Method, system and equipment for improving power quality of power distribution network and medium

Abstract

The invention relates to the technical field of power distribution network management, and discloses a power distribution network electric energy quality improvement method, system, equipment and medium, and the method comprises the steps: building a power distribution network optimization model according to the obtained power grid node data of a target power distribution network and the equipment electrical data of a multiphase energy storage mutual aid device; the power distribution network optimization model takes the minimum sum of the active loss and the internal loss of the whole network line as a target, and the constraint conditions comprise the operation constraint of the multiphase energy storage mutual aid device and the operation constraint of the power distribution network; and based on a state optimization algorithm and a differential evolution algorithm, constructing a state difference optimization algorithm, and performing optimization solution on the power distribution network optimization model by adopting the state difference optimization algorithm to obtain active power and reactive power of a back-to-back voltage source converter of the multiphase energy storage mutual aid device injected into a feeder line under each phase. And the energy storage unit output data of the energy storage unit under each phase. According to the method, the power distribution network loss can be effectively reduced, the node voltage level is improved, the voltage unbalance degree is suppressed, and the electric energy quality is improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network management technology, and in particular to a method, system, equipment and medium for improving the power quality of power distribution networks. Background Technology

[0002] With the rapid expansion of distributed renewable energy and electric vehicle charging facilities in the distribution network, the problem of severe voltage fluctuations and phase-to-phase load imbalance has intensified, leading to an increase in harmonic pollution. These problems are further manifested as voltage dips, voltage flicker, and three-phase voltage asymmetry during the operation of the distribution network. These issues not only affect the normal operation of user-end electrical equipment, but in severe cases, they can also shorten the service life of electrical equipment and cause malfunctions of protection devices, posing a significant threat to the safe and reliable operation of the distribution network.

[0003] To address the aforementioned power quality challenges, multiphase energy storage and mutual assistance devices have emerged as an effective solution. In these devices, back-to-back voltage source converters (VSCs) and corresponding energy storage units are connected in parallel to the same DC bus. The energy storage units absorb excess energy when power quality demand is low and rapidly release energy during peak demand or disturbances, thus smoothly suppressing power fluctuations in the distribution network and providing a hardware foundation for improving power quality. However, existing scheduling and control methods for multiphase energy storage and mutual assistance devices still have significant shortcomings: on the one hand, existing methods mostly focus on controlling single-phase energy storage units or scheduling the overall power of the device, lacking in-depth modeling of the three-phase component characteristics of the distribution network, making it difficult to accurately address key issues such as three-phase imbalance; on the other hand, existing control strategies lack effective constraints, limiting the overall operational efficiency of multiphase energy storage and mutual assistance devices and failing to fully realize their potential in power quality management.

[0004] Therefore, it is evident that improving the effectiveness of power quality management in distribution networks has become a pressing technical problem that needs to be addressed by those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, equipment, and medium for improving the power quality of a distribution network, in order to solve the technical problem of how to improve the power quality management effect of the distribution network, and to achieve comprehensive optimization of the three-phase component control of the multi-phase energy storage mutual assistance device, balance multi-dimensional losses and constraints, and improve the power quality management effect and stability of the distribution network.

[0006] In a first aspect, the present invention provides a method for improving the power quality of a distribution network, the method being applied to a distribution network including a multiphase energy storage and mutual assistance device, the method comprising: Acquire grid node data and equipment electrical data of the multiphase energy storage and mutual assistance device of the target distribution network; Based on the power grid node data and the equipment electrical data, a distribution network optimization model is constructed. The distribution network optimization model is set to minimize the sum of the active power loss of the entire network lines of the target distribution network and the internal loss of the multiphase energy storage mutual assistance device as the objective function. Based on the state optimization algorithm and the differential evolution algorithm, a state differential optimization algorithm is constructed, and the state differential optimization algorithm is used to optimize and solve the distribution network optimization model to obtain the active power and reactive power injected into the feeder of each back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase. Based on the active power, reactive power, and energy storage unit output data, the power supply of the target distribution network is improved and regulated.

[0007] Preferably, the acquisition of grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device includes: Obtain grid node data of the target distribution network, wherein the grid node data includes at least three-phase voltage data, three-phase current data, grid active power data, and grid reactive power data; Obtain the equipment electrical data of the multiphase energy storage mutual assistance device, the equipment electrical data including at least: the maximum apparent capacity of the back-to-back voltage source converter of the multiphase energy storage mutual assistance device, the upper and lower limits of the reactive power output of the back-to-back voltage source converter, and the upper and lower limits of the output of the energy storage unit of the multiphase energy storage mutual assistance device.

[0008] Preferably, the step of constructing a distribution network optimization model based on the power grid node data and the equipment electrical data includes: The first operating mode of the back-to-back voltage source converter and the second operating mode of the energy storage unit are determined. The first operating mode is set to adjust the power transmission of the main bus of the target distribution network to maintain the target distribution network in an active-reactive-DC voltage control mode. The second operating mode is set to dynamically adjust the active power output of the target distribution network according to the power flow gap of the target distribution network. Based on the first working mode and the second working mode, and according to the power balance relationship between the injected feeder power of the multiphase energy storage mutual aid device and the internal apparent capacity of the back-to-back voltage source converter, the operating constraints of the multiphase energy storage mutual aid device are constructed. The operating constraints of the multiphase energy storage mutual aid device include the apparent capacity constraint of the converter, the reactive power constraint of the converter, the output constraint of the energy storage unit, the active power injection constraint, and the internal loss constraint of the converter. Based on the principles of power flow conservation and three-phase voltage imbalance, power grid operation constraints are constructed. These constraints include at least: power flow balance constraints, node voltage magnitude constraints, and three-phase voltage imbalance constraints. Using the operational constraints of the multiphase energy storage mutual aid device and the power grid operational constraints as constraints, and minimizing the sum of the total line losses of the target distribution network and the internal losses of the multiphase energy storage mutual aid device as the objective function, a distribution network optimization model is constructed.

