantity control equation based on the preset consensus algorithm for consensus control on each load of the graph theory model, and constructing the Jacobi matrix based on the dynamic quantity control equation, comprises:

[0012] A corresponding Laplacian matrix is determined according to the graph theory model.

[0013] An initial value of an internal dynamic quantity of the preset Laplacian matrix of the power supply and the load in the power grid is determined.

[0014] A distributed consensus algorithm is adopted, the initial value of each internal dynamic quantity of the graph theory model and the Laplacian matrix is combined, and a dynamic quantity control equation of each load is obtained through a time domain response of a dynamic variable.

[0015] The dynamic quantity control equation is Taylor expanded at any time, a real-time Taylor expansion function of each equation is obtained, a Jacobi matrix is constructed based on the Taylor expansion function, and the Jacobi matrix containing preset parameters is obtained.

[0016] The Jacobi matrix of the application is obtained by consensus control and Taylor expansion on the graph theory model, and the Jacobi matrix constructed based on mathematical related formulas can provide a scientific and reliable classification basis for subsequent load classification, and improves the classification accuracy to a certain extent.

[0017] In an optional implementation, the preset determination mode comprises: determining a type of the corresponding load based on a distribution range of each row and column value of the Jacobi matrix or a change rate of an absolute value of a derivative of the Jacobi matrix, wherein the derivative of the Jacobi matrix comprises a first derivative and a second derivative.

[0018] The preset determination mode of the load classification can be adaptively adjusted according to actual application requirements, so that the classification of the load is more personalized, higher in flexibility and pertinence, and convenient for expansion and modification; the type of each load is determined by the distribution range of the numerical values of each row and column of the Jacobian matrix or the change rate of the absolute value of the derivative, so that the load recognition and classification are high in accuracy, high in reliability of the determination mode, good in stability and real-time performance.

[0019] In an optional embodiment, the load is subjected to hierarchical power allocation by a power grid control system, wherein the power grid control system is a two-level controller including a global controller and a plurality of local controllers.

[0020] The power of the load is subjected to hierarchical allocation, which helps to improve the power allocation efficiency and obtain optimal allocation power.

[0021] In an optional embodiment, the circuit parameters corresponding to different types of loads are acquired, and the process of hierarchical power allocation of the corresponding load by a preset reference power includes:

[0022] The local controllers store the classification and recognition results of different types of loads;

[0023] Each local controller receives the preset reference power provided by the global controller, combines the classification and recognition results, and performs decoupling, power distribution and quadrant conversion processing and calculation to obtain tracking values, controls the tracking values by a local control loop and tracks the tracking values by a phase-locked loop, and stores and monitors the obtained tracking effect.

[0024] The hierarchical power allocation of the load by the global controller and the local controllers helps to improve the power allocation efficiency, obtain optimal allocation power, and realize real-time tracking control of specific values in the allocation process, thereby facilitating visual analysis of the subsequent allocation process.

[0025] In an optional embodiment, the circuit parameters are acquired by a signal acquisition and processing circuit, and the signal acquisition and processing circuit includes an acquisition unit, a processing unit and a transmission unit; the circuit parameters include voltage, current, active power, reactive power and frequency.

[0026] The signal acquisition and processing circuit can quickly acquire, process and transmit the related circuit parameters of the allocation, thereby improving the power allocation efficiency to a certain extent.

[0027] In an optional embodiment, the preset power demand includes the overall output expected requirement of the micro-grid power supply and the power demand of each load.

[0028] The power values of the loads to be allocated are numerized based on the preset power demand, which can provide a reference basis for the power allocation of the loads.

[0029] In a second aspect, an embodiment of the present application provides a micro-grid load power allocation device based on graph theory, the device comprising:

[0030] a graph theory model establishing module configured to establish a graph theory model of topological relations of each load of the micro-grid based on graph theory;

[0031] a Jacobian matrix constructing module configured to perform consensus control on each load of the graph theory model based on a preset consensus algorithm, obtain a corresponding dynamic quantity control equation, and construct a Jacobian matrix based on the dynamic quantity control equation, wherein each row and column value in the Jacobian matrix is used to determine a load type;

[0032] a load classification and identification module configured to perform load classification and identification on the Jacobian matrix based on a preset determination method, and obtain the type of the corresponding load;

[0033] a hierarchical power allocation module configured to obtain circuit parameters corresponding to different types of loads, and perform hierarchical power allocation on the corresponding loads through a preset reference power.

[0034] The micro-grid load power allocation device based on graph theory of the present application adopts optimal control identification and graph theory topological structure, takes the minimum generation cost and loss cost as the optimization target, focuses on optimizing and identifying the topological structure, can perform classification management and hierarchical power allocation on the loads after accurately identifying the load types, and realizes efficient signal transmission and maximization of cost saving.

