Power distribution network voltage active control method and device based on energy storage converter regulation

Abstract

The invention discloses a power distribution network voltage active control method and device based on energy storage converter regulation, and the method comprises the steps: building a multi-objective optimization function with system line loss, converter regulation power quantity and voltage fluctuation risk indexes as optimization objectives, and calculating energy storage converter regulation reactive power quantity according to an action voltage threshold; solving the multi-objective optimization function by taking the action voltage threshold as a decision variable to obtain an optimal action voltage threshold of the energy storage converter; and monitoring the actual voltage of the target grid-connected node in real time, comparing the actual voltage with the optimal action voltage threshold corresponding to the target energy storage converter, triggering the target energy storage converter to carry out reactive power regulation according to the comparison result, and calculating the reactive power regulation quantity required by the target energy storage converter according to the deviation value and the reactive power-voltage sensitivity matrix. The grid-connected point voltage of the power distribution network can be adaptively optimized and adjusted, and the utilization rate, the scheduling performance and the flexibility of scheduling resources are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a method and device for active voltage control of power distribution networks based on energy storage converter regulation. Background Technology

[0002] Currently, a large proportion of new energy sources such as wind power and photovoltaics are being integrated into the distribution network. However, due to the randomness and uncertainty of wind and solar power output, the voltage of the distribution network fluctuates frequently, and in severe cases, voltage exceedances occur, affecting power supply security and power quality. The voltage stability of the distribution network faces a serious challenge. To achieve voltage regulation and control in the distribution network, existing technologies typically employ discrete compensation devices or static var compensators (SVCs). Traditional discrete compensation devices, such as on-load tap changers (OLTCs) or shunt reactors (CBs), can only achieve coarse voltage adjustment with slow response speed and limited regulation capacity, failing to meet the real-time regulation requirements of the power system. While SVCs can achieve continuous regulation, their thyristor-controlled reactors generate harmonic currents, causing power quality problems. Therefore, additional filtering circuits are required, increasing the complexity and footprint of the device and significantly increasing implementation costs, resulting in poor economic efficiency in practical applications. Energy storage converters (PCS) can rapidly generate or absorb reactive power without increasing additional hardware costs. Existing technologies either treat PCS simply as energy transfer devices, only scheduling active power for peak shaving and valley filling, neglecting its reactive power regulation characteristics and wasting regulation resources, or they simply use fixed thresholds for regulation. This means that when the monitored voltage exceeds a preset fixed voltage threshold, the PCS is activated to output reactive power. Fixed thresholds offer poor flexibility and cannot achieve adaptive preventative regulation control. Furthermore, since the distribution network structure parameters and real-time operating states of different distribution network systems often differ, the corresponding voltage regulation thresholds also vary. Using fixed voltage thresholds cannot adaptively match different distribution network systems, resulting in poor actual control and regulation effects. Summary of the Invention

[0003] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems existing in the prior art, the present invention provides a method and device for active control of distribution network voltage based on energy storage converter regulation, which can make full use of the reactive power regulation characteristics of energy storage converter, adaptively optimize the regulation of distribution network connection point voltage, and improve the utilization rate of dispatching resources, dispatching performance and flexibility.

[0004] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for active voltage control in a distribution network based on energy storage converter regulation, comprising the following steps: A multi-objective optimization function is constructed with the optimization objectives of minimizing system line loss, minimizing reactive power regulation by energy storage converter, and minimizing voltage fluctuation risk index. The voltage fluctuation risk index is calculated based on the photovoltaic output at two consecutive moments. The operating voltage threshold at the current moment is calculated based on the operating voltage threshold at the previous moment and the voltage fluctuation risk index. The reactive power regulation by energy storage converter is calculated based on the operating voltage threshold at the current moment. The system obtains the line losses of each branch in the controlled distribution network, the regulated reactive power of the energy storage converter connected to the grid node, and the photovoltaic output at different times under different photovoltaic and load output scenarios. The system solves the constructed multi-objective optimization function with the operating voltage threshold of the energy storage converter as the decision variable to obtain the optimal operating voltage threshold of the energy storage converter. Real-time monitoring of the actual voltage of the target grid-connected node, and comparison with the optimal operating voltage threshold corresponding to the target energy storage converter connected to the target grid-connected node. Based on the comparison result, the target energy storage converter is triggered to perform reactive power regulation in order to adaptively adjust the voltage of the energy storage grid-connected node. When the target energy storage converter is triggered to perform no regulation, the deviation between the actual voltage of the target grid-connected node monitored in real time and the optimal action voltage threshold is calculated. Based on the deviation and the reactive power-voltage sensitivity matrix, the reactive power regulation required by the target energy storage converter is calculated.

