Global optimization method for quality of electric energy of micro-grid

Abstract

A global optimization method for the quality of electric energy of a micro-grid. The method comprises: on the basis of an equivalent virtual impedance of an inverter of each node in a micro-grid and a local load of each node, establishing a node admittance matrix for the micro-grid (S1); on the basis of a weight value assigned to each node in the micro-grid and a voltage distortion rate of each node, constructing an objective function for the micro-grid (S2); on the basis of the node admittance matrix and the remaining capacity of the inverter of each node, constructing a constraint condition for harmonic modulation of each node in the micro-grid (S3); and in response to the constraint conditions being met, analyzing an optimal solution to the objective function, and on the basis of the optimal solution, adjusting the equivalent virtual impedance on each node (S4).

Description

Global Optimization Method for Microgrid Power Quality

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on July 18, 2023, with application number 202310878580.3, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of power supply technology, for example, to a global optimization method for microgrid power quality. Background Art

[0003] A microgrid is a small power network that integrates distributed energy resources and loads. It operates in two modes, island and grid-connected, with flexible switching. With the increasing demand for local consumption of distributed energy resources, microgrid construction has entered a period of rapid development. However, the increasing number and power of nonlinear loads in microgrids has led to a significant increase in voltage harmonic distortion (VHD) in microgrids.

[0004] Currently, harmonic control in microgrids mostly uses similar measures to those used in traditional distribution networks, primarily using specialized harmonic control equipment such as active filters. However, since microgrids are primarily used to meet power needs in remote areas, installing specialized voltage quality control equipment is constrained by factors such as economic costs.

[0005] Summary of the Invention

[0006] An embodiment of the present application provides a global optimization method for the power quality of a microgrid. The problem to be solved is that currently, microgrids solve the microgrid harmonic problem by adding special voltage quality management equipment. However, this method is constrained by factors such as economic costs and is not suitable for power demand in remote areas.

[0007] On the one hand, the present application provides a method for global optimization of microgrid power quality, comprising:

[0008] Establishing a node admittance matrix of the microgrid according to the equivalent virtual impedance of the inverter of each node of the microgrid and the local load of each node;

[0009] Constructing an objective function of the microgrid according to a weight value assigned to each node in the microgrid and a voltage distortion rate of each node;

[0010] Constructing a constraint condition for harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node;

[0011] In response to satisfying the constraint condition, the objective function corresponding to each particle in the particle set is analyzed, an optimal solution of the objective function is determined, and the equivalent virtual impedance of the inverter at each node is adjusted according to the optimal solution, wherein a set of values ​​of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle.

[0012] In some embodiments, constructing the objective function of the microgrid according to the weight value assigned to each node in the microgrid and the voltage distortion rate of each node includes:

[0013] Determining the voltage distortion rate of each node according to the fundamental voltage effective value and the harmonic voltage effective value of each node;

[0014] The voltage distortion rate of each node is multiplied by the weight value of each node and then summed to obtain the objective function of the microgrid.

[0015] In some embodiments, the objective function of the microgrid is:

[0016] Wherein, N represents the number of nodes in the microgrid, a j is the weight value corresponding to the j-th node, THD j is the voltage distortion rate corresponding to the j-th node;

[0017] In some embodiments, the voltage distortion rate is:

[0018] Among them, U 1,j is the effective value of the fundamental voltage at the jth node, is the effective value of the hth harmonic voltage at the jth node; Y h is the node admittance matrix under the hth harmonic, is the hth harmonic current at the jth node.

[0019] In some embodiments, constructing the constraint conditions for harmonic modulation of each node of the microgrid based on the node admittance matrix and the remaining capacity of the inverter of each node includes:

[0020] determining a remaining capacity of the inverter according to the rated capacity of the inverter, the active power output by the inverter, and the reactive power output by the inverter;

[0021] determining a compensation capacity of the inverter according to a fundamental voltage of the inverter and each harmonic current flowing through the inverter;

[0022] The constraint condition includes that the compensation capacity is smaller than the remaining capacity.

[0023] In some embodiments, the remaining capacity is:

[0024] Among them, S R is the remaining capacity of the inverter, S N is the rated capacity of the inverter, P is the active power output by the inverter, and Q is the reactive power output by the inverter;

[0025] The compensation capacity is:

[0026] Among them, S H is the compensation capacity of the inverter, U 1,rms is the fundamental voltage of the inverter, I h,rms is the harmonic current flowing through the inverter.