[0035] In a third aspect, the present application provides a computer device, comprising a memory and a processor, the memory and the processor are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the micro-grid load power allocation method based on graph theory of the first aspect or any of the corresponding embodiments thereof.

[0036] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the micro-grid load power allocation method based on graph theory of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0038] Figure 1is a flowchart of a micro-grid load power allocation method based on graph theory according to an embodiment of the present application;

[0039] Figure 2 is a micro-grid structure diagram according to an embodiment of the present application;

[0040] Figure 3 is a voltage graph theory model diagram according to an embodiment of the present application;

[0041] Figure 4 is a voltage consensus control trajectory diagram according to an embodiment of the present application;

[0042] Figure 5 is a power grid control diagram according to an embodiment of the present application;

[0043] Figure 6 is a control loop diagram according to an embodiment of the present application;

[0044] Figure 7 is a phase-locked loop structure diagram according to an embodiment of the present application;

[0045] Figure 8 is a transfer function diagram according to an embodiment of the present application;

[0046] Figure 9 is a data processing chip diagram according to an embodiment of the present application;

[0047] Figure 10 is a data processing flow diagram according to an embodiment of the present application;

[0048] Figure 11 is a GPS pin diagram according to an embodiment of the present application;

[0049] Figure 12 is an information transmission diagram according to an embodiment of the present application;

[0050] Figure 13 A and B are respectively a power distribution network and a power supply side structure diagram according to an embodiment of the present application;

[0051] Figure 14 is a structure block diagram of a micro-grid load power allocation device based on graph theory according to an embodiment of the present application;

[0052] Figure 15 is a hardware structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, 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 some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0054] At present, due to the advantages of various, easy to deform and easy to expand of the graph theory topology, the identification thereof has the problems of low efficiency and low precision, and the optimal deployment of load power cannot be achieved. The micro-grid load power deployment method based on graph theory proposed in the embodiments of the present application focuses on optimizing the topology structure, constructs a network by the topology matrix and the in-out relationship of the power supply and the resistance, and takes the minimum generation cost and loss cost as the optimization target to transform into a single-objective optimization problem, so that the economic, environmental protection and efficient optimization topology structure is obtained, which makes the later load determination more simple and the efficiency of power deployment also greatly improved. Meanwhile, it is also beneficial to the later maintenance and repair, and the maintenance personnel can quickly identify the types of various loads, and after obtaining accurate statistical data, the corresponding deployment mode can be quickly taken for regulation and control. To some extent, the micro-grid obtains the maximum benefit at a low cost, and meets the increasing demand of people for power supply reliability and power quality.

[0055] The micro-grid load power deployment method based on graph theory in the embodiments of the present application is shown in Figure 1 The method comprises the following steps:

[0056] In step S101, a graph theory model is established based on the topology relationship of each load in the micro-grid.

[0057] It should be noted that in the mathematical graph theory, a graph is a data structure for establishing the relationship of objects, including: vertices and edges (i.e. the connection line between vertices), wherein the vertices are corresponding to pairs or multiple points. In the topology structure of the micro-grid, the vertices of the graph are power supplies or loads, and the connection line of the edges is the voltage or current trend in the graph theory of the power grid, and the specified direction is positive (i.e. using the arrow direction to specify the flow direction of a certain voltage or current as positive). Specifically, referring to Figure 2 The power supply and the resistance in the micro-grid structure are graphically constructed by the topology matrix and the in-out relationship to obtain the corresponding graph theory model.

[0058] In the embodiments, the types of the graph theory model include: a voltage graph theory model and a current graph theory model. The voltage graph theory model is taken as an example for illustration, as shown in Figure 3

[0059] ​It should be noted that the form of the graph theory model includes: directed graph form and undirected graph form. When the graph theory model is in the form of a directed graph, the graph theory model can be simplified according to the design cost, complexity and response speed of the micro-grid system. For example, the voltage and current are taken in a certain direction as positive, that is, the graph theory model can be clockwise or from top to bottom. Among them, the arrow in the directed graph form can be one-way or two-way, depending on the required response speed and complexity of the system. If the system requires a faster response speed and a higher budget, two-way transmission can be used; if the system has a higher tolerance, low sensitivity and cost constraints, one-way transmission or part of the line can be used to simplify the system, which is only used as an example and is not limited thereto.

[0060] In step S102, the consensus control is performed on each load of the graph theory model based on a preset consensus algorithm, a corresponding dynamic quantity control equation is obtained, and a Jacobian matrix is constructed based on the dynamic quantity control equation. The values of each row and column in the Jacobian matrix are used to determine the load type.