[0005] Furthermore, the constructed multi-objective optimization function is as follows:

[0006] The constraints include:

[0007] in, To optimize system line loss, The goal is to optimize the regulation of reactive power in energy storage converters. Optimize the target for voltage fluctuation risk indicators. , , These are the weighting coefficients. , They are respectively with the first The upper and lower voltage thresholds are among the operating voltage thresholds of the energy storage converters connected to each grid-connected node. , , These are the upper and lower limits of the operating voltage threshold and the rated value; the optimization target for the reactive power regulation of the energy storage converter. The calculation expression is:

[0008] in, For the number of scenes, The total number of moments. This represents the number of grid-connected nodes within the distribution network. For the scene The probability of occurrence, For the scene Next access The energy storage converter of each grid-connected node is in t The amount of reactive power regulated at any given time, including the amount of reactive power generated or absorbed, is discussed in the context of... t The formula for calculating the regulated reactive power of the energy storage converter at time t is:

[0009] in, for The deviation between the actual voltage of the grid-connected node and the operating voltage threshold at any given time. The actual voltage of the grid-connected node. , These are the upper and lower voltage thresholds for the action voltage threshold. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This is the upper limit of the reactive power capacity of the energy storage converter. This represents the reactive power-voltage sensitivity matrix.

[0010] Furthermore, the optimization target of voltage fluctuation risk indicators The calculation expression is:

[0011] in, , The upper and lower voltage thresholds are the operating voltage thresholds for the energy storage converter at the next moment. , These are the upper and lower voltage thresholds of the operating voltage threshold for the energy storage converter at the previous moment. for t Real-time photovoltaic output for t- Photovoltaic power output at 1 moment For photovoltaic rated capacity, This is a voltage risk fluctuation risk indicator. , This is a preset ratio value. > , These are standardized constants; System line loss optimization target The calculation expression is:

[0012] in, For the number of distribution network branches, In the scene Down t Time of the first l Line loss on one branch.

[0013] Furthermore, when constructing the multi-objective optimization function, the constraints also include any one or more of the following: power flow constraints, branch current constraints, converter reactive power regulation capacity constraints, power factor constraints, and operating frequency constraints. The converter reactive power regulation capacity constraint ensures that the reactive power regulation of the energy storage converter is within a preset range. The branch current constraint ensures that the current in each branch at each time point is lower than a preset upper current limit. The power factor constraint ensures that the power factor of the energy storage converter is greater than a preset minimum power factor. The power flow constraints are:

[0014] in, , Injection nodes Active and reactive power, , and These are the grid-connected nodes. With grid connection nodes The branch conductance, susceptance, and phase angle difference between them Number of distribution network branches; , These are the grid-connected nodes. With grid connection nodes The voltage; The action frequency constraint is:

[0015] in, , These are the weighting coefficients. The penalty coefficient is... The number of adjustments made by the energy storage converter. T Indicates the statistical time period. f The regulation frequency for the energy storage converter. This is the maximum regulation frequency of the energy storage converter.

[0016] Furthermore, the optimal operating voltage threshold includes an upper voltage threshold and a lower voltage threshold, and the step of controlling the energy storage converter at the controlled grid-connected node to perform reactive power regulation based on the comparison result includes: When the actual voltage at the target grid connection point is greater than the upper limit of the optimal action voltage threshold, the target energy storage converter is triggered to absorb reactive power to suppress overvoltage. When the actual voltage at the target grid connection point is less than the lower limit voltage threshold of the optimal action voltage threshold, the target converter is triggered to output reactive power for voltage compensation. When the actual voltage at the target grid connection point is within the range between the upper and lower voltage thresholds of the optimal operating voltage threshold, the target energy storage converter will not perform reactive power regulation.

[0017] Furthermore, the calculation expression for the required reactive power adjustment based on the deviation value is as follows:

[0018] in, for The deviation value at time [time], The actual voltage at the energy storage grid connection point. , These are the upper and lower voltage thresholds for the action voltage threshold, respectively. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This represents the upper limit of the reactive power capacity of the energy storage converter at the moment of operation. This represents the reactive power-voltage sensitivity matrix.

[0019] Furthermore, an improved Grey Wolf algorithm is used to solve the constructed multi-objective optimization function. In this improved Grey Wolf algorithm, a back-learning method is employed to determine the position of the Grey Wolf back-initialization.

[0020] In the formula: , These represent the positions before and after the gray wolf's reverse initialization. , These represent the upper and lower boundaries of the search space, respectively. The following convergence factor is used during the iteration process:

[0021]

[0022] in, Let be the convergence factor for the t-th iteration. , These are the upper and lower limits of the convergence factor, respectively. t , T These are the current iteration count and the maximum iteration count, respectively. This is the decay function.

[0023] Furthermore, in the improved gray wolf algorithm, the foraging mechanism based on the parrot algorithm is implemented in the later stages of iteration. Determine the individual's position after each iteration during the iterative process. , , These represent the individual's position after iteration, the individual's position before iteration, and the position of the globally optimal individual, respectively. The folding factor, , All are random numbers between [0, 1]; In the later stages of iteration, the Lévy flight strategy is used to generate variable-asynchronous lengths for individual positions to help escape local optima. , in, For variable asynchronous length, Indicates the direction of movement.

[0024] An electronic device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0025] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This application fully considers the reactive power regulation characteristics of the energy storage converter. By monitoring the actual voltage of the target grid-connected node in real time, the energy storage converter is triggered to perform reactive power regulation based on the actual voltage status of the target grid-connected node. The regulation based on the energy storage converter can quickly respond to achieve active distribution network voltage regulation, which can improve the utilization rate and efficiency of dispatching resources, and no additional hardware equipment is required.