[0027] In some embodiments, in response to satisfying the constraint condition, analyzing the objective function corresponding to each particle in the particle set, determining an optimal solution to the objective function, and adjusting the equivalent virtual impedance of the inverter at each node according to the optimal solution includes:

[0028] Analyzing the objective function corresponding to each particle in the particle set, comparing the objective functions corresponding to all particles in the particle set, determining a particle corresponding to the smallest objective function, and recording the particle as the global optimal particle;

[0029] Inputting a randomly generated initial particle, an initial iteration speed, and an initial optimal particle into a particle update model, and updating the first particle in the particle set, wherein the particle update model is used to update a next particle using a previous particle and the iteration speed of the previous particle;

[0030] Comparing the updated objective function of the first particle with the objective function of the initial optimal particle;

[0031] In response to the objective function of the first particle after update being smaller than the objective function of the initial optimal particle, replacing the initial optimal particle with the first particle after update and recording the particle as a historical optimal particle;

[0032] Inputting the updated particle, the historical optimal particle, and the current iteration speed into the particle update model to obtain the next iteration speed and the next updated particle, and cyclically updating all the particles in the particle set in sequence;

[0033] After each update, the objective function corresponding to the updated particle is compared with the historical optimal particle;

[0034] In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the historical optimal particle, replacing the historical optimal particle with the updated particle;

[0035] Comparing the objective function corresponding to the updated particle with the objective function corresponding to the global optimal particle;

[0036] In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the global optimal particle, replacing the global optimal particle with the updated particle;

[0037] After all the particles in the particle set are updated, an iterative update of the particle set is completed, and the particle set is updated a specified number of times to obtain the final global optimal particle;

[0038] The equivalent virtual impedance at each node in the microgrid is adjusted according to the global optimal particle.

[0039] In some embodiments, after each update, comparing the objective function corresponding to the updated particle with the historical optimal particle includes:

[0040] After each update, determine whether the value of the particle is within a specified range;

[0041] In response to the value of the particle exceeding the specified range, the loop update ends;

[0042] In response to the value of the particle being within the specified range, the objective function corresponding to the updated particle is compared with the historical optimal particle.

[0043] In some embodiments, after each update, after comparing the objective function corresponding to the updated particle with the historical optimal particle, the microgrid power quality global optimization method further includes:

[0044] In response to the objective function corresponding to the updated particle being greater than or equal to the objective function corresponding to the historical optimal particle, marking the update as an update failure, and counting the number of consecutive update failures;

[0045] In response to the number of consecutive update failures being less than the failure number threshold, continuing the cyclic update;

[0046] In response to the number of consecutive update failures being greater than or equal to a failure number threshold, switching the current particle into a learning function mode to obtain the learned particle, resetting the number of consecutive update failures, and replacing the current particle with the learned particle and then entering the cyclic update;

[0047] In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the historical optimal particle, the number of consecutive update failures is reset.

[0048] In some embodiments, in response to the number of consecutive update failures being greater than or equal to a failure number threshold, switching the current particle into a learning function mode, obtaining the learned particle, resetting the number of consecutive update failures, and replacing the current particle with the learned particle and then entering the cyclic update, includes:

[0049] In response to the number of consecutive update failures being greater than or equal to a failure number threshold, generating a random number, and determining whether the random number is less than a learning probability factor, wherein the learning probability factor is calculated based on a position sequence number of the current particle in the particle set and a total number of particles in the particle set;

[0050] In response to the random number being less than the learning probability factor, randomly selecting n particles in the particle set that are behind the current particle and have not been updated;

[0051] Comparing the objective functions corresponding to the n selected particles, screening out the particle corresponding to the smallest objective function, recording the particle as the optimal learning particle, and replacing the current particle with the optimal learning particle;

[0052] In response to the random number being greater than or equal to the learning probability factor, replacing each data in the current particle with the best individual historical data of the data;

[0053] The number of consecutive update failures is reset, and the updated current particle is put into the cyclic update again.

[0054] In some embodiments, after comparing the objective function corresponding to the updated particle with the objective function corresponding to the global optimal particle, the microgrid power quality global optimization method further includes:

[0055] In response to the objective function corresponding to the updated particle being greater than or equal to the objective function corresponding to the global optimal particle, the global optimal particle remains unchanged.

[0056] On the other hand, the present application provides a microgrid power quality global optimization system, comprising:

[0057] a node admittance matrix building module, configured to build a node admittance matrix of the microgrid according to an equivalent virtual impedance of an inverter of each node of the microgrid and a local load of each node;

[0058] an objective function establishment module, configured to construct an objective function of the microgrid based on a weight value assigned to each node in the microgrid and a voltage distortion rate of each node;

[0059] a constraint condition construction module, configured to construct a constraint condition for harmonic modulation of each node of the microgrid based on the node admittance matrix and the remaining capacity of the inverter of each node;

[0060] An optimal solution finding module is configured to analyze the objective function corresponding to each particle in the particle set in response to satisfying the constraint condition, determine an optimal solution to the objective function, and adjust the equivalent virtual impedance of the inverter at each node according to the optimal solution, wherein a set of values ​​of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] FIG1 is a flow chart showing a method for global optimization of microgrid power quality according to an embodiment of the present application;

[0062] FIG2 is a schematic diagram showing the analysis process of the optimal solution of the objective function in an embodiment of the present application;

[0063] FIG3 shows a schematic structural diagram of a single-phase island microgrid multi-bus system;

[0064] FIG4 shows a schematic diagram of the voltage harmonic distortion control effect of the bus 6 in FIG3 ;

[0065] FIG5 shows a schematic diagram of the capacity change of each inverter in the microgrid;

[0066] FIG6 shows a schematic structural diagram of a microgrid power quality global optimization system according to an embodiment of the present application. DETAILED DESCRIPTION

[0067] The specific embodiments of the present application are described below with reference to the accompanying drawings.