[0061] It should be noted that in the distributed and multi-vertex micro-grid, if there are multiple faults, it is difficult to ensure the overall reliability and stability of the system. Therefore, the system usually needs to achieve consistency through a collaborative process, and some numerical values need to reach consensus. The consensus problem requires that the data values of all vertices reach agreement. However, some vertices may fail in some aspects, so the consensus control needs to have certain fault tolerance and flexibility, that is, the effective vertices need to exceed half, otherwise when there are too many abnormal points, the consensus algorithm cannot converge and stabilize. In the process, their reference values are obtained, communicated with each other, and the unique formula is agreed, and the consensus problem is the basis for the intelligent system control of the micro-grid.

[0062] It should be noted that in the micro-grid system, it is assumed that the system measures multiple dynamic quantities, for example, power supply voltage x G , inductance current x iL , capacitor voltage x vc , DC voltage x vdc , load power x Pload , etc. The parameters are only used for illustrative purposes, and the corresponding dynamic quantity parameters can be adjusted adaptively based on system requirements.

[0063] The dynamic quantity has a fixed dynamic motion mode, denoted as a dynamic equation. The dynamic equation is related to the state of other various dynamic quantities, which is a multiple-input multiple-output complex state. Let the equations of these changes be f(x G ), f(x iL ), f(x vC ), f(x vdc ), f(x Pload ), these equations are linear equations or nonlinear equations.

[0064] For example, the system dynamic equation is expressed as or wherein R, L, C are parameters of elements corresponding to the system, and their values are determined according to actual application. If the dynamic equation is a high-order or nonlinear equation, it can also be expressed by the change mode.

[0065] When the micro-grid system is a nonlinear system, the slope and change mode are not determined. At any time, Taylor expansion is performed to obtain the real-time Taylor expansion of each equation, which is expressed as Since the coefficients of the square and higher order terms of the expansion are small, they can be ignored, thereby obtaining the Jacobian matrix containing specific parameters, which is denoted as:

[0066]

[0067] wherein a k is the weight of the kth equation f(x), and b k is the weight of the kth parameter x. Their superposition is the weight proportion of each parameter to each equation, and the proportion can be modified according to the system tendency. For example, if the capacitance voltage x vC is considered to be more important than the inductance current x iL , then a vC = 1.5, a iL = 1, which is only illustrative and is not limited thereto. It should be noted that the specific parameter setting in the Jacobian matrix has monitoring effect on the weak change or long-term change of the voltage amplitude and phase. Due to the intermittent power supply and switch of the power sources in the micro-grid, the reliability of the power grid will be affected by the irregular transfer of the power flow. The topology structure of the present embodiment cooperates with the consensus control and sets the Jacobian matrix of the specific parameters, so that the circuit voltage is stable and the power flow balance is good.

[0068] In the present embodiment, the roots of the Jacobian matrix are determined, and the stability of the matrix is determined based on the determination result of the roots. If all the roots fall in the left half plane of the S plane (a plane composed of the real axis and the imaginary axis as the horizontal and vertical coordinate axes by transforming the time domain function into the complex frequency domain by Laplace transform), the matrix is asymptotically stable; if any root falls in the right half plane of the S plane, the matrix is unstable; if some roots fall in the left half plane of the S plane or on the imaginary axis, the stability of the matrix is uncertain.

[0069] In step S103, the load classification recognition is performed on the Jacobian matrix based on the preset determination mode, and the type corresponding to the load is obtained.

[0070] In this embodiment, the types of loads include: general loads and special loads. The general loads are loads with current-voltage ratio within a certain range and without special mutations or excessive nonlinear changes. Other loads except the general loads are special loads, such as high-voltage small-current loads and low-voltage large-current loads, which are only used as examples and are not limited thereto. The specific types of loads are determined according to actual application requirements.

[0071] In step S104, the circuit parameters corresponding to different types of loads are obtained, and the power of the corresponding loads is graded and adjusted through a preset reference power.

[0072] In this embodiment, the preset power requirement includes the overall output expected requirement of the micro-grid power supply and the power requirement of each load, which is only used as an example and is adaptively adjusted according to actual application requirements.

[0073] The micro-grid load power adjustment method based on graph theory in the embodiment of the present application takes the minimum generation cost and loss cost as the optimization target, focuses on optimizing and identifying the topological structure, and obtains a better and more robust adjustment mode. The load can be accurately identified and classified for management and optimal power adjustment, and efficient signal transmission and maximized cost saving are realized.

[0074] Specifically, in step S102, the following steps are included.

[0075] In step S1021, the corresponding Laplacian matrix is determined according to the graph theory model.