[0027] 2. This application addresses the variable distribution network scenarios involving both power generation and load, as well as wind and solar power. It uses minimizing system line losses, energy storage converter regulation power, and voltage fluctuation as multi-objective optimization functions, and the energy storage converter's operating voltage threshold as the decision variable. Based on the actual structural parameters and operating status of the controlled distribution network, it adaptively solves for the optimal operating voltage threshold of the energy storage converter connected to the grid-connected node. Then, when regulating the grid-connected node, it adjusts reactive power based on the actual voltage of the node and the adaptively determined optimal operating voltage threshold to compensate for the system's reactive power deficit. This allows for adaptive matching of different distribution network systems under various wind and solar power scenarios, quickly achieving optimal scheduling of the distribution network's grid-connected node voltage, effectively improving scheduling response efficiency and flexibility, thereby enhancing optimization performance. Attached Figure Description

[0028] Figure 1 This is a schematic diagram illustrating the implementation process of the active voltage control method for distribution networks based on energy storage converter regulation in this embodiment.

[0029] Figure 2 This is a schematic diagram illustrating the implementation process of using the improved Grey Wolf algorithm to solve for the optimal action voltage threshold in this embodiment.

[0030] Figure 3 This is a schematic diagram illustrating the specific process of active distribution network voltage control and regulation based on reactive power regulation using an energy storage converter in this embodiment.

[0031] Figure 4 This is a schematic diagram of the structure of the improved IEEE 33-node power distribution system used in a specific application embodiment.

[0032] Figure 5 This is a schematic diagram of source and load data used in a specific application embodiment.

[0033] Figure 6 This is a schematic diagram showing the comparison of compensation effects under different voltage deviation scenarios in specific application embodiments.

[0034] Figure 7 This is a schematic diagram of the fitness convergence curves and optimization results of different algorithms in specific application embodiments. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0036] Assume the system has a total There are 1 node, with node 0 being the power node. The voltage at the grid-connected node of a distributed photovoltaic system, after being regulated by an energy storage converter, is as follows: (1) The grid-connected node to which the energy storage converter is connected Located at the photovoltaic grid connection point Previously, that is When the grid connection node voltage is:

[0037] (2) The grid-connected node to which the energy storage converter is connected Located at the photovoltaic grid connection point After that, When the network node voltage is:

[0038] in, The voltage at the grid connection point after compensation, The source-side voltage amplitude, , Connected to The equivalent load of a node is the original load minus the photovoltaic power. , These are the branch resistance and reactance, respectively. , These are the active and reactive power outputs of the energy storage converter, respectively. For grid connection point The voltage at that point.

[0039] Grid connection nodes before and after adjustment by energy storage converter The voltage change is:

[0040] in, For grid connection nodes The amount of voltage change. These are the branch resistance and reactance, respectively. , These represent the active and reactive power outputs of the energy storage converter, respectively. For grid connection nodes The voltage at that point.

[0041] As shown in the above formula, the energy storage converter has reactive power regulation characteristics, when At that time, the energy storage converter absorbs excess reactive power. When the voltage at the grid-connected node drops, the energy storage converter can raise the system voltage by supplying reactive power to the grid-connected node.

[0042] This application fully considers the reactive power regulation characteristics of the energy storage converter, and by monitoring the actual voltage of the target grid-connected node in real time, it triggers the energy storage converter to perform reactive power regulation based on the actual voltage status of the target grid-connected node. The regulation based on the energy storage converter can quickly respond to achieve active distribution network voltage regulation, which can improve the utilization rate and efficiency of dispatching resources, and no additional hardware equipment is required. Meanwhile, considering that the fixed threshold method has problems such as poor flexibility, inability to achieve adaptive preventive regulation and control, and inability to adapt to different distribution network systems, this application is aimed at the distribution network with variable source-load and wind-solar scenarios. It takes the minimization of system line loss, energy storage converter regulation (generation / absorption) power, and voltage fluctuation index as the multi-objective optimization function, and uses the energy storage converter action voltage threshold as the decision variable. Based on the actual structural parameters and operating status of the controlled distribution network, the optimal action voltage threshold of the energy storage converter connected to the grid node is adaptively solved. Then, when regulating the grid node, reactive power is regulated according to the actual voltage of the grid node and the adaptively determined optimal action voltage threshold to compensate for the reactive power difference of the system. It can adaptively match different distribution network systems in various wind and solar scenarios, quickly achieve optimal scheduling of the voltage of the distribution network grid node, effectively improve the response efficiency and flexibility of scheduling, and has better optimization results and economy than the traditional ESS system that only performs active power compensation and quota compensation.

[0043] like Figure 1 As shown, the steps of the active voltage control method for distribution networks based on energy storage converter regulation in this embodiment include: Step S01: Construct a multi-objective optimization function with the optimization objectives of minimizing system line loss, minimizing reactive power regulation by the energy storage converter, and minimizing voltage fluctuation risk index. The voltage fluctuation risk index is calculated based on the photovoltaic output at two consecutive moments. The action voltage threshold at the current moment is calculated based on the action voltage threshold at the previous moment and the voltage fluctuation risk index. The reactive power regulation by the energy storage converter is calculated based on the action voltage threshold at the current moment. Step S02: Obtain the line losses of each branch in the controlled distribution network, the regulated reactive power of the energy storage converter connected to each grid node, and the photovoltaic output at different times under different photovoltaic and load output scenarios. Solve the constructed multi-objective optimization function with the operating voltage threshold of the energy storage converter as the decision variable to obtain the optimal operating voltage threshold of the energy storage converter. Step S03: Monitor the actual voltage of the target grid-connected node in real time and compare it with the optimal operating voltage threshold corresponding to the target energy storage converter connected to the target grid-connected node. Based on the comparison result, control the target energy storage converter to perform reactive power regulation in order to adaptively adjust the voltage of the energy storage grid-connected node. Step S04: When the target energy storage converter is triggered to perform reactive power regulation, calculate the deviation between the actual voltage of the target grid-connected node monitored in real time and the optimal operating voltage threshold, and calculate the reactive power regulation amount required by the target energy storage converter based on the deviation value and the reactive power-voltage sensitivity matrix.