[0068] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0069] Throughout this specification, references to terms such as "an embodiment," "one embodiment," and "an implementation" mean that the features, structures, materials, or characteristics described in connection with that embodiment or implementation are included in at least one embodiment or implementation of the present application. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or implementation. Furthermore, the described features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or implementations.

[0070] FIG1 shows a flow chart of a method for global optimization of microgrid power quality according to an embodiment of the present application. The method comprises:

[0071] S1: Establishing a node admittance matrix of the microgrid according to the equivalent virtual impedance of the inverter of each node of the microgrid and the local load of each node;

[0072] For example, a power network is a system consisting of static components such as transmission lines, power transformers, and parallel (series) capacitors. The node admittance matrix is ​​a description of the power network. Equivalent virtual impedance control introduces a virtual impedance control link into the inverter current or voltage control loop, changing the inverter's output voltage and current characteristics so that the inverter behaves like a power supply device with a series physical impedance. This approach can effectively utilize existing inverters in a microgrid to effectively manage grid harmonics, avoiding the cost of installing additional power quality management equipment.

[0073] In this embodiment, the relationship between the node admittance matrix Y and the harmonic voltage U and harmonic current I is YU=I.

[0074] The node admittance matrix under each harmonic is Y h , Y h The harmonic current of the jth node under the hth harmonic is The harmonic voltage is The relationship between the three is The microgrid is subject to harmonic power flow constraints during operation. After establishing the harmonic equivalent network, the node admittance matrix under each harmonic can be obtained, for example, as:

[0075] in, is the phasor representation of the harmonic voltage at the jth node under the hth harmonic, is the phasor representation of the harmonic current at the jth node under the hth harmonic, is the inherent impedance (i.e., local load) of the jth node under the hth harmonic without adding virtual impedance, is the applied equivalent virtual impedance.

[0076] S2: constructing an objective function of the microgrid according to a weight value assigned to each node in the microgrid and a voltage distortion rate of each node;

[0077] For example, different nodes may have different power quality requirements. Therefore, different weight coefficients are assigned to different nodes to achieve differentiated voltage harmonic distortion rate management effects. Given the limited inverter management capabilities, the power quality requirements of important nodes are prioritized, improving the power satisfaction of system users. To minimize harmonic interference, the weight value of each node can be multiplied and added with the node voltage distortion rate, constructing an objective function. The voltage distortion rate of each node can be comprehensively considered in the objective function.

[0078] S3: Constructing a constraint condition for harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node;

[0079] For example, during harmonic compensation, the inverter's compensation capacity is constrained by the inverter's remaining capacity. The compensation capacity cannot exceed the remaining capacity, preventing inverter overload. Furthermore, the inverter's compensation capacity is calculated based on the harmonic currents, which are related to the node admittance matrix.

[0080] S4: Under the condition that the constraint conditions are met, analyze the objective function corresponding to each particle in the particle set, determine the optimal solution of the objective function, and adjust the equivalent virtual impedance of the inverter at each node according to the optimal solution, wherein a set of values ​​of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle.

[0081] In this embodiment, based on the node admittance matrix and the remaining capacity of the inverter of each node, the constraint conditions for harmonic modulation of each node of the microgrid are constructed, the equivalent virtual impedance is used as a variable in the node admittance matrix, and a weight value is assigned to each node in the microgrid. Taking into account the different requirements of different nodes for power quality, the objective function of the microgrid is constructed based on the weight value and the voltage distortion rate of each node. The goal is to minimize the value of this objective function. When the constraint conditions are met, the optimal solution of the objective function is analyzed, and the equivalent virtual impedance of the inverter of each node is adjusted according to the optimal solution, so as to achieve global optimization management of differentiated voltage harmonic distortion. There is no need to install special voltage quality management equipment. Effective management of harmonics can be achieved through the inverter and the construction of equivalent virtual impedance on the inverter.

[0082] In some embodiments, it should be noted that if there is no inverter at a node, that is, the inverter is not connected, then the equivalent virtual impedance of the inverter does not exist at the node. Therefore, there is no need to adjust the equivalent virtual impedance of the inverter at the node as a variable.

[0083] In one embodiment of the present application, constructing the objective function of the microgrid according to the weight value assigned to each node in the microgrid and the voltage distortion rate of each node includes:

[0084] The voltage distortion rate at each node is determined based on the RMS fundamental voltage and harmonic voltage at each node. The voltage distortion rate (also known as the voltage harmonic distortion rate) is a measure of the degree of harmonic pollution in a power system. It represents the relative magnitude of the harmonic components to the fundamental component in the voltage waveform. Harmonic pollution can cause the following hazards to the power system: reduced equipment efficiency, equipment overheating, interference with communication systems, impact on the accuracy of electricity meters, and malfunction of protective devices.

[0085] Exemplarily, the voltage distortion rate is:

[0086] Among them, U 1,j is the effective value of the fundamental voltage at the jth node, is the effective value of the hth harmonic voltage at the jth node, where h represents the order of harmonic distortion. The calculation starts from the 3rd harmonic distortion and ends at the 9th harmonic distortion. The voltage distortion rate corresponding to the 3rd to 9th and the odd-numbered harmonic distortions in between is calculated.