[0076] In this embodiment, the Laplacian matrix L is determined according to the graph theory model, and each element l of the Laplacian matrix L is expressed as:

[0077]

[0078] where i and j are vertices in the graph theory model, N i is the neighbor of i, and is expressed as N i ={j∈V,(j,i)∈E}, V is a vertex set in the graph theory model, and E is an edge set in the graph theory model. Then, the Laplacian matrix L of the voltage graph theory model is determined based on the above expression. Figure 3

[0079] It should be noted that the eigenvalue λ of the Laplacian matrix L needs to satisfy the following conditions:

[0080] 0=λ1(L)<λ2(L)≤...≤λ N (L)

[0081] where N is the number of eigenvalues λ.

[0082] The state of the node n of the Laplacian matrix is defined as x n (i=0)=r​n n = 1, …, N, and different line weights are added according to the importance and priority of the lines.

[0083] In step S1022, the initial value of the internal dynamic quantity of the Laplace matrix is set according to the preset power supply and load in the power grid.

[0084] In this embodiment, the initial value of each internal dynamic quantity x of the Laplace matrix is set according to the nature of the micro-grid system. For example, x(0) = col(2, 3, 1, 8, 4), which is only an example and is not limited thereto.

[0085] In step S1023, a distributed consensus algorithm is used to obtain the dynamic quantity control equation of each load by combining the graph theory model and the initial value of each internal dynamic quantity of the Laplace matrix through the time domain response of the dynamic variable.

[0086] In this embodiment, the formula of the distributed consensus algorithm is wherein, is the expression form after the consensus control of the dynamic quantity x. The voltage graph theory model is combined with the initial value of each internal dynamic quantity of the Laplace matrix x(0) = col(2, 3, 1, 8, 4), and the consensus control trajectory of each load is obtained through the time domain response of the dynamic variable, that is, Figure 3 The voltage consensus control trajectory diagram of the voltage graph theory model. The horizontal coordinate is time, the vertical coordinate is dynamic quantity, and the legends 1, 2, 3, 4, and 5 in the figure are the vertices in the voltage graph theory model, which are only used as an example. It should be noted that the system can balance and improve between points i and j through the input of consensus control, so that it is not affected by system disturbance and changes as much as possible. Figure 4 Figure 3 The dynamic quantity control equation is related to its deviation variable and input matrix, and is obtained by iteration from the initial time. The dynamic quantity control equation is represented as:

[0087]

[0088]

[0089] wherein, is the iteration sum of the dynamic quantity at k+1 time, is the iteration sum of the dynamic quantity at k time, A is a coefficient, and P(k) is the deviation variable at k time.

[0090] In step S1024, Taylor expansion is performed on the dynamic quantity control equation at any time to obtain the real-time Taylor expansion function of each equation, and a Jacobian matrix is constructed based on the Taylor expansion function to obtain a Jacobian matrix containing preset parameters.

[0091] In this embodiment, the absolute values of the first derivative and the second derivative of the dynamic quantity x in the Jacobian matrix are focused on. ​​

[0092] It should be noted that the current consensus control principle is similar to the voltage consensus control, and a corresponding current consensus control scheme can be formulated, and a corresponding Jacobian matrix can be obtained for determining the load type.

[0093] In addition, the feasibility and stability of the graph theory model can be verified based on the Lyapunov matrix equation and the Lyapunov-Krasovskii theorem, and the reliability and effectiveness of the model can be confirmed, which can ensure good classification accuracy of the load and robustness of the graph theory model.

[0094] In the embodiment, the preset determination method includes determining the corresponding load type based on the distribution range of the numerical values of each row and column of the Jacobian matrix or the change rate of the absolute value of the derivative of the Jacobian matrix, wherein the derivative of the Jacobian matrix includes the first derivative and the second derivative.

[0095] In the embodiment, the distribution range of the numerical values of each row and column of the Jacobian matrix can be used to determine the type and range of the load. For example, if the root mean square values are the same, the circuit element corresponding to the Jacobian matrix with a larger number of rows has a large voltage float, a high frequency, and a strong nonlinearity, and is easy to generate harmonics, which should be distinguished from low-frequency circuit elements. Assuming that the numerical value distribution range of a certain column m of the Jacobian matrix is 0≤m≤10, 10≤m≤50, 50≤m≤100, 100≤m≤1000, etc., the different ranges are specific types of power or load.

[0096] In the embodiment, the absolute value of the derivative of the Jacobian matrix is the change rate of each dynamic quantity. If the change rate of the dynamic quantity is too large, the corresponding circuit element is judged as a special load, such as a semiconductor, a special gas, a precision, an amplifier, etc. If the change rates of multiple dimensions are all too large, the corresponding circuit element is a composite type of multiple special loads.