[0009] Preferably, the three-phase voltage imbalance constraint is set as the ratio between the deviation of the square of the three-phase voltage amplitude at the distribution network node and the mean of the square of the three-phase voltage amplitude at the distribution network node.

[0010] Preferably, the optimization of the distribution network optimization model using the state difference optimization algorithm yields the active and reactive power injected into the feeder by the back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase, including: A state optimization algorithm is used to dynamically adjust the power distribution of each phase of each multiphase energy storage mutual aid device in the power distribution network optimization model in the global scope. After reaching the preset iteration interval, an elite solution set is obtained. The elite solution set is locally fine-tuned using a differential evolution algorithm to obtain the active and reactive power of the back-to-back voltage source converter of each multiphase energy storage mutual aid device injected into the feeder in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

[0011] Preferably, the state optimization algorithm is used to dynamically adjust the power distribution of each phase of the multiphase energy storage mutual assistance device in the distribution network optimization model globally. After reaching a preset iteration interval, an elite solution set is obtained, including: A dual-population strategy is used for population initialization to obtain the initial population; The initial population is constrained by the operating constraints of the multiphase energy storage mutual aid device and the operating constraints of the power grid, and the first fitness value of each candidate solution in the initial population is obtained according to the objective function. Based on the first fitness value, the local elite individuals and global elite individuals of the family to which each candidate solution belongs are determined, and based on the local elite individuals and the global elite individuals, the candidate solutions are updated in position using an adaptive social influence weighting mechanism to generate new candidate solutions; Based on the three-phase voltage imbalance constraint, the voltage imbalance deviation of the new candidate solution is obtained, and a penalty term for the new candidate solution is generated based on the voltage imbalance deviation. The first fitness value is then optimized based on the penalty term to obtain a post-penalty fitness value. Based on the comparison result between the first fitness value and the post-penalty fitness value, the candidate solutions are iteratively updated, and after reaching a preset iteration interval, an elite solution set is obtained.

[0012] Preferably, the step of using a differential evolution algorithm to locally fine-tune the elite solution set to obtain the active and reactive power injected into the feeder by the back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, and the energy storage unit output data of each multiphase energy storage mutual aid device in each phase, including: The elite solution set is mutated and crossovered using a differential evolution algorithm to obtain an elite offspring solution set; Based on the objective function, the second fitness value of each elite sub-solution in the elite sub-solution set is obtained; The elite offspring solution set is updated according to the second fitness value until the preset convergence condition is met, and the globally optimal population is obtained. Based on the global optimal population, the active and reactive power of the back-to-back voltage source converter of each multiphase energy storage mutual aid device injected into the feeder in each phase are obtained, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

[0013] Secondly, the present invention also provides a power quality improvement system for a distribution network, which implements the power quality improvement method for the distribution network described above. The system is applied to a distribution network including a multiphase energy storage mutual assistance device. The system includes: a data acquisition module, a distribution network optimization model construction module, a model solving module, and a power improvement and regulation module. The data acquisition module is used to acquire grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device; The distribution network optimization model construction module is used to construct a distribution network optimization model based on the power grid node data and the equipment electrical data. The distribution network optimization model is set to minimize the sum of the total active power loss of the target distribution network and the internal loss of the multi-phase energy storage mutual assistance device as the objective function. The model solving module is used to construct a state differential optimization algorithm based on the state optimization algorithm and the differential evolution algorithm, and to use the state differential optimization algorithm to optimize and solve the distribution network optimization model, so as to obtain the active power and reactive power injected into the feeder of each back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, and the energy storage unit output data of each multiphase energy storage mutual aid device in each phase. The power improvement and regulation module is used to improve and regulate the power of the target distribution network based on the active power, the reactive power, and the output data of the energy storage unit.

[0014] Thirdly, the present invention also provides a computer device, the computer device including a memory, a processor and a transceiver, which are connected to each other via a bus; the memory is used to store a set of computer program instructions and data, and to transmit the stored data to the processor, the processor executing the computer program instructions stored in the memory to perform the above-described method for improving the power quality of the power distribution network.

[0015] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed, implements the above-described method for improving power quality in a power distribution network.