[0087] The voltage distortion rate of each node is multiplied by the weight value of each node and then summed to obtain the objective function of the microgrid.

[0088] Exemplarily, the objective function of the microgrid is:

[0089] Wherein, N represents the number of nodes in the microgrid; a j is the weight value corresponding to the jth node, and the sum of the weight coefficients of each node is 1; THD j is the voltage distortion rate corresponding to the j-th node.

[0090] In one embodiment of the present application, constructing the constraint conditions for harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node includes:

[0091] determining a remaining capacity of the inverter according to the rated capacity of the inverter, the active power output by the inverter, and the reactive power output by the inverter;

[0092] Exemplarily, the remaining capacity is:

[0093] Among them, S R is the remaining capacity of the inverter, S N is the rated capacity of the inverter, P is the active power output by the inverter, and Q is the reactive power output by the inverter.

[0094] determining a compensation capacity of the inverter according to a fundamental voltage of the inverter and each harmonic current flowing through the inverter;

[0095] Exemplarily, the compensation capacity is:

[0096] Among them, S H is the compensation capacity of the inverter of the distributed power source, U 1,rms is the fundamental voltage of the inverter. During the optimization process, the fundamental voltage can be approximately considered unaffected. The fundamental voltage of the capacitor connected in parallel to each inverter when not controlled can be selected as the fundamental voltage during the optimization process. Therefore, the result calculated using this fundamental voltage can be approximately regarded as the compensation capacity. h,rms To calculate the harmonic current flowing through the inverter, the fundamental voltage and harmonic current of the inverter at any node can be substituted into the formula to calculate the compensation variable, where h = 3, 5, 7 and 9, that is, the squares of the 3rd, 5th, 7th and 9th harmonic currents are summed.

[0097] The constraint condition includes that the compensation capacity is smaller than the remaining capacity.

[0098] As shown in Figure 2, flag = 0 means that the number of consecutive update failures is reset to 0, flagi means the number of consecutive update failures of the i-th particle, and m is the failure threshold. When the objective function of the i-th particle relative to the historical optimal particle has not improved in more than m iterations, it is considered that the particle may be trapped in a local optimal solution. At this time, the learning function mode is activated so that the i-th particle can learn from other particles and help it escape the local optimal solution. i represents the i-th particle in the particle set, w represents the inertia weight, k is the number of iterations, and K max is the maximum number of iterations. min With X max Represents the minimum and maximum values ​​of each dimension variable of the particle, d is the d-th data or d-dimensional data in a particle, v max is the maximum iteration speed, D is the total number of data in a particle, f(x i ) is the data x in the particle iThe value obtained by substituting other data in the same particle into the objective function, pbest i The best value in history, gbest i Global optimal value, f(pbest i ) is the objective function value corresponding to the historical optimal value, f(gbest i ) is the objective function value corresponding to the global optimal value, and ps is the total number of particles in the particle set.

[0099] In one embodiment of the present application, as shown in FIG2 , while the constraint condition is satisfied, analyzing the objective function corresponding to each particle in the particle set, determining the optimal solution of the objective function, and adjusting the equivalent virtual impedance of the inverter at each node according to the optimal solution includes:

[0100] Analyzing the objective function corresponding to each particle in the particle set, comparing the objective functions corresponding to all particles in the particle set, determining a particle corresponding to the smallest objective function, and recording the particle as the global optimal particle;

[0101] For example, before starting the optimization analysis, the parameters in the process need to be initialized. Assume there are 100 particles in the particle set, and each particle contains five pairs of data (an equivalent virtual impedance has a real part and an imaginary part, forming a pair of data), totaling ten data. Substitute these ten data into the calculation of the objective function to calculate the value of the objective function. Calculate the objective function values ​​for 100 particles, and then find the minimum objective function value. The particle corresponding to this minimum objective function value is the global optimal particle among the current 100 particles.

[0102] Inputting a randomly generated initial particle, an initial iteration speed, and an initial optimal particle into a particle update model, and updating the first particle in the particle set, wherein the particle update model is used to update a next particle using a previous particle and the iteration speed of the previous particle;

[0103] For example, the randomly generated initial particle, initial iteration speed, and initial optimal particle are mainly used to start the update of the first particle. Because the data of the previous iteration speed, previous particle, and historical optimal particle do not exist for the first particle, random generation is required. In addition, the particle update model is:

[0104] Where, is the d-dimensional variable of the i-th particle in the k+1-th iteration; is the d-dimensional velocity of the i-th particle in the k+1-th iteration, where 1≤d≤D, d is the d-th data in the particle, and D represents the total amount of data in the particle; ω is the inertia weight; is the d-dimensional velocity of the i-th particle in the k-th iteration; c1 is the acceleration coefficient; r1 is a random number in the range [0, 1]; is the dth value corresponding to the historical optimal fitness of the fi(d)th particle in the kth iteration.

[0105] In addition, it should be noted that, since a particle contains multiple data, the multiple data in a particle need to be updated one by one, and the next step can be performed only after all the data in a particle are updated.