[0097] It should be noted that the preset determination method of the load classification described above is only used as an example for illustration, and is not limited thereto, and can be adaptively adjusted according to actual application requirements. For example, one or more parameters such as the size of the dynamic quantity, the size of the row and column of the Jacobian matrix, and the size of the absolute value of the derivative of the Jacobian matrix are used for multi-dimensional fine classification and identification.

[0098] The embodiment uses graph theory to optimize the topology structure in the traditional topology to solve the problems of low efficiency and large loss, etc. The structure is refined, simple and easy to detect, so that the later circuit determination is more simple, the controller tracking is faster, the efficiency is improved, and the later maintenance and maintenance are beneficial. At the same time, it can assist the control strategy to solve the problems of supply and demand imbalance, voltage fluctuation, frequency fluctuation, voltage and current harmonics, unstable output power, etc. It has strong expandability, and the order of the system is proportional to the matrix. For the future power system with more and more electrical appliances, it can achieve the effect of large capacity, high power, high conversion efficiency, multi-port and multi-functional parallel

[0099] In a specific embodiment, a reasonable range is set for different circuits, that is, the voltage change rate changes more than 100V in 1ms is the first range, more than 1KV is the second range, and the partition classification standard is set for each voltage and current range. If the change of current and voltage exceeds a certain range, the absolute value of the second derivative of the dynamic quantity x is determined by the preset range. For example, the current is lower than the first range and the voltage is higher than the third range, which can be determined as a certain type of load. This type and partition can be defined by itself, and can be personalized modified according to the circuit topology structure, load size and initial value of voltage and current, which has high flexibility and pertinence, and is convenient for the expansion and upgrading of future micro-grid system.

[0100] In the embodiment, the load is power-dispatched by the grid control system, wherein the grid control system is a two-stage controller, including a global controller and a plurality of local controllers.

[0101] Figure 5 The grid control schematic diagram of the embodiment is shown in the figure, and the grid as a whole includes a global controller, a plurality of local controllers and an energy storage system composed of a plurality of distributed power generations. Specifically, after the voltage and current signals on each point and branch are collected, they are input into the global controller for power dispatching, and the approximate reference power range is obtained through the overall output of the power supply and the load demand. Based on the rated voltage of each branch, the reference voltage and current are obtained, and the quadrant can be selected according to the different tracking controllers. Then the reference voltage and current are provided to each power supply, and the distributed power generation is dispatched and corrected by the local controller. At the same time, there are various energy storage devices such as battery energy storage system (BESS) in the system to charge and discharge the circuit to supplement the power balance and smooth flow of the circuit. The final output effect is fed back to the circuit to obtain more accurate real-time correction, and Kalman filter or other optimization controllers can be added.

[0102] Specifically, in the step S104, the following steps are included:

[0103] In step S1041, the local controller stores the classification and identification results of different types of loads.

[0104] Step S1042, each local controller receives the preset reference power provided by the global controller, combines the classification recognition result, and performs decoupling, power distribution and quadrant conversion processing calculation to obtain tracking values, and controls the tracking values through the local control loop and tracks the tracking values through the phase-locked loop, and stores and monitors the obtained tracking effect.

[0105] Specifically, the local controller obtains the reference data provided by the global controller, and performs a series of calculations such as decoupling, power distribution and quadrant conversion to obtain the values that should be tracked by the unit, and performs individual control. The inverter local control loop of each power supply is as shown in the figure. Figure 6 As can be seen from the figure, it is a control method for a single power supply, similar to droop control, which links voltage, frequency and power. Its control loop first performs power control and then performs current control, and its voltage generally remains unchanged.

[0106] In this embodiment, the active power and reactive power reference values in the micro-grid system provide current reference values for subsequent current control, and use a controller for tracking. The phase is different for different vertices, and the power supply can be obtained by itself, and the load needs to be obtained by a phase-locked loop. The phase-locked loop structure is as shown in the figure. Figure 7 It should be noted that the phase-locked loop of the present embodiment is a commonly used element in the field, and its specific working principle will not be specifically explained here. The phase-locked loop of the present embodiment uses Figure 7 to track the values, and the corresponding power and current are obtained by real-time tracking and calculation of the frequency and voltage.

[0107] Figure 8 is a transfer function diagram of the embodiment of the present application. The transfer function follows the phase-locked loop and is a collection of transfer functions of the controller, the power supply, the inverter, the voltage stabilizer and the smoothing circuit, which can display the voltage and current relationship between the transfer functions and the points, and is used for reference calculation of related parameters.