[0016] This application provides a method, system, device, and medium for improving power quality in a power distribution network. Compared with the prior art, the beneficial effects of the embodiments of this application are as follows: This application discloses a method for improving power quality in distribution networks. Based on multiphase energy storage mutual assistance devices, it conducts coordinated control of active and reactive power, taking into account key factors such as three-phase voltage imbalance, current asymmetry, and system operating losses. It analyzes the operating characteristics of multiphase energy storage mutual assistance devices in distribution networks, establishes a distribution network optimization model considering the power regulation capability of multiphase energy storage mutual assistance devices, energy storage charging and discharging losses, voltage deviation, and three-phase imbalance constraints, and proposes a hybrid optimization method combining state optimization algorithm and differential evolution algorithm. Through state evolution-driven global search and fusion of mutation crossover, it achieves local fine-tuning, which can effectively reduce distribution network losses, improve node voltage levels, and suppress voltage imbalance. It provides a theoretical basis and algorithmic support for the engineering deployment and control of multiphase energy storage mutual assistance devices in complex distribution networks, improves power quality, and enhances power supply reliability. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the steps of a method for improving power quality in a power distribution network according to a preferred embodiment of the present invention; Figure 2 This is a schematic diagram of the access location of an energy storage mutual aid device in a single-phase 33-node distribution network, provided by a preferred embodiment of the present invention; Figure 3 This is a schematic diagram of the voltage curves of the distribution network nodes when the multiphase energy storage mutual assistance device provided in a preferred embodiment of the present invention is connected to different locations; Figure 4 This is a schematic diagram of the access location of a multiphase energy storage and mutual assistance device in a three-phase 33-node distribution network provided by a preferred embodiment of the present invention; Figure 5 This is a voltage curve diagram of each node in the distribution network provided in a preferred embodiment of the present invention (Case 1). Figure 6 This is a voltage curve diagram of each node in a distribution network provided in a preferred embodiment of the present invention (Case 2). Figure 7 This is a voltage curve diagram of each node in a distribution network provided in a preferred embodiment of the present invention (Case 3). Figure 8 This is a schematic diagram of a power quality improvement system for a power distribution network provided in a preferred embodiment of the present invention; Figure 9 This is an internal structural diagram of the computer device in an embodiment of the present invention; Figure label: 34-Data acquisition module, 35-Distribution network optimization model construction module, 36-Model solving module, 37-Power improvement and regulation module. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and should not be construed as limiting the invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of protection of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of this invention. In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0019] In the description of this invention, it should be noted that, unless otherwise expressly specified and limited, the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] Please see Figure 1 The diagram illustrates the steps of a power quality improvement method for a distribution network. The method is applied to a distribution network including a multiphase energy storage and mutual assistance device. In an embodiment of the present invention, a power quality improvement method for a distribution network is provided, the method comprising: S1. Obtain grid node data and equipment electrical data of the multiphase energy storage and mutual assistance device of the target distribution network; In the preferred embodiment of this application, the operating characteristics of photovoltaic power generation system, wind power generation equipment and electrical load in the target distribution network are analyzed to determine the nodes on the main bus that face power quality problems such as voltage fluctuations and load imbalances, and multiphase energy storage and mutual assistance devices are arranged on the above nodes. The multiphase energy storage and mutual assistance device includes a back-to-back voltage source converter and an energy storage unit. The back-to-back voltage source converter and the energy storage unit of the same multiphase energy storage and mutual assistance device are connected in parallel on the same DC bus, so that the energy storage unit can absorb excess energy when the power quality demand is low and release energy quickly when the demand is high or when disturbances occur to smooth power fluctuations. The bidirectional active and reactive power flow between the energy storage unit and the back-to-back voltage source converter of each phase is realized through the DC bus, and each back-to-back voltage source converter of each phase is given independent power control capability. Furthermore, grid node data and equipment electrical data of the multiphase energy storage mutual aid device are collected from each node in the target distribution network. The grid node data includes three-phase voltage data, three-phase current data, grid active power data, and grid reactive power data. The equipment electrical data of the multiphase energy storage mutual aid device includes the maximum apparent capacity of the back-to-back voltage source converter of the multiphase energy storage mutual aid device, the upper and lower limits of reactive power output of the back-to-back voltage source converter, and the upper and lower limits of output of the energy storage unit of the multiphase energy storage mutual aid device.

[0022] S2. Based on the power grid node data and the equipment electrical data, a distribution network optimization model is constructed. The distribution network optimization model is set with the objective function being to minimize the sum of the total active power loss of the entire network lines and the internal losses of the multi-phase energy storage and mutual assistance device in the target distribution network. In a preferred embodiment of this application, the objective function is to minimize the sum of the active power loss of the target distribution network including the line active power loss and the internal losses of the multi-phase energy storage and mutual assistance device. The expression of the objective function is as follows: in, Let {a, b, c} represent the set of all nodes in the target distribution network, and let {a, b, c} represent the set of three phases. Represents a node Injection The active power of the phase, This refers to a collection of multiphase energy storage and mutual assistance devices connected to the target distribution network. This represents the set of lines connected to the multiphase energy storage and mutual assistance device in the target distribution network. Indicates the first A multiphase energy storage and mutual assistance device in the first The line Power loss inside a back-to-back voltage source converter.

[0023] In a multiphase energy storage and mutual assistance device, the first operating mode of the back-to-back voltage source converter is to regulate the power transmission of the main bus of the target distribution network to maintain the target distribution network in an active / reactive-DC voltage control mode, converting DC voltage to AC voltage. The second operating mode of the energy storage unit is to dynamically adjust the dynamic response of the active power of the target distribution network according to the power flow gap. Based on the first operating mode of the back-to-back voltage source converter and the corresponding second operating mode of the energy storage unit, and according to the power balance relationship between the injected feeder power of the multiphase energy storage and mutual assistance device and the internal apparent capacity of the back-to-back voltage source converter, the operating constraints of the multiphase energy storage and mutual assistance device are constructed. The operating constraints of the multiphase energy storage and mutual assistance device include converter apparent capacity constraints, converter reactive power constraints, energy storage unit output constraints, active power injection constraints, and converter internal loss constraints. Among them, the converter apparent capacity constraint is expressed as: in, Indicates the first A multiphase energy storage and mutual assistance device in the first The line Active power injected into the feeder by the back-to-back voltage source converter Indicates the first A multiphase energy storage and mutual assistance device in the first The line Reactive power injected into the feeder by the back-to-back voltage source converter Indicates the first A multiphase energy storage and mutual assistance device in the first The line Maximum apparent capacity of back-to-back voltage source converters.