[0106] Comparing the updated objective function of the first particle with the objective function of the initial optimal particle;

[0107] When the objective function of the first updated particle is less than that of the initial optimal particle, the updated particle replaces the initial optimal particle and is recorded as the historical optimal particle. However, when the objective function of the first updated particle is greater than or equal to that of the initial optimal particle, the initial optimal particle is recorded as the historical optimal particle. It should be noted that the particle updated by the particle update model is a new particle and is not among the 100 particles in the particle set. This is equivalent to expanding the particle set, increasing the number of particles and finding an optimal solution that is closer to the actual optimal particle.

[0108] Inputting the updated particle, the historical optimal particle, and the current iteration speed into the particle update model to obtain the next iteration speed and the next updated particle, and cyclically updating all the particles in the particle set in sequence;

[0109] For example, the above particle update model is continuously adopted, the update process is repeated, and 100 particles are updated one by one. In this update, 100 new particles will be obtained, making the particle data richer.

[0110] After each update, the objective function corresponding to the updated particle is compared with the historical optimal particle;

[0111] When the objective function corresponding to the updated particle is less than the objective function corresponding to the historical optimal particle, it means that a new optimal particle has been found. The historical optimal particle is replaced with the updated particle. At this time, the historical optimal particle is the newly obtained particle. This comparison can be performed each time to obtain the historical optimal solution.

[0112] The objective function corresponding to the updated particle is compared with the objective function corresponding to the global optimal particle. Since this new particle is not the data in the original 100 particles, the objective function value corresponding to the newly obtained particle is compared with the objective function value of the global optimal particle, and a particle better than the original global optimal particle may be found.

[0113] When the objective function corresponding to the updated particle is greater than or equal to the objective function corresponding to the global optimal particle, it means that the harmonic control effect of the original global optimal particle is still better, and the global optimal particle remains unchanged.

[0114] When the objective function corresponding to the updated particle is smaller than the objective function corresponding to the global optimal particle, it indicates that better data than the original global optimal particle has been found, and the global optimal particle is replaced with the updated particle.

[0115] Once all particles in the particle set have been updated, an iterative update of the particle set is complete, resulting in a new particle set. The original particle set has been overwritten by the new particle set. This new particle set is then updated again based on the new particle set until the particle set is updated a specified number of times, resulting in the final global optimal particle. For example, if the specified number of updates is 2000, then a global optimal particle is found in 2000*100 data points. This global optimal particle is closer to the true optimal particle, allowing the automatic optimization method to find a more effective optimal solution for regulating and controlling the equivalent virtual impedance of each node.

[0116] The equivalent virtual impedance at each node in the microgrid is adjusted according to the global optimal particle.

[0117] In this embodiment, when not overloaded, each particle simultaneously learns from the historically optimal particle and the global optimal particle of the swarm, continuously updating each particle to maximize the use of the inverter's remaining capacity for better harmonic control. This reduces the problems of increased circuit and equipment losses, and the impact on system stability caused by harmonics. Each particle represents a combination of all equivalent virtual impedances in the system, where each dimension of each particle represents the virtual impedance value of each inverter. By solving the constructed optimization equation, obtaining the optimized virtual impedance output value of each inverter, and distributing it to each inverter, effective harmonic distortion control can be achieved in the microgrid system.

[0118] In one embodiment of the present application, as shown in FIG2 , after each update, comparing the objective function corresponding to the updated particle with the historical optimal particle includes:

[0119] After each update, determine whether the value of the particle is within a specified range;

[0120] When the value of the particle exceeds the specified range, it indicates that an error occurs in the particle update. Since the particle exceeds the specified range, it is meaningless to continue the cycle, and the cycle update ends at this time.

[0121] When the value of the particle is within the specified range, the objective function corresponding to the updated particle is compared with the historical optimal particle.

[0122] In this embodiment, the values ​​in the updated particles are checked once to avoid continuing the optimization process when the limit is exceeded, thereby reducing unnecessary analysis processes.

[0123] In one embodiment of the present application, as shown in FIG2 , after each update, after comparing the objective function corresponding to the updated particle with the historical optimal particle, the microgrid power quality global optimization method further includes:

[0124] When the objective function corresponding to the updated particle is greater than or equal to the objective function corresponding to the historical optimal particle, the update is recorded as an update failure, and the number of consecutive update failures is counted;

[0125] For example, the purpose of counting the number of consecutive update failures is that when the fitness of a particle has not been improved during m iterations, it is considered that it may be trapped in a local optimal solution, and a learning function mode is executed on it, so that the particle has the opportunity to learn from the individual historical optimal values ​​of other particles in the corresponding dimensions in each dimension.