[0108] In this embodiment, the circuit parameters are obtained by a signal acquisition and processing circuit. The signal acquisition and processing circuit comprises an acquisition unit, a processing unit and a transmission unit. The circuit parameters include voltage, current, active power, reactive power and frequency.

[0109] Specifically, the data processing chip of the acquisition unit can use S3C44B0, which is described in Figure 9 . The kernel, clock and pulse width modulation (PWM) of the data processing chip are used for detection, processing, input and output, storage and alarm functions; at the same time, a keyboard, a display and the like can be connected, and all detected data can be transmitted and saved.

[0110] In the embodiment, the processing unit is a CD4021 incorporated into the outgoing processing chip, which is used for processing complex and diverse signals. The CD4021 chip is an eight-bit register, which can be input and output. The output can only be used in a serial manner, while the input can be in a serial manner and a parallel manner. The chip uses a digital signal processor D5 for parallel input, controls through a pin P / S, and then outputs through a pin of 5-7 bits. The output of the three-bit pin is connected to the corresponding three pins of the S3C44B0 chip. The driving signal of the CD4021 chip is controlled by a pin C and a pin P / S. When the pin P / S is low, the signal can be input in a serial manner, and vice versa, the signal can be input in a parallel manner. Finally, the signal is sent to the processor through a serial line Q8. The corresponding data can be obtained by reading the value of the I / O port control register, and the data processing flow is as shown in Figure 10

[0111] In the embodiment, the signal transmission or communication module of the transmission unit adopts a GPS module and a GPRS transmission module. Specifically, a GPS positioning system of a GPS25-LVS series or other software is selected. Figure 11 is a schematic diagram of GPS pins of the embodiment of the application. It should be noted that when the GPS is selected, multiple channels of the original element need to be connected in parallel, and multiple satellite signals need to be received to ensure the accuracy of positioning. The starting time, positioning time and search time need to be shortened, and the precision and accuracy are extremely high, and the failure rate should be within 10%. The real-time differential signal is better when positioning, the precision can reach about 20mRMS, and the order of magnitude reaches 10-6 seconds. At the same time, the baud rate can be adjusted and controlled by a self-defined program. The working temperature, rated voltage and current range need to be suitable for the micro-grid.

[0112] In the embodiment, the GPRS module can perform information packet switching on the transmission system of the wireless network, and can transmit signals at high speed and high efficiency. A GPRS interface circuit is used to receive and process signals, and the urban general network and the self-equipment are used for cooperation transmission, so that the purpose of timely transmission of circuit signals is achieved.

[0113] Figure 12 is a schematic diagram of information transmission of the embodiment of the application. As shown in the figure: GGSN is a GPRS gateway support node, SGSN is a GPRS service support node, PCU is a packet control unit, BSC is a base station controller, BTS is a base station transceiver, MSC is a mobile service switching center, and PSTN / ISDN / PLMN is a public switched telephone network / integrated services digital network / public land mobile telephone network.

[0114] ​In a specific embodiment, the GPRS circuit module selects the MC35i chip, when using the GPRS receiving module, the first ten pins are power supply interface and GND, the 15th and 31st pins are start and stop interfaces for controlling the start and stop of the components. In use, in order to prevent accidents, the MC35i chip is equipped with a 32nd pin for observing whether it is working normally, and the 32nd pin can be connected with an LED for preliminary detection by whether it emits light. When the MC35i chip works, the 16th-23rd pins are connected with the single-chip microcomputer, and the single-chip microcomputer is programmed for operation; the rich pin interfaces and different functions of the MC35i chip fully meet the requirements of the embodiment for GPRS input and output processing.

[0115] In a specific embodiment, the structure of the power distribution network (A) and the power supply side (B) is as shown in Figure 13 As can be seen from the figure, the power supply load relationship of the power grid structure is complex, and the power supply load relationship can be constructed by graph theory model through the topological relationship of the embodiment, and based on the consensus control and Jacobian matrix, the load is classified and the corresponding load power is deployed, so as to obtain suitable power distribution and efficient control effect.

[0116] Specifically, the signal processing mode of the power grid structure is similar to that of the conventional microgrid, and it is necessary to track the parameter requirements between the power supply end and the load end in real time. For example, the voltage amplitude and phase of the slack node are known, the active power and reactive power requirements of the load node are known, and the active power and voltage amplitude of the power generation end node are known, and the calculation and measurement methods are slightly different. The data collected by the sensor are analyzed in amplitude and phase, and then decoupled and regulated in real time, and the results are accumulated, and the total amount of the power supply end and the power supply end is obtained; after a series of data processing such as filtering, rectification, linearization and the like, it enters the power matching and regulation link. In this link, the supply-demand matching and optimal scheduling are carried out, and the results are output to the specific reference value. The reference value is transmitted to the controller through the signal, the controller obtains the control input, and then the sine pulse width modulation (SPWM) and gate signal are transmitted to the inverter connected with each power source for execution, and finally the ideal output result is realized, that is, the power distribution meeting the load requirements.