[0024] The reactive power constraint of the converter is expressed as: in, Indicates the first A multiphase energy storage and mutual assistance device in the first The line Minimum reactive power injected into the feeder by a back-to-back voltage source converter Indicates the first A multiphase energy storage and mutual assistance device in the first The line The maximum reactive power injected into the feeder by the back-to-back voltage source converter.

[0025] The output constraint of the energy storage unit is expressed as: in, Indicates the first A multiphase energy storage and mutual assistance device in the first The line Phase energy storage unit output, Indicates the first A multiphase energy storage and mutual assistance device in the first The line Lower limit of phase energy storage unit output Indicates the first A multiphase energy storage and mutual assistance device in the first The line Upper limit of output of phase energy storage unit.

[0026] The active power injection constraint is expressed as follows: The internal loss constraints of the converter are expressed as follows: in, This indicates the percentage of apparent power loss per unit of power in a back-to-back voltage source converter.

[0027] Furthermore, based on the principles of power flow conservation and three-phase voltage imbalance, power grid operation constraints are constructed. These constraints include at least: power flow balance constraints, node voltage magnitude constraints, and three-phase voltage imbalance constraints. The power flow balance constraints are expressed as follows: in, Indicates distribution network node Flow to distribution network nodes The current, Indicates distribution network node With distribution network nodes Complex power between Indicates distribution network node With distribution network nodes Line impedance between Indicates distribution network node Injection power, Represents the set of branches in a power distribution network. Indicates distribution network node voltage, Indicates distribution network node Distributed energy power, Indicates distribution network node The power of the multiphase energy storage mutual assistance device Indicates distribution network node The load power, Indicates the reference node of the distribution network. This refers to a regular node in the distribution network, excluding the distribution network reference node.

[0028] The node voltage magnitude constraint is expressed as follows: in, Indicates distribution network node of Phase voltage, Indicates distribution network node of Upper limit of phase voltage Indicates distribution network node of The lower limit of phase voltage, this constraint applies to all nodes and lines except the distribution network reference node.

[0029] To ensure the computability and numerical stability of the target distribution network constraints and to facilitate the coupling of voltage imbalance and the objective function in the mathematical model, the voltage imbalance is defined as the deviation of the square of the phase voltage amplitude at the distribution network node divided by the average of the squares of the three-phase voltage amplitudes. The three-phase voltage imbalance constraint is expressed as follows: in, Indicates distribution network node exist Phase voltage imbalance Indicates distribution network node The average of the squares of the three-phase voltage amplitudes Indicates distribution network node exist Upper limit of voltage imbalance tolerance for phase.

[0030] S3. Based on the state optimization algorithm and the differential evolution algorithm, a state differential optimization algorithm is constructed, and the state differential optimization algorithm is used to optimize and solve the distribution network optimization model to obtain the active power and reactive power injected into the feeder of each phase of the back-to-back voltage source converter of each multiphase energy storage mutual aid device, as well as the energy storage unit output data of each phase of the energy storage mutual aid device. In the preferred embodiment of this application, based on the state optimization algorithm and the differential evolution algorithm, a state differential optimization algorithm (SBO-DE optimization algorithm) is constructed, and the state differential optimization algorithm is used to optimize and solve the distribution network optimization model. The state optimization algorithm (Social-Based Optimization, SBO) is a metaheuristic optimization method that draws on social behavior and influence mechanisms. Its core idea is to regard each solution (individual) as a social member and realize global search by simulating the "social influence" and "individual exploration" process within the group. In SBO, each iteration first dynamically calculates the "social influence weight" based on the individual's historical performance, so that the individual moves towards both the "local elite" and the "global elite" who perform best in the group, emphasizing the spread of accumulated experience. For individuals who fail to enter the elite ranks, random perturbation is introduced to increase search diversity and prevent them from getting trapped in local optima.

[0031] A state optimization algorithm is used to dynamically adjust the power distribution of each phase of each multi-phase energy storage mutual aid device in the distribution network optimization model globally, obtaining a candidate solution set. After a preset iteration interval, an elite solution set is obtained. A certain proportion of the best-performing elite solutions are then introduced into the Differential Evolution (DE) algorithm for fine-tuning to improve local search accuracy. The core idea of ​​the DE algorithm is to generate a mutation vector by using the differential information of individuals within the population, then generate candidate individuals by combining the crossover operation with the parent individuals, and finally retain the better individuals through a selection operation, iteratively approaching the optimal solution. Its optimization process does not rely on gradient information and is suitable for complex optimization problems such as nonlinearity, nonconvexity, and multi-peaks.

[0032] In a preferred embodiment of this application, when the state optimization algorithm reaches a preset iteration interval, a preset proportion of elite child solutions are selected from the elite solution set. For each elite solution in the elite child solution set, three different groups of individuals are randomly selected. By generating a mutation vector, and then recombining it positionally with the benchmark solution according to the crossover probability, candidate experimental solutions are obtained. Subsequently, the candidate experimental solutions are mapped to the feasible region that satisfies the operating constraints of the multiphase energy storage mutual aid device and the grid operating constraints, and their objective function values ​​are calculated. Based on the second fitness value, the elite child solutions in the elite child solution set are updated until the preset convergence condition is met, and the globally optimal population is obtained. This process is completed within the elite child solution set, ensuring the diversity of the global search and the precision of the local optimization, and continuously updating the globally optimal solution, ultimately obtaining a high-quality solution that takes into account the minimization of line loss, the minimization of the loss of the multiphase energy storage mutual aid device itself, and the optimization of power quality constraints.