[0044] In this embodiment, the constructed multi-objective optimization function is: (1) The constraints include: (2) in, To optimize system line loss, The goal is to optimize the regulation of reactive power in energy storage converters. Optimize the target for voltage fluctuation risk indicators. , , These are the weighting coefficients. , They are respectively with the first The upper and lower voltage thresholds are among the operating voltage thresholds of the energy storage converters connected to each grid-connected node. , , These are the upper and lower limits of the operating voltage threshold and the rated value, respectively.

[0045] As an optional implementation method, the system line loss optimization objective The calculation expression is:

[0046] in, For the number of scenes, For the number of distribution network branches, For the total number of moments (e.g., if t is in hours, then...) Take 24). In the scene Down t Time of the first l Line loss on a branch line, scenario Specifically, this refers to the reduced photovoltaic and load output scenarios, which means reducing the wind and solar load scenarios within a specified time period and replacing them with a typical scenario.

[0047] As an optional implementation method, the energy storage converter optimizes the reactive power output. The calculation expression is: (3) in, For the number of scenes, The total number of moments. This represents the number of grid-connected nodes within the distribution network. For the scene The probability of occurrence, For the scene Next access The energy storage converter of each grid-connected node is in t The regulation of reactive power at any given time includes the amount of reactive power generated or absorbed. t The expression for calculating the regulated reactive power of the energy storage converter at the target grid-connected node under a specified scenario at a given time is: (4) in, for The deviation between the actual voltage of the grid-connected node and the operating voltage threshold at any given time. The actual voltage of the grid-connected node. , These are the upper and lower voltage thresholds for the action voltage threshold. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This is the upper limit of the reactive power capacity of the energy storage converter. This represents the reactive power-voltage sensitivity matrix.

[0048] As shown in equation (4), when the voltage of the grid-connected node to which the energy storage converter is connected... When, the energy storage converter is triggered to absorb reactive power to suppress overvoltage; when When the system voltage is high, the energy storage converter is triggered to generate reactive power to compensate for the system voltage; when At this time, the voltage is within the safe range, and the energy storage converter does not operate. Therefore, based on the operating voltage threshold... , It can calculate the amount of reactive power that the energy storage converter at the grid-connected node can regulate under different scenarios.

[0049] As an optional implementation method, the voltage fluctuation risk index optimization target The calculation expression is: (5) in, , The upper and lower voltage thresholds are the operating voltage thresholds for the energy storage converter at the next moment. , These are the upper and lower voltage thresholds of the operating voltage threshold for the energy storage converter at the previous moment. for t Real-time photovoltaic output for t- Photovoltaic power output at 1 moment For photovoltaic rated capacity, This is a voltage risk fluctuation risk indicator. , For a preset ratio value (e.g.) 5% is acceptable. (10% is acceptable) > , is a standardized constant.

[0050] As shown in equation (5) above, voltage risk fluctuation risk index The larger the value, the more severe the fluctuation in photovoltaic output and the higher the risk of voltage fluctuation. If the voltage changes negatively (below the lower voltage threshold), the risk of voltage fluctuation is even higher, and the threshold needs to be reduced accordingly to reduce the risk of voltage fluctuation while ensuring adjustment accuracy. The maximum adjustment of the action voltage threshold is set to no more than 2 (i.e., a maximum voltage offset of ±0.02 pu). Therefore, according to... Adjust the threshold; however, if the voltage is changing positively (greater than the upper voltage threshold), and the risk of voltage fluctuation is higher, the threshold needs to be increased accordingly to reduce the risk of voltage fluctuation, i.e., according to... Adjusting the threshold. Using the methods described above, the operating voltage threshold of the energy storage converter can be dynamically adjusted based on the voltage fluctuation risk, thereby minimizing the voltage fluctuation risk.

[0051] To ensure the validity of the calculated operating voltage threshold, this embodiment incorporates power flow constraints, branch current constraints, converter reactive power regulation capacity constraints, power factor constraints, and operating frequency constraints when constructing the multi-objective optimization function. The power flow constraints can be expressed as follows: (6) in, , Injection nodes Active and reactive power, , and These are the grid-connected nodes. With grid connection nodes The branch conductance, susceptance, and phase angle difference between them For the number of distribution network branches, , These are the grid-connected nodes. With grid connection nodes The voltage.

[0052] Branch current constraint means that the current in each branch at any given time is lower than a preset current upper limit value, which can be expressed as follows: (7) in, For the scene Down Time Branch The current, This is the upper limit of the branch current.