[0117] The excellent communication efficiency and advanced topology structure of the embodiment accelerate the overall control loop speed, respond faster, and improve problems such as partial oscillation and loss in the system; and for the current extreme weather, sudden peak and valley power consumption, the embodiment is applicable to various power sources and loads in any region, terrain, and weather, has a wide range of applications, strong robustness, simple and accurate mathematical model, and fast and simple classification and partition; in addition, the embodiment can also carry effective fault protection measures, has extremely small time delay, can determine the parameter uncertainty range, perform linear programming calculation and solution on the model, and according to the optimization algorithm of feasibility and uncertainty in the calculation, the optimization effect is good, so that each structure in the micro-grid cooperates to exchange energy and realizes cost minimization.

[0118] In the embodiment, a micro-grid load power allocation device based on graph theory is also provided. The device system is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is contemplated.

[0119] The present application provides a micro-grid load power allocation device based on graph theory, as shown in Figure 14 The device comprises:

[0120] The graph theory model establishing module 1401 is configured to establish a graph theory model of the topological relationship of each load of the micro-grid based on graph theory;

[0121] The Jacobian matrix constructing module 1402 is configured to perform consensus control on each load of the graph theory model based on a preset consensus algorithm, obtain a corresponding dynamic quantity control equation, and construct a Jacobian matrix based on the dynamic quantity control equation, wherein the values of each row and column in the Jacobian matrix are used to determine the type of the load;

[0122] The load classification and identification module 1403 is configured to perform load classification and identification on the Jacobian matrix based on a preset determination method, and obtain the type of the corresponding load;

[0123] The hierarchical power allocation module 1404 is configured to obtain the circuit parameters corresponding to different types of loads, and perform hierarchical power allocation on the corresponding loads through a preset reference power.

[0124] The further function description of each module is the same as the corresponding embodiment described above, and will not be described again. The micro-grid load power allocation device based on graph theory has multiple advantages, including:

[0125] 1. In view of the strong uncertainty of the power supply end, the large floating of the power consumption end and the extreme disaster weather, the high-precision power consumption device can be accurately classified and regulated, the topology model is a multi-field combination and algorithm upgrade version, the feasibility is stronger, and the application range is wider;

[0126] 2. In view of the unified coordination control without grading in the traditional power grid, the embodiment of the application adopts grading, classification, parameter division and algorithm, and carries out targeted regulation, which is high in efficiency, power saving and response speed.

[0127] 3. In view of the problems of large and complex power grid disorder, the load is simplified and classified and data statistics are carried out, and real-time monitoring and alarm can be realized; the matrix size of the embodiment of the application increases with the increase of the system order, but the calculation amount increases little, and the future expansion and upgrading are facilitated.

[0128] The embodiment of the application also provides a computer device, please refer to Figure 15 , Figure 15 is a structural schematic diagram of the above-mentioned controller provided by the optional embodiment of the application, as shown in Figure 15 , the controller comprises one or more processors 10, a memory 20 and an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are communicatively connected to each other by different buses, and can be installed on a common mainboard or in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in the memory or memory to display GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memories, if necessary. Similarly, multiple computer devices can be connected, each device providing part of the necessary operation (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 15 In the above-mentioned computer device, the processor 10 is taken as an example.

[0129] The processor 10 can be a central processor, a network processor or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic gate array, a general array logic or any combination thereof.

[0130] The memory 20 stores instructions executable by the at least one processor 10, so that the at least one processor 10 executes the method shown in the above-mentioned embodiment.

[0131] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely with respect to the processor 10, which can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0132] The memory 20 can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk. The memory 20 can also include a combination of the above-mentioned types of memories.

[0133] The controller further includes a communication interface 30 for the master chip to communicate with other devices or communication networks.

[0134] The embodiments of the present application also provide a computer readable storage medium. The above-mentioned method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or implemented as computer code stored in a remote storage medium or a non-transitory machine readable storage medium and stored in a local storage medium to be downloaded through a network, so that the method described herein can be processed by such software on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, and the like. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, the processor, the microprocessor master chip, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method shown in the above-mentioned embodiments.

[0135] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be suggested by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the appended claims.