[0033] Specifically, a dual-population strategy is adopted, generating two sets of independent, uniformly distributed individuals X within the upper and lower bounds of each dimension. (1) X (2) They are referred to as Family 1 and Family 2, respectively, to obtain the initial population. The dual-family strategy can effectively improve the diversity of the population. Each candidate solution in the initial population corresponds to the active power, reactive power, and energy storage unit output of the back-to-back voltage source converter injected into the feeder of the multiphase energy storage mutual aid device in each phase. The operating constraints of the multiphase energy storage mutual aid device and the grid operating constraints are used to constrain each candidate solution in the initial population. According to the objective function, the first fitness value of each candidate solution in the initial population is obtained. Based on the first fitness value, the local elite individuals and global elite individuals of each candidate set to their respective family are determined. Based on the local elite individuals and global elite individuals, an adaptive social influence weighting mechanism is used to update the position of the candidate solutions to enhance the convergence rate and generate new candidate solutions. The position update formula is as follows: in, Indicates the first The position of the new candidate solution corresponding to each candidate solution. Indicates the first The positions of the candidate solutions. , The weighting coefficients are random numbers within the range (0,1), used to determine the new candidate solutions. , , The weight, This indicates the switching threshold, which decreases with the number of evaluations. Indicates the magnification factor. for( Random numbers within the range are used to escape local optima. Indicates the location of a local elite individual. Indicates the position of the elite individual in the entire system. This represents a random number within the range (0,1).

[0034] For candidate solutions that fail to be selected as family elites, diversity is injected through time-based random perturbation to prevent them from falling into local optima too early.

[0035] After obtaining the new candidate solution, the voltage imbalance deviation of the new candidate solution is obtained according to the three-phase voltage imbalance constraint. Based on the voltage imbalance deviation, a penalty term for the new candidate solution is generated. The penalty term calculation formula is as follows: in, Indicates new candidate penalty items. , and Let represent the coefficient of the penalty function, and .

[0036] The corresponding penalty is added to the first fitness value to obtain the post-penalty fitness value. The original first fitness value and the post-penalty fitness value are compared. If the post-penalty fitness value is better than the original first fitness value, the original candidate solution is replaced with the new candidate solution. After reaching the preset iteration interval, the elite solution set is obtained.

[0037] The differential evolution algorithm is used to locally fine-tune the elite solution set to obtain the active and reactive power injected into the feeder of the back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

[0038] Elite solutions are extracted from the current elite solution set at a certain ratio. For each elite solution, a differential evolution algorithm is applied for mutation and crossover to obtain an elite offspring solution set. The mutation process is represented as follows: in, Indicates elite solution and elite solution The mutated solution vector, This represents the vector of the current globally optimal solution in the elite solution set. Indicates elite solution The vector, Indicates elite solution The vector, This represents the variation factor, used to control the magnitude of variation.

[0039] After constrained correction, the fitness is re-evaluated according to the objective function to obtain the second fitness value for each elite child solution in the elite child solution set. The elite child solution set is then updated based on the second fitness value. The above state optimization algorithm and differential evolution algorithm are performed alternately until the preset convergence condition is met, resulting in the globally optimal population.

[0040] Based on the global optimal population, the active and reactive power injected into the feeder by the back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase are obtained, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

[0041] S4. Based on the active power, reactive power, and energy storage unit output data, the power of the target distribution network is improved and regulated; in a preferred embodiment of this application, the multiphase energy storage mutual assistance device is controlled according to the active power, reactive power, and energy storage unit output data to improve and regulate the power of the target distribution network.

[0042] In a preferred embodiment of this application, an IEEE 33-node standard distribution network model is constructed using the MATLAB simulation platform to verify the power quality improvement capability of the proposed distribution network power quality improvement method at different access locations for power quality and distribution network losses. In the simulation, energy storage and mutual aid devices with a rated capacity of 500kVA are connected in the intervals from node 12 to node 22, from node 18 to node 33, and from node 9 to node 15, respectively. Figure 2 The diagram shows the connection locations of energy storage mutual aid devices in a single-phase 33-node distribution network. TS1, TS2, and TS3 are all multi-phase energy storage mutual aid devices. Simulation results show that without any devices connected, the distribution network loss is 204.00 kW. After the multi-phase energy storage mutual aid devices are connected to the intervals from node 12 to node 22, from node 18 to node 33, and from node 9 to node 15, the optimized network loss for the interval from node 12 to node 22 is 174.09 kW, the optimized network loss for the interval from node 18 to node 33 is 85.47 kW, and the optimized network loss for the interval from node 9 to node 15 is 107.6 kW, all significantly better than the baseline condition.

[0043] like Figure 3 The diagram shows the voltage curves of distribution network nodes when a multiphase energy storage mutual assistance device is connected at different locations. Figure 3 It can be seen that the connection of multiphase energy storage mutual assistance devices not only improves the voltage level of the overall distribution network, but also improves the voltage deviation of local nodes.