[0053] The reactive power regulation capacity constraint of the converter is that the reactive power regulation of the energy storage converter is within a preset range, which can be expressed as follows: (8) in, For the scene Down Time and grid connection nodes The reactive power output of the connected energy storage converter , Scenes Down Grid connection node at all times The upper and lower limits of the reactive power output of the connected energy storage converter.

[0054] The power factor constraint ensures that the power factor of the energy storage converter is greater than a preset minimum power factor. This controls the power factor of the energy storage converter, preventing unnecessary operation that could affect equipment lifespan and thus reducing line losses. Specifically, the power factor constraint can be expressed as: (9) in, , These represent the active and reactive power outputs of the energy storage converter, respectively. The minimum allowable power factor can be, for example, 0.95.

[0055] Action frequency constraints can be expressed as: (10) in, , These are the weighting coefficients. The penalty coefficient is... The number of adjustments made by the energy storage converter. T Indicates the statistical time period. f The regulation frequency for the energy storage converter. This is the maximum regulation frequency of the energy storage converter. f Exceeding the maximum allowed value When the frequency is 10 times / hour, the penalty takes effect, causing threshold optimization to reduce the number of actions. The maximum adjustment frequency mentioned above... It can be determined based on the lifespan model of the energy storage converter.

[0056] This embodiment, by setting the above-mentioned operating frequency constraint, can prevent the converter from frequently operating, which accelerates the aging of the IGBT module and shortens the equipment life. By limiting the number of adjustments per unit of practice during the threshold determination process, it can balance voltage quality and equipment life.

[0057] By using the operating voltage threshold as the decision variable and solving the multi-objective optimization function constructed above, the optimal operating voltage threshold of the energy storage converter at the grid-connected node can be adaptively determined to suit different distribution network systems and different operating conditions.

[0058] Because distribution networks contain a large number of structural variables and nonlinear constraints, their optimization problem is a typical mixed-integer programming problem. Existing technologies mainly obtain feasible solutions through numerical algorithms or intelligent algorithms. However, numerical algorithms become more difficult to solve as the model dimension increases. Intelligent algorithms are widely used to deal with nonlinear programming problems due to their good robustness and scalability. However, traditional intelligent algorithms (such as particle swarm optimization and simulated annealing) are prone to getting trapped in local optima during the model solving process, and the optimized scheduling scheme is not optimal.

[0059] The Multi-Objective Grey Wolf Algorithm (MOGWO) simulates the hunting strategy of wolves in nature, led by an alpha wolf, to search for and prey on their prey. Compared to traditional intelligent algorithms, the MOGWO algorithm has stronger robustness and global search capabilities. The solution process of the MOGWO algorithm is as follows: (1) Searching for surrounding prey: In the early stages of the algorithm, by controlling parameters The larger value ( This guides the wolf pack to conduct a wide-ranging random search, enabling extensive exploration of the solution space. Simultaneously, all individuals, based on the current three best individuals (…),… , , The model moves closer to the location of the object through position updates, simulating encirclement behavior. As iterations proceed, the convergence factor... Decreasing linearly from 2 to 0, resulting in The value gradually decreases. When At that time, the search mode shifted from exploration to development, and individuals in , , A detailed search is conducted in the surrounding area, and the encirclement is gradually narrowed to approach the global optimal solution.

[0060] (11) in, , These are the individual wolf pack positions before and after the update. For the location of the prey, Let be the distance vector between the prey and the wolf pack. , These are the convergence coefficient and the oscillation coefficient, respectively. , All are random numbers between [0, 1]; is the convergence factor.

[0061] (2) Hunting prey: Based on the area formed by the positions of the three optimal gray wolves, gradually surround and hunt the prey: (12) However, the traditional MOGWO algorithm has low population diversity and is prone to getting trapped in local optima. When directly applying the traditional MOGWO algorithm to solve the multi-objective optimization function mentioned above in this embodiment, since the multi-objective optimization function is a complex nonlinear programming model, there may be a situation where the optimal scheduling strategy cannot be obtained. In order to address this problem, this embodiment further optimizes the traditional MOGWO algorithm and uses it to solve the model.

[0062] In this embodiment, the improved Grey Wolf Algorithm (IGWO) is used to solve the constructed multi-objective optimization function, and back-learning is used to initialize the population to improve the quality of the optimal solution; an improved convergence factor is introduced. While balancing and enhancing the algorithm's exploration and development capabilities, it accelerates the convergence speed; it combines the somersault mechanism of the parrot algorithm to improve population diversity; and it adds the Levy flight strategy to help the algorithm escape local optima.

[0063] Specifically, considering that the GWO algorithm uses a strategy of random initialization to generate initial solutions, the global distribution uniformity of the initial solutions is poor, resulting in poor quality of the optimal solution. In this embodiment, the improved Grey Wolf algorithm uses a reverse learning method to determine the position of the Grey Wolf reverse initialization in order to improve the quality of the optimal solution. (13) In the formula: , These represent the positions before and after the gray wolf's reverse initialization. , These represent the upper and lower boundaries of the search space, respectively. Considering the convergence factor used in the GWO algorithm To achieve linear convergence, the algorithm's exploration is incomplete in the early stages, making it prone to local optima in the later stages. This embodiment's improved Grey Wolf algorithm uses the following convergence factor during iteration to enhance the algorithm's exploration and development capabilities while accelerating convergence: (14) (15) in, Let be the convergence factor for the t-th iteration. , These are the upper and lower limits of the convergence factor, respectively. t , T These are the current iteration count and the maximum iteration count, respectively. This is the decay function.