Claims

1. A graph-based microgrid load power dispatching method, characterized in that, The method comprises: a graph theory model is established based on the topological relationship of each load of the micro-grid; a preset consensus algorithm is used to perform consensus control on each load of the graph theory model, a corresponding dynamic quantity control equation is obtained, and a Jacobi matrix is constructed based on the dynamic quantity control equation, wherein the numerical values of each row and column in the Jacobi matrix are used to determine the type of the load; a preset determination method is used to perform load classification and identification on the Jacobi matrix, and the type of the corresponding load is obtained; circuit parameters corresponding to different types of loads are obtained, and hierarchical power allocation is performed on the corresponding loads through a preset reference power; wherein the process of performing consensus control on each load of the graph theory model based on the preset consensus algorithm, obtaining the corresponding dynamic quantity control equation, and constructing the Jacobi matrix based on the dynamic quantity control equation comprises: a Laplace matrix corresponding to the graph theory model is determined; initial values of internal dynamic quantities in the preset Laplace matrix of the power supply and the load in the power grid are determined; a distributed consensus algorithm is used, the initial values of the internal dynamic quantities of the graph theory model and the Laplace matrix are combined, and a dynamic quantity control equation of each load is obtained through a dynamic variable time domain response; at any time, Taylor expansion is performed on the dynamic quantity control equation to obtain a real-time Taylor expansion function of each equation, and a Jacobi matrix is constructed based on the Taylor expansion function to obtain a Jacobi matrix containing preset parameters.

2. The graph theory based microgrid load power dispatching method of claim 1, wherein, The preset determination method comprises determining the type of the corresponding load based on the distribution range of the numerical values of each row and column of the Jacobi matrix or the change rate of the absolute value of the derivative of the Jacobi matrix, wherein the derivative of the Jacobi matrix includes a first derivative and a second derivative.

3. The graph theory based microgrid load power dispatching method of claim 1, wherein, The hierarchical power allocation of the load is performed through the power grid control system, wherein the power grid control system is a two-level controller, including a global controller and a plurality of local controllers.

4. The graph theory based microgrid load power dispatching method of claim 3, wherein, The process of obtaining circuit parameters corresponding to different types of loads and performing hierarchical power allocation on the corresponding loads through a preset reference power comprises: the local controller stores the classification and identification results of different types of loads; each local controller receives a preset reference power provided by the global controller, combines the classification and identification results, and performs decoupling, power distribution, and quadrant conversion processing and calculation to obtain tracking values, performs corresponding control on the tracking values through a local control loop, and performs corresponding tracking on the tracking values through a phase-locked loop, and stores and monitors the obtained tracking effect.

5. The graph theory based microgrid load power dispatching method of claim 1, wherein, The circuit parameters are obtained through a signal acquisition and processing circuit, and the signal acquisition and processing circuit comprises an acquisition unit, a processing unit, and a transmission unit; the circuit parameters comprise voltage, current, active power, reactive power, and frequency.

6. The graph theory based microgrid load power dispatching method of claim 1, wherein, The demand for the preset reference power comprises the overall output expected requirement of the micro-grid power supply and the power demand of each load.

7. A graph-based microgrid load power dispatching device, characterized in that, The device comprises: a graph theory model establishment module for establishing a graph theory model based on the topological relationship of each load of the micro-grid; a Jacobi matrix construction module for performing consensus control on each load of the graph theory model based on a preset consensus algorithm, obtaining a corresponding dynamic quantity control equation, and constructing a Jacobi matrix based on the dynamic quantity control equation, wherein the numerical values of each row and column in the Jacobi matrix are used to determine the type of the load; The load classification identification module is configured to perform load classification identification on the Jacobian matrix based on a preset determination mode to obtain a type corresponding to the load. The hierarchical power allocation module is configured to obtain circuit parameters corresponding to different types of loads and perform hierarchical power allocation on the corresponding loads by using a preset reference power. The process of performing consensus control on each load of the graph theory model based on a preset consensus algorithm to obtain a dynamic quantity control equation and constructing the Jacobian matrix based on the dynamic quantity control equation includes: determining a corresponding Laplacian matrix according to the graph theory model; determining initial values of internal dynamic quantities in the preset Laplacian matrix of the power source and the load in the power grid; adopting a distributed consensus algorithm, combining the initial values of the internal dynamic quantities of the graph theory model and the Laplacian matrix, and obtaining a dynamic quantity control equation of each load through a time-domain response of a dynamic variable; performing Taylor expansion on the dynamic quantity control equation at any time to obtain a real-time Taylor expansion function of each equation, and constructing the Jacobian matrix based on the Taylor expansion function to obtain the Jacobian matrix containing preset parameters.

8. A computer device, comprising: The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the graph theory-based micro-grid load power allocation method in any one of claims 1 to 6. The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the graph theory-based micro-grid load power allocation method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, ​

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