[0126] When the number of consecutive update failures is less than the failure number threshold, continuing the cyclic update;

[0127] When the number of consecutive update failures is greater than or equal to a failure threshold, the current particle is switched to a learning function mode to obtain the learned particle, the number of consecutive update failures is reset, and the learned particle replaces the current particle and is put into a cyclic update;

[0128] For example, since the constructed equation is nonlinear, solving it according to the previous embodiment can easily lead to local optima. To minimize this, when the number of consecutive update failures is greater than or equal to a failure threshold, this indicates that the optimization solution has fallen into a local optimum. At this point, the sequential update optimization mode is interrupted and a backward random optimization learning mode is inserted to break the current local optimum. For example, when m is 5, starting with the 40th particle, the objective function value corresponding to the updated particle is greater than or equal to the objective function value corresponding to the historical optimal particle. This occurs for five consecutive particles. At the 46th particle, the particle enters the learning mode.

[0129] When the objective function corresponding to the updated particle is smaller than the objective function corresponding to the historical optimal particle, the number of consecutive update failures is reset.

[0130] For example, although the objective function value corresponding to the updated particle is greater than or equal to the objective function value corresponding to the historical best particle starting from the 40th particle, the objective function value corresponding to the updated particle in the 43rd data is less than the objective function value corresponding to the historical best particle, the effect of the updated particle is improved, and the number of consecutive update failures is reset to 0.

[0131] In one embodiment of the present application, when the number of consecutive update failures is greater than or equal to a failure number threshold, switching the current particle into a learning function mode, obtaining the learned particle, resetting the number of consecutive update failures, and replacing the current particle with the learned particle and then entering the cyclic update, includes:

[0132] When the number of consecutive update failures is greater than or equal to the failure threshold, a random number is generated, and it is determined whether the random number is less than the learning probability factor, wherein the learning probability factor P ci It is calculated based on the position number of the current particle in the particle set and the total number of particles in the particle set.

[0133] For example, before each iteration, each particle is sorted in ascending order according to the corresponding objective function. ci =i′ / (2*N), where N represents the total number of particles, and i′ represents the corresponding particle’s position i in the sorting. When i is smaller, the better the solution result is, the smaller the probability of the particle learning from other particles is, and the fewer the number of particles learned is. On the contrary, the larger i is, the worse the solution result is, which gives it a larger learning probability factor P. ci , making it more likely to learn from other particles, and the number of particles learning is also greater. ciThe range is [1 / (2*N), 1 / 2].

[0134] When the random number is less than the learning probability factor, randomly selecting n particles in the particle set that are behind the current particle and have not been updated;

[0135] Where n = 2 + round(([N / 2] - 2) / N*i′), where N represents the total number of particles and i′ represents the i-th position of the corresponding particle in the sorting. [x] is the rounding function, where [x] represents the largest integer not exceeding x. The round function returns a value that is rounded to the specified number of decimal places.

[0136] Comparing the objective functions corresponding to the n selected particles, screening out the particle corresponding to the smallest objective function, recording the particle as the optimal learning particle, and replacing the current particle with the optimal learning particle;

[0137] When the random number is greater than or equal to the learning probability factor, each data in the current particle is replaced with the best historical data of the individual data;

[0138] For example, assume that a microgrid has five nodes, each node is connected to a distributed power supply and an inverter, and an equivalent virtual impedance is connected to the location where the inverter is connected to the bus. When the global optimality is not considered, but the harmonics at a node are considered, in order to reduce the harmonic distortion rate, while satisfying the inverter capacity constraint, the value of the equivalent virtual impedance that minimizes the voltage harmonic distortion rate is screened out from the historical operating data of the node, and this value is used as the individual historical optimal data.

[0139] Finally, the number of consecutive update failures is reset, and the updated current particle is put into the cyclic update again, and the cyclic update is re-entered to update the particles in the particle set one by one.

[0140] In this embodiment, a learning function is established for particles in the population. If a particle's fitness fails to improve after m iterations, it is considered to be stuck in a local optimum. A learning function is then applied to the particle, allowing each data point in the particle to learn from the individual historical optimal data points of other particles on the corresponding data points. Global control is implemented using a method that optimizes solution performance, effectively preventing the solution from being stuck in a local optimum and improving optimization efficiency.

[0141] As shown in FIG6 , in one embodiment of the present application, a microgrid power quality global optimization system is further provided, including:

[0142] a node admittance matrix building module 610 configured to build a node admittance matrix of the microgrid based on an equivalent virtual impedance of an inverter of each node of the microgrid and a local load of each node;

[0143] an objective function establishing module 620 configured to construct an objective function of the microgrid based on a weight value assigned to each node in the microgrid and a voltage distortion rate of each node;

[0144] a constraint condition construction module 630 configured to construct a constraint condition for harmonic modulation of each node of the microgrid based on the node admittance matrix and the remaining capacity of the inverter of each node;

[0145] The optimal solution finding module 640 is configured to analyze the objective function corresponding to each particle in the particle set in response to satisfying the constraint condition, determine the optimal solution of the objective function, and adjust the equivalent virtual impedance of the inverter at each node according to the optimal solution, wherein a set of values ​​of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle.

[0146] The microgrid power quality global optimization system in the embodiment of the present application has similar technical effects to the above-mentioned microgrid power quality global optimization method.

[0147] In one embodiment of the present application, FIG3 shows a schematic structural diagram of a single-phase island microgrid multi-bus system, and system-related parameters are shown in Table 1.

[0148] Step 1: Establish the harmonic equivalent network of the island microgrid system.