[0064] As the algorithm iterates to the later stages, all gray wolves tend to converge towards the optimal individual, resulting in low population diversity and a tendency to get trapped in local optima. To address this issue, the improved gray wolf algorithm in this embodiment uses a foraging mechanism based on the parrot algorithm in the later stages of iteration to determine the individual position after each iteration according to the following formula, thereby improving population diversity: (16) in, , , These represent the individual's position after iteration, the individual's position before iteration, and the position of the globally optimal individual, respectively. The folding factor, , All are random numbers between [0,1].

[0065] In the later stages of algorithm iteration, individuals are rapidly assimilated by the current optimal solution, clustering around it, causing the algorithm's search to stagnate and leading to premature convergence. To address this issue, the improved Grey Wolf algorithm in this embodiment generates variable-asynchronous lengths for individual positions based on the Levy flight strategy in the later stages of iteration to help escape local optima. (17) in, For variable asynchronous length, Indicates the direction of movement.

[0066] This embodiment uses the improved Grey Wolf algorithm described above to solve the multi-objective optimization function, employs back-learning to initialize the population to improve the quality of the optimal solution, and introduces an improved convergence factor. This allows for improved algorithm exploration and development capabilities while accelerating convergence speed. Furthermore, combining the parrot algorithm's somersault mechanism enhances population diversity. By adding the Levy flight strategy, the algorithm can also escape local optima, thus enabling the rapid and accurate solution of the optimal operating voltage threshold for the energy storage converter.

[0067] Specifically, the improved Grey Wolf algorithm is used to solve for the optimal action voltage threshold, as follows: Figure 2 As shown, the specific action flow is as follows: (1) Input the distribution network parameters, such as the system admittance matrix, transformer ratio and impedance value, node load power, etc., initialize the system operating conditions and set the initial charge and discharge state (SOC), charge and discharge efficiency and equipment response time of the energy storage converter.

[0068] (2) An initial population is generated by adopting a reverse learning strategy to improve the diversity of solutions and global search capability.

[0069] (3) Calculate the objective function value (i.e. fitness) of all individuals, determine the three best individuals, and select the individual (α wolf) and the second best individuals (β and δ wolves) and set them as the leader wolf.

[0070] (4) Determine whether the individual is in a clustered state: If it is in a clustered state, execute the Levi flight operation to enhance the global search capability; otherwise, calculate A, a, and C according to equations (13) and (16).

[0071] (5) Determine whether the equipment operates based on the optimal threshold obtained by the solution. If the node exceeds the limit, perform reactive power flow calculation and make corrections according to the set adjustment constraints to compensate the system voltage. Update the group position according to equation (14). If the node voltage does not exceed the limit, the equipment will charge and discharge normally.

[0072] (6) Check if the number of iterations has reached the preset termination condition. If not, return to step (3) to continue iterating; otherwise, output the optimal solution.

[0073] In this embodiment, after determining the optimal operating voltage threshold of the energy storage converter, the actual voltage of the grid-connected node is compared with the optimal operating voltage threshold to determine whether the energy storage converter needs to be triggered. The optimal operating voltage threshold includes an upper voltage threshold and a lower voltage threshold. Step S03, controlling the energy storage converter at the controlled grid-connected node to perform reactive power regulation based on the comparison result, includes: When the actual voltage at the target grid connection point is greater than the upper limit of the optimal operating voltage threshold, the target energy storage converter is triggered to absorb reactive power to suppress overvoltage. When the actual voltage at the target grid connection point is less than the lower limit of the optimal operating voltage threshold, the target converter is triggered to output reactive power for voltage compensation. When the actual voltage at the target grid connection point is within the range between the upper and lower voltage thresholds of the optimal operating voltage threshold, the target energy storage converter will not perform reactive power regulation.

[0074] This embodiment determines whether the grid-connected node voltage exceeds the limit based on the optimal operating voltage threshold determined adaptively. When the voltage exceeds the limit, the energy storage converter operates to suppress the voltage exceedance, thereby realizing adaptive regulation of the distribution network voltage. In this embodiment, the active power output of the converter is not regulated, but only its reactive power output is regulated.

[0075] Specifically, such as Figure 3 As shown, the specific process of achieving active distribution network voltage control and regulation based on reactive power regulation using an energy storage converter in this embodiment is as follows: (1) Before the initial parameter setting process starts, input the distribution network structure parameters, generate the reactive power-voltage sensitivity matrix, and adaptively solve the optimal operating voltage threshold of the target energy storage converter at the target grid connection node according to the multi-objective optimization function, including the upper limit threshold and the lower limit threshold.

[0076] (2) Monitor the actual operating status of the target grid-connected node in real time and obtain key data: collect the real-time voltage of the grid-connected node at fixed time intervals (e.g., 1 minute).

[0077] (3) Based on the relationship between the real-time voltage of the grid-connected node and the optimal operating voltage threshold, determine whether the energy storage converter should start regulation: ①If The voltage is determined to be overvoltage, triggering the converter to absorb reactive power and suppress further voltage increases.