[0044] To verify the performance improvement of the SBO-DE optimization algorithm, which combines the state optimization algorithm and the differential evolution algorithm proposed in this application, in terms of strategy evolution, the following four optimization strategies were set, taking the multiphase energy storage mutual aid device connected to the interval from node 12 to node 22 as the object: Strategy A: Use the State Optimization Algorithm (SBO) for optimization; Strategy B: Based on the State Optimization Algorithm (SBO), an adaptive weight strategy is introduced for optimization; Strategy C: Based on the state optimization algorithm SBO, the Levy flight mutation strategy is introduced for optimization; Strategy D: The SBO-DE optimization algorithm, which combines the state optimization algorithm and the differential evolution algorithm disclosed in this embodiment, is used for optimization.

[0045] The total active power loss of the entire network and the internal loss of the multiphase energy storage mutual aid device obtained under the four strategies are 187.62kW, 176.86kW, 177.91kW and 174.09kW, respectively. From the above results, it can be seen that the SBO-DE optimization algorithm of this application has the best optimization effect.

[0046] To evaluate the role of energy storage units in multiphase energy storage and mutual assistance devices, this embodiment compares a multiphase energy storage and mutual assistance device containing energy storage units with a converter structure (without energy storage units, only possessing AC-DC-AC functionality). Under the same rated capacity, the same access location, and the same SBO-DE optimization algorithm, the total active power loss of the entire network and the internal losses of the multiphase energy storage and mutual assistance device after optimization are 193.11kW and 174.09kW, respectively. It is evident that the multiphase energy storage and mutual assistance device considering energy storage units significantly improves the suppression effect on network losses in the distribution network compared to the converter structure.

[0047] Furthermore, the above simulation is extended to a three-phase 33-node distribution network model, such as... Figure 4 The diagram shows the connection locations of a multiphase energy storage and mutual assistance device in a three-phase 33-node distribution network. 150kW single-phase distributed photovoltaic (PV) systems are connected at nodes 4, 19, and 26 respectively, while a multiphase energy storage and mutual assistance device is connected between nodes 18 and 33. PV(A), PV(B), and PV(C) represent single-phase distributed PV systems, and TS4 represents the multiphase energy storage and mutual assistance device. The following three simulation examples are used to simulate typical operating conditions of distributed power generation connected to the grid: Case 1: No multiphase energy storage and mutual assistance device is configured, and no converter is configured; Case 2: Only the converter device is used, without the configuration of a multiphase energy storage and mutual assistance device; Case 3: Configure a multiphase energy storage and mutual assistance device.

[0048] The total network loss of the distribution network in Case 1 is 228.47kW, in Case 2 it is 226.91kW, and in Case 3 it is 217.11kW. Figure 5 The figure shown is a voltage curve diagram of each node in the distribution network of Case 1. Figure 6 The figure shown is a voltage curve diagram of each node in the distribution network of Case 1. Figure 7 The figure shown is a voltage curve diagram of each node in the distribution network of Case 1.

[0049] The experimental results show that Case 3, with its multiphase energy storage and mutual assistance device, significantly outperforms the other two cases in terms of network loss optimization. Case 3 also demonstrates superior symmetrical regulation capabilities in voltage level and three-phase imbalance control. The simulation results above demonstrate that the proposed power quality improvement method for distribution networks has engineering applicability and comprehensive governance potential in complex distribution scenarios with multiple access sources.

[0050] In a preferred embodiment of the present invention, grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device are obtained; based on the grid node data and equipment electrical data, a distribution network optimization model is constructed, which is set to minimize the sum of the total active power loss of the entire network lines and the internal losses of the multiphase energy storage mutual assistance device; based on the state optimization algorithm and the differential evolution algorithm, a state differential optimization algorithm is constructed, and the state differential optimization algorithm is used to optimize and solve the distribution network optimization model to obtain the active power and reactive power injected into the feeder of the back-to-back voltage source converter of each multiphase energy storage mutual assistance device in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual assistance device in each phase; based on the active power, reactive power and energy storage unit output data, the power of the target distribution network is improved and regulated. This application discloses a method for improving power quality in distribution networks. Based on multiphase energy storage mutual assistance devices, it conducts coordinated control of active and reactive power, taking into account key factors such as three-phase voltage imbalance, current asymmetry, and system operating losses. It analyzes the operating characteristics of multiphase energy storage mutual assistance devices in distribution networks, establishes a distribution network optimization model considering the power regulation capability of multiphase energy storage mutual assistance devices, energy storage charging and discharging losses, voltage deviation, and three-phase imbalance constraints, and proposes a hybrid optimization method combining state optimization algorithm and differential evolution algorithm. Through state evolution-driven global search and fusion of mutation crossover, it achieves local fine-tuning, which can effectively reduce distribution network losses, improve node voltage levels, and suppress voltage imbalance. It provides a theoretical basis and algorithmic support for the engineering deployment and control of multiphase energy storage mutual assistance devices in complex distribution networks, improves power quality, and enhances power supply reliability.