[0149] Step 2: Construct the corresponding objective function with the minimum average voltage harmonic distortion rate of each bus. The importance of each bus is the same, that is, a1 = a2 = a3 = a4 = a5 = a6 = 1 / 6. The objective function is:

[0150] Step 3: Establish capacity constraints and harmonic power flow constraints for the five inverters, using the virtual impedances of the five inverters as decision variables.

[0151] As shown in Figure 3, bus 6 is not connected to a distributed power source or inverter, which means that there is no equivalent virtual impedance at bus 6. Therefore, there is no need to adjust the equivalent virtual impedance at bus 6 as a variable, so the number of decision variables is five.

[0152] Table 1 Single-phase island microgrid system parameters

[0153] Step 4: Under the condition that the constraints are met, the objective function is optimized and solved. The relevant parameters are shown in Table 2.

[0154] The inverter equivalent virtual impedance is split into resistance and inductance, that is, the real part and the imaginary part are optimized separately, so the dimension of a single particle is 10. The variable X representing the resistance real The range is set to [0, 20], and the variable X representing the inductance imag The range is set to [0, -Lg].

[0155] The optimal solution obtained by the method described in this application is compared with the optimal solution obtained by the local control strategy without the optimization algorithm (the local control strategy imitates the traditional active filter and realizes the voltage harmonic distortion rate control by providing the smallest possible harmonic impedance path). At the 2nd second (s), each inverter in the system adopts the local control strategy, and the optimized equivalent virtual impedance value calculated by the global optimization control strategy is adopted at the 16th second. The bus 6 voltage harmonic distortion rate control effect is shown in Figure 4. In Figure 4, since the voltage sinusoidal curve appears many times within 1 second, when the unit time length of the time axis is short, the adjacent sinusoidal curves will be infinitely close to each other. When many sinusoidal curves are infinitely close, when the length of the time axis is set to the length shown in Figure 4, the voltage change curve presents the black area shown in Figure 4. It is difficult for the naked eye to distinguish each point value of the sinusoidal curve, so it is necessary to intercept and amplify the voltage curve within a certain time period that you want to observe. In Figure 4, a section of the voltage curve before 2s, before 16s, and before 20s is intercepted and amplified to observe the changes in voltage after harmonic pollution or regulation.

[0156] Table 2 Parameter settings in the optimization algorithm

[0157] Table 3 Harmonic distortion rate of each bus voltage under different control

[0158] At 2 seconds, the local control strategy was adopted. When the system reached stable operation, the busbar 6 harmonic voltage distortion rate was 4.18%. At 16 seconds, the globally optimized equivalent virtual impedance value was issued, and the distortion rate stabilized at 3.61%. The capacity changes of each inverter are shown in Figure 5.

[0159] The effect of controlling the harmonic distortion rate of each bus voltage is shown in Table 3. It can be seen that compared with the local control strategy, the global optimization control strategy can improve the voltage quality of each node in the system without overloading the inverter.

Claims

1. A method for global optimization of power quality of a microgrid, comprising: Establishing a node admittance matrix of the microgrid according to the equivalent virtual impedance of the inverter of each node of the microgrid and the local load of each node; Constructing an objective function of the microgrid according to a weight value assigned to each node in the microgrid and a voltage distortion rate of each node; Constructing the constraint conditions of harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node; In response to satisfying the constraint condition, analyzing the objective function corresponding to each particle in the particle set, determining the optimal solution of the objective function, and adjusting the equivalent virtual impedance of the inverter at each node according to the optimal solution, wherein one value of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle, and the particle set includes a plurality of particles; The objective function of the microgrid is constructed according to the weight value assigned to each node in the microgrid and the voltage distortion rate of each node, including: Determine the voltage distortion rate of each node according to the effective value of the fundamental voltage and the effective value of the harmonic voltage of each node; The voltage distortion rate of each node is multiplied by the weight value of each node and then summed to obtain the objective function of the microgrid.

2. The microgrid power quality global optimization method according to claim 1, wherein: The objective function of the microgrid is: Wherein, N represents the number of nodes in the microgrid, a j is the weight value corresponding to the jth node, THD j is the voltage distortion rate corresponding to the j-th node; The voltage distortion rate is: Among them, U 1,j is the effective value of the fundamental voltage at the jth node, is the effective value of the hth harmonic voltage at the jth node; Y h is the node admittance matrix under the hth harmonic, is the hth harmonic current at the jth node.

3. The microgrid power quality global optimization method according to claim 1, wherein: The step of constructing the constraint conditions for harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node includes: Determining the remaining capacity of the inverter according to the rated capacity of the inverter, the active power output by the inverter, and the reactive power output by the inverter; Determining the compensation capacity of the inverter according to the fundamental voltage of the inverter and each harmonic current flowing through the inverter; The constraint condition includes that the compensation capacity is smaller than the remaining capacity.

4. The microgrid power quality global optimization method according to claim 3, wherein: The remaining capacity is: Among them, S R is the remaining capacity of the inverter, S N is the rated capacity of the inverter, P is the active power output by the inverter, and Q is the reactive power output by the inverter; The compensation capacity is: Among them, S H is the compensation capacity of the inverter, U 1,rms is the fundamental voltage of the inverter, I h,rms is the harmonic current flowing through the inverter.