[0078] ②If If the voltage is determined to be undervoltage, the converter is triggered to enter reactive power mode, thereby increasing the system voltage.

[0079] ③If If the voltage is within the safe range, the converter remains in standby mode, does not perform reactive power regulation, and performs normal energy storage charging and discharging.

[0080] (4) Calculate the amount of reactive power required for the energy storage converter based on the threshold between the real-time voltage of the grid-connected node and the optimal operating voltage threshold, and then determine the output power of the energy storage converter.

[0081] As an optional implementation, the calculation expression for the required reactive power regulation based on the deviation value, corresponding to the constructed multi-objective optimization function, is as follows: , for The deviation between the actual voltage of the grid-connected node and the operating voltage threshold at any given time. The actual voltage of the grid-connected node. , These are the upper and lower voltage thresholds for the action voltage threshold. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This is the upper limit of the reactive power capacity of the energy storage converter. This represents the reactive power-voltage sensitivity matrix.

[0082] Specifically, if an adjustment command is triggered, the required reactive power adjustment is calculated based on the sensitivity matrix. The generated reactive power-voltage sensitivity matrix is ​​then invoked, and combined with the current voltage deviation, the required reactive power change is calculated according to the above formula. Furthermore, considering the converter's reactive power capacity constraints, the system voltage is corrected to ensure the final adjustment is within the equipment's allowable range, thereby executing voltage regulation. The system calculates and outputs reactive power to achieve precise voltage control. After completing one adjustment, the results are verified to form a closed-loop control. The grid connection point voltage is collected again, and the voltage deviation after adjustment is calculated. If it is within the safe range, the adjustment ends, and the system returns to step S03 to enter the next round of monitoring. If it still exceeds the threshold, steps S03-S04 are repeated until the voltage meets the standard.

[0083] Specifically, the reactive power-voltage sensitivity matrix is ​​used to describe the degree of influence of changes in reactive power injection at nodes on the node voltage amplitude. The calculation expression for the reactive power-voltage sensitivity matrix is ​​as follows: (18) in, This represents the change in voltage amplitude. , , , The Jacobian matrix is ​​composed of the structural parameters of the distribution network. , , , These correspond to reactive power-voltage sensitivity, reactive power-phase angle sensitivity, active power-phase angle sensitivity, and active power-voltage sensitivity, respectively. Inject reactive power changes into the grid-connected nodes. This is the reactive power-voltage sensitivity matrix. It is calculated by comparing the voltage at the grid connection point. With the set action threshold voltage and This enables the device to adapt its actions.

[0084] To verify the effectiveness of the present invention, the method of the present invention was used based on an improved IEEE 33-node power distribution system (such as...). Figure 4 Simulations were performed (as shown), where 1-33 represent the grid-connected node number, PV represents the photovoltaic system, ESS represents the energy storage system, and the reference voltage is... =12.66kV, reference capacity =100MVA, with an allowable node voltage range of 0.93~1.07pu; energy storage devices with a capacity of 1000kW·h are installed at nodes 22 and 18; photovoltaics are connected at nodes 3, 14, and 22, each with a capacity of 2.5MW.

[0085] In this embodiment, three scenarios are set for verification and analysis. Scenario 1: The energy storage device capacity is only optimized for active power, without reactive power compensation. In this case, the power factor is... =1; Scenario 2: The converter adopts a traditional quantitative reactive power compensation strategy, that is, the equipment action threshold is set to a fixed value; Scenario 3: The converter's action threshold adopts an adaptive control strategy, that is, the method of this invention. Based on Figure 4 The power distribution network structure diagram, and the action threshold settings for scenarios 2 and 3 are shown in Table 1: Table 1: Action Threshold Settings

[0086] Typical source-load data used in this embodiment are as follows: Figure 5 As shown, based on Figure 5 The source payload data used, for Figure 4 The distribution network was optimized and the results are shown in Table 2.

[0087] Table 2: Optimization results for different scenarios

[0088] As shown in Table 2, compared to Scenario 2, the present invention sets the ESS system to track system voltage changes in real time and dynamically decides the reactive power output of the equipment. This results in a much larger reactive power change in the converter compared to Scenario 2. This relatively ample reactive power dispatch capacity also ensures that the distribution network can be effectively regulated during peak electricity consumption periods. Therefore, Scenario 3 further reduces network losses and voltage deviation by 7.2% and 61.7% respectively, compared to Scenario 2. If a fixed reactive power control (i.e., Scenario 2) is used, the converter's set action threshold range is relatively large, resulting in fewer reactive power actions and primarily active power regulation of the system. Therefore, the network losses and voltage deviations in Scenario 1 and Scenario 2 are relatively small. In summary, under similar economic conditions, the dispatching results of the present invention are optimal, effectively verifying the effectiveness of the present invention.

[0089] Comparison results of compensation effects under voltage deviations in different scenarios are as follows: Figure 6As shown. This embodiment selects the system node voltage at 18:00 for comparative analysis, from... Figure 6 It can be seen that after compensation during this heavy load period, the minimum system voltage in the three scenarios is 0.938 pu, 0.954 pu, and 0.961 pu, respectively. Comparing the minimum system voltages in scenarios 1 and 2, it can be seen that the ESS only performs traditional active power compensation for the distribution network. Although it can meet the basic allowable voltage deviation requirements, if the grid-connected load increases later, the energy storage system needs to be expanded, resulting in poor economy and scalability. Comparing scenarios 2 and 3, it can be seen that this invention, by adaptively tracking the real-time system voltage and dynamically adjusting the reactive power output of the ESS, can more accurately improve the system voltage quality, and the optimization result of this invention is optimal.