[0051] Accordingly, such as Figure 8The diagram shows the structure of a power quality improvement system for a distribution network. Based on a power quality improvement method for a distribution network, this embodiment of the invention also provides a power quality improvement system for a distribution network, implementing the power quality improvement method for a distribution network disclosed in this embodiment of the invention. The system is applied to a distribution network including a multiphase energy storage and mutual assistance device. The system includes: a data acquisition module 34, a distribution network optimization model construction module 35, a model solving module 36, and a power improvement and regulation module 37. The data acquisition module 33 is used to acquire grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device; The distribution network optimization model construction module 34 is used to construct a distribution network optimization model based on the power grid node data and the equipment electrical data. The distribution network optimization model is set to minimize the sum of the active power loss of the entire network lines of the target distribution network and the internal loss of the multi-phase energy storage mutual assistance device. The model solving module 36 is used to construct a state differential optimization algorithm based on the state optimization algorithm and the differential evolution algorithm, and to use the state differential optimization algorithm to optimize and solve the distribution network optimization model, so as to obtain the active power and reactive power injected into the feeder of each back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, and the energy storage unit output data of each multiphase energy storage mutual aid device in each phase. The power improvement and regulation module 37 is used to improve and regulate the power of the target distribution network based on the active power, the reactive power and the output data of the energy storage unit.

[0052] For specific limitations regarding a power quality improvement system for a distribution network, please refer to the above-described limitations regarding a power quality improvement method for a distribution network, which will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this invention can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0053] like Figure 9 The diagram shows the internal structure of a computer device. An embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the embodiment of the power quality improvement method for power distribution networks, for example... Figure 1 Steps S1 to S4 as described above.

[0054] Those skilled in the art will understand that the illustrations Figure 9 This is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0055] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device via various interfaces and lines.

[0056] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0057] If the modules integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0059] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the steps described in the embodiments of the power quality improvement method for distribution networks as described above, for example... Figure 1 Steps S1 to S4 as described above.

[0060] In summary, the embodiments of this application provide a method, system, device, and medium for improving power quality in distribution networks, addressing the technical problem of how to improve the effectiveness of power quality management in distribution networks. The method includes: acquiring grid node data and equipment electrical data of multiphase energy storage and mutual assistance devices in the target distribution network; constructing a distribution network optimization model based on the grid node data and equipment electrical data, wherein the distribution network optimization model is set to minimize the sum of active power loss of the entire network lines and internal losses of the multiphase energy storage and mutual assistance devices in the target distribution network; constructing a state differential optimization algorithm based on state optimization algorithm and differential evolution algorithm, and using the state differential optimization algorithm to optimize and solve the distribution network optimization model, obtaining the active and reactive power injected into the feeder by the back-to-back voltage source converter of each multiphase energy storage and mutual assistance device in each phase, and the energy storage unit output data of each multiphase energy storage and mutual assistance device in each phase; and improving and regulating the power of the target distribution network based on the active power, reactive power, and energy storage unit output data. This application discloses a method for improving power quality in distribution networks. Based on multiphase energy storage mutual assistance devices, it conducts coordinated control of active and reactive power, taking into account key factors such as three-phase voltage imbalance, current asymmetry, and system operating losses. It analyzes the operating characteristics of multiphase energy storage mutual assistance devices in distribution networks, establishes a distribution network optimization model considering the power regulation capability of multiphase energy storage mutual assistance devices, energy storage charging and discharging losses, voltage deviation, and three-phase imbalance constraints, and proposes a hybrid optimization method combining state optimization algorithm and differential evolution algorithm. Through state evolution-driven global search and fusion of mutation crossover, it achieves local fine-tuning, which can effectively reduce distribution network losses, improve node voltage levels, and suppress voltage imbalance. It provides a theoretical basis and algorithmic support for the engineering deployment and control of multiphase energy storage mutual assistance devices in complex distribution networks, improves power quality, and enhances power supply reliability.

[0061] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0062] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for improving power quality in a distribution network, characterized in that, The method is applied to a distribution network including a multiphase energy storage and mutual assistance device, and the method includes: Acquire grid node data and equipment electrical data of the multiphase energy storage and mutual assistance device of the target distribution network; Based on the power grid node data and the equipment electrical data, a distribution network optimization model is constructed. The distribution network optimization model is set to minimize the sum of the active power loss of the entire network lines of the target distribution network and the internal loss of the multiphase energy storage mutual assistance device as the objective function. Based on the state optimization algorithm and the differential evolution algorithm, a state differential optimization algorithm is constructed, and the state differential optimization algorithm is used to optimize and solve the distribution network optimization model to obtain the active power and reactive power injected into the feeder of each back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase. Based on the active power, reactive power, and energy storage unit output data, the power supply of the target distribution network is improved and regulated.

2. The method for improving power quality in a distribution network as described in claim 1, characterized in that, The acquisition of grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device includes: Obtain grid node data of the target distribution network, wherein the grid node data includes at least three-phase voltage data, three-phase current data, grid active power data, and grid reactive power data; Obtain the equipment electrical data of the multiphase energy storage mutual assistance device, the equipment electrical data including at least: the maximum apparent capacity of the back-to-back voltage source converter of the multiphase energy storage mutual assistance device, the upper and lower limits of the reactive power output of the back-to-back voltage source converter, and the upper and lower limits of the output of the energy storage unit of the multiphase energy storage mutual assistance device.