5. The microgrid power quality global optimization method according to claim 1, wherein: In response to satisfying the constraint condition, analyzing the objective function corresponding to each particle in the particle set, determining an optimal solution of the objective function, and adjusting the equivalent virtual impedance of the inverter of each node according to the optimal solution, including: Analyze the objective function corresponding to each particle in the particle set, compare the objective functions corresponding to all particles in the particle set, determine the particle corresponding to the smallest objective function, and record it as the global optimal particle; Inputting the randomly generated initial particle, initial iteration speed and initial optimal particle into a particle update model, and updating the first particle in the particle set, wherein the particle update model is used to update the next particle using the previous particle and the iteration speed of the previous particle; Compare the updated objective function of the first particle with the objective function of the initial optimal particle; In response to the objective function of the first particle after update being less than the objective function of the initial optimal particle, replacing the initial optimal particle with the first particle after update and recording it as a historical optimal particle; Inputting the updated particle, the historical optimal particle and the current iteration speed into the particle update model to obtain the next iteration speed and the next updated particle, and cyclically updating all the particles in the particle set in turn; After each update, comparing the objective function corresponding to the updated particle with the historical optimal particle; In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the historical optimal particle, replacing the historical optimal particle with the updated particle; Compare the objective function corresponding to the updated particle with the objective function corresponding to the global optimal particle; In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the global optimal particle, replacing the global optimal particle with the updated particle; Until all the particles in the particle set are updated, an iterative update of the particle set is completed, and the particle set is updated a specified number of times to obtain the final global optimal particle; According to the global optimal particle, the equivalent virtual impedance at each node in the microgrid is adjusted.

6. The microgrid power quality global optimization method according to claim 5, wherein: After each update, comparing the objective function corresponding to the updated particle with the historical optimal particle includes: After each update, determining whether the value of the particle is within a specified range; In response to the value of the particle exceeding the specified range, the loop update ends; In response to the value of the particle being within the specified range, the objective function corresponding to the updated particle is compared with the historical optimal particle.

7. The method for global optimization of power quality of a microgrid according to claim 5, wherein after each update, after comparing the objective function corresponding to the updated particle with the historical optimal particle, the method further comprises: In response to the objective function corresponding to the updated particle being greater than or equal to the objective function corresponding to the historical optimal particle, the update is recorded as an update failure, and the number of consecutive update failures is counted; In response to the number of consecutive update failures being less than a failure number threshold, continuing the cyclic update; In response to the number of consecutive update failures being greater than or equal to a failure number threshold, the current particle is switched to a learning function mode to obtain the learned particle, the number of consecutive update failures is reset, and the learned particle replaces the current particle and is put into a cyclic update; In response to the objective function corresponding to the updated particle being smaller than the objective function corresponding to the historical optimal particle, the number of consecutive update failures is reset.

8. The microgrid power quality global optimization method according to claim 7, wherein: In response to the number of consecutive update failures being greater than or equal to a failure number threshold, switching the current particle into a learning function mode, obtaining the learned particle, resetting the number of consecutive update failures, and replacing the current particle with the learned particle and then putting it into a cyclic update, including: In response to the number of consecutive update failures being greater than or equal to a failure number threshold, generating a random number, and determining whether the random number is less than a learning probability factor, wherein the learning probability factor is calculated based on a position number of the current particle in the particle set and a total number of particles in the particle set; In response to the random number being less than the learning probability factor, randomly selecting n particles in the particle set that are behind the current particle and have not been updated; Compare the objective functions corresponding to the selected n particles, select the particle corresponding to the smallest objective function, record the particle as the optimal learning particle, and replace the current particle with the optimal learning particle; In response to the random number being greater than or equal to the learning probability factor, replacing each data in the current particle with the best individual historical data of the data; The number of consecutive update failures is reset, and the updated current particle is put into the cyclic update again.

9. The method for global optimization of microgrid power quality according to claim 5, after comparing the objective function corresponding to the updated particle with the objective function corresponding to the global optimal particle, the method further comprises: In response to the objective function corresponding to the updated particle being greater than or equal to the objective function corresponding to the global optimal particle, the global optimal particle remains unchanged.

10. A microgrid power quality global optimization system, comprising: A node admittance matrix building module is configured to build a node admittance matrix of the microgrid according to an equivalent virtual impedance of an inverter of each node of the microgrid and a local load of each node; An objective function establishment module is configured to construct an objective function of the microgrid according to a weight value assigned to each node in the microgrid and a voltage distortion rate of each node; A constraint condition building module, configured to build a constraint condition for harmonic modulation of each node of the microgrid according to the node admittance matrix and the remaining capacity of the inverter of each node; The optimal solution finding module is configured to analyze the objective function corresponding to each particle in the particle set in response to satisfying the constraint condition, determine the optimal solution of the objective function, and adjust the equivalent virtual impedance of the inverter of each node according to the optimal solution, wherein a set of values ​​of the real part and the imaginary part of the equivalent virtual impedance at all nodes in the microgrid constitutes one particle.

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