[0090] To verify the feasibility and advantages of the model solving method in this invention, the optimization model was solved using the MOGWO (Multi-Objective Grey Wolf Optimization Algorithm), PSO (Particle Swarm Optimization Algorithm), and GWO (Grey Wolf Optimization Algorithm) algorithms of this invention, respectively. The fitness convergence curves and optimization results of different algorithms are shown below. Figure 7 And as shown in Table 3. According to Figure 7 It is known that the MOGWO, GWO, and PSO algorithms of this invention require 15, 105, and 140 iterations, respectively, to obtain the optimal solution of the model. However, since the GWO and PSO algorithms have not been improved, they have repeatedly fallen into local optima during the iteration process. The MOGWO algorithm of this invention adopts an initialization strategy of reverse learning, which results in a more uniform distribution of the initial solution in the solution space. Furthermore, it uses the foraging mechanism of the parrot algorithm to search for potential optimal solutions during the iteration process. Therefore, it can ensure that the target solution is obtained in a shorter time, and the quality of the obtained solution is far superior to that of the GWO and PSO algorithms.

[0091] Table 3: Optimization results of different algorithms

[0092] As shown in Table 3, although the number of actions to control the converter and the reactive power output in the model scheduling strategies obtained by the PSO algorithm and GWO algorithm are slightly lower than those of the present invention, the network loss and voltage deviation of the optimized system are similar to those of the present invention. However, the calculation time of the MOGWO algorithm of the present invention is lower than that of the PSO and GWO algorithms, and it is more suitable for distribution networks that require real-time scheduling.

[0093] This embodiment further provides a computer device, including a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method as described above.

[0094] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0095] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0096] Those skilled in the art will understand that the above embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for active voltage control of a distribution network based on energy storage converter regulation, characterized in that the steps include... include: A multi-objective optimization function is constructed with the optimization objectives of minimizing system line loss, minimizing reactive power regulation by energy storage converter, and minimizing voltage fluctuation risk index. The voltage fluctuation risk index is calculated based on the photovoltaic output at two consecutive moments. The operating voltage threshold at the current moment is calculated based on the operating voltage threshold at the previous moment and the voltage fluctuation risk index. The reactive power regulation by energy storage converter is calculated based on the operating voltage threshold at the current moment. The system obtains the line losses of each branch in the controlled distribution network, the regulated reactive power of the energy storage converter connected to the grid node, and the photovoltaic output at different times under different photovoltaic and load output scenarios. The system solves the constructed multi-objective optimization function with the operating voltage threshold of the energy storage converter as the decision variable to obtain the optimal operating voltage threshold of the energy storage converter. Real-time monitoring of the actual voltage of the target grid-connected node, and comparison with the optimal operating voltage threshold corresponding to the target energy storage converter connected to the target grid-connected node. Based on the comparison result, the target energy storage converter is triggered to perform reactive power regulation in order to adaptively adjust the voltage of the energy storage grid-connected node. When the target energy storage converter is triggered to perform no regulation, the deviation between the actual voltage of the target grid-connected node monitored in real time and the optimal action voltage threshold is calculated. Based on the deviation and the reactive power-voltage sensitivity matrix, the reactive power regulation required by the target energy storage converter is calculated.

2. The active voltage control method for distribution networks based on energy storage converter regulation according to claim 1, characterized in that, The constructed multi-objective optimization function is as follows: The constraints include: in, To optimize system line loss, The goal is to optimize the regulation of reactive power in energy storage converters. Optimize the target for voltage fluctuation risk indicators. , , These are the weighting coefficients. , They are respectively with the first The upper and lower voltage thresholds are among the operating voltage thresholds of the energy storage converters connected to each grid-connected node. , , These are the upper and lower limits of the operating voltage threshold and the rated value; the optimization target for the reactive power regulation of the energy storage converter. The calculation expression is: in, For the number of scenes, The total number of moments. This represents the number of grid-connected nodes within the distribution network. For the scene The probability of occurrence, For the scene Next access The energy storage converter of each grid-connected node is in t The amount of reactive power regulated at any given time, including the amount of reactive power generated or absorbed, is discussed in the context of... t The expression for calculating the regulated reactive power of the energy storage converter at the target grid-connected node under a specified scenario at a given time is: in, for The deviation between the actual voltage of the grid-connected node and the operating voltage threshold at any given time. The actual voltage of the grid-connected node. , These are the upper and lower voltage thresholds for the action voltage threshold. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This is the upper limit of the reactive power capacity of the energy storage converter. This represents the reactive power-voltage sensitivity matrix.

3. The active voltage control method for distribution networks based on energy storage converter regulation according to claim 2, characterized in that, Voltage fluctuation risk index optimization target The calculation expression is: in, , The upper and lower voltage thresholds are the operating voltage thresholds for the energy storage converter at the next moment. , These are the upper and lower voltage thresholds of the operating voltage threshold for the energy storage converter at the previous moment. for t Real-time photovoltaic output for t- Photovoltaic