3. The method for improving power quality in a distribution network as described in claim 2, characterized in that, The step of constructing a distribution network optimization model based on the power grid node data and the equipment electrical data includes: The first operating mode of the back-to-back voltage source converter and the second operating mode of the energy storage unit are determined. The first operating mode is set to adjust the power transmission of the main bus of the target distribution network to maintain the target distribution network in an active-reactive-DC voltage control mode. The second operating mode is set to dynamically adjust the active power output of the target distribution network according to the power flow gap of the target distribution network. Based on the first working mode and the second working mode, and according to the power balance relationship between the injected feeder power of the multiphase energy storage mutual aid device and the internal apparent capacity of the back-to-back voltage source converter, the operating constraints of the multiphase energy storage mutual aid device are constructed. The operating constraints of the multiphase energy storage mutual aid device include the apparent capacity constraint of the converter, the reactive power constraint of the converter, the output constraint of the energy storage unit, the active power injection constraint, and the internal loss constraint of the converter. Based on the principles of power flow conservation and three-phase voltage imbalance, power grid operation constraints are constructed. These constraints include at least: power flow balance constraints, node voltage magnitude constraints, and three-phase voltage imbalance constraints. Using the operational constraints of the multiphase energy storage mutual aid device and the power grid operational constraints as constraints, and minimizing the sum of the total line losses of the target distribution network and the internal losses of the multiphase energy storage mutual aid device as the objective function, a distribution network optimization model is constructed.

4. The method for improving power quality in a distribution network as described in claim 3, characterized in that, The three-phase voltage imbalance constraint is set as the ratio between the deviation of the square of the three-phase voltage amplitude at the distribution network node and the mean of the square of the three-phase voltage amplitude at the distribution network node.

5. The method for improving power quality in a distribution network as described in claim 3, characterized in that, The state difference optimization algorithm is used to optimize and solve the distribution network optimization model, obtaining the active and reactive power injected into the feeder by the back-to-back voltage source converter of each multiphase energy storage mutual assistance device in each phase, and the energy storage unit output data of each multiphase energy storage mutual assistance device in each phase, including: A state optimization algorithm is used to dynamically adjust the power distribution of each phase of each multiphase energy storage mutual aid device in the power distribution network optimization model in the global scope. After reaching the preset iteration interval, an elite solution set is obtained. The elite solution set is locally fine-tuned using a differential evolution algorithm to obtain the active and reactive power of the back-to-back voltage source converter of each multiphase energy storage mutual aid device injected into the feeder in each phase, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

6. The method for improving power quality in a distribution network as described in claim 5, characterized in that, The state optimization algorithm is used to dynamically adjust the power distribution of each phase of the multiphase energy storage mutual assistance device in the distribution network optimization model globally. After reaching a preset iteration interval, an elite solution set is obtained, including: A dual-population strategy is used for population initialization to obtain the initial population; The initial population is constrained by the operating constraints of the multiphase energy storage mutual aid device and the operating constraints of the power grid, and the first fitness value of each candidate solution in the initial population is obtained according to the objective function. Based on the first fitness value, the local elite individuals and global elite individuals of the family to which each candidate solution belongs are determined, and based on the local elite individuals and the global elite individuals, the candidate solutions are updated in position using an adaptive social influence weighting mechanism to generate new candidate solutions; Based on the three-phase voltage imbalance constraint, the voltage imbalance deviation of the new candidate solution is obtained, and a penalty term for the new candidate solution is generated based on the voltage imbalance deviation. The first fitness value is then optimized based on the penalty term to obtain a post-penalty fitness value. Based on the comparison result between the first fitness value and the post-penalty fitness value, the candidate solutions are iteratively updated, and after reaching a preset iteration interval, an elite solution set is obtained.

7. The method for improving power quality in a distribution network as described in claim 5, characterized in that, The differential evolution algorithm is used to locally fine-tune the elite solution set to obtain the active and reactive power injected into the feeder of each back-to-back voltage source converter of the multiphase energy storage mutual assistance device in each phase, as well as the energy storage unit output data of each energy storage mutual assistance device in each phase, including: The elite solution set is mutated and crossovered using a differential evolution algorithm to obtain an elite offspring solution set; Based on the objective function, the second fitness value of each elite sub-solution in the elite sub-solution set is obtained; The elite offspring solution set is updated according to the second fitness value until the preset convergence condition is met, and the globally optimal population is obtained. Based on the global optimal population, the active and reactive power of the back-to-back voltage source converter of each multiphase energy storage mutual aid device injected into the feeder in each phase are obtained, as well as the energy storage unit output data of each multiphase energy storage mutual aid device in each phase.

8. A power quality improvement system for a distribution network, used to implement the power quality improvement method for a distribution network as described in any one of claims 1-7, characterized in that, The system is applied to a distribution network including a multiphase energy storage and mutual assistance device. The system includes: a data acquisition module, a distribution network optimization model construction module, a model solving module, and a power improvement and regulation module. The data acquisition module is used to acquire grid node data of the target distribution network and equipment electrical data of the multiphase energy storage mutual assistance device; The distribution network optimization model construction module is used to construct a distribution network optimization model based on the power grid node data and the equipment electrical data. The distribution network optimization model is set to minimize the sum of the total active power loss of the target distribution network and the internal loss of the multi-phase energy storage mutual assistance device as the objective function. The model solving module is used to construct a state differential optimization algorithm based on the state optimization algorithm and the differential evolution algorithm, and to use the state differential optimization algorithm to optimize and solve the distribution network optimization model, so as to obtain the active power and reactive power injected into the feeder of each back-to-back voltage source converter of each multiphase energy storage mutual aid device in each phase, and the energy storage unit output data of each multiphase energy storage mutual aid device in each phase. The power improvement and regulation module is used to improve and regulate the power of the target distribution network based on the active power, the reactive power, and the output data of the energy storage unit.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and a transceiver, which are connected to each other via a bus; the memory is used to store a set of computer program instructions and data, and to transmit the stored data to the processor, the processor executing the computer program instructions stored in the memory to perform the power quality improvement method for the power distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed, implements the power quality improvement method for the power distribution network as described in any one of claims 1 to 7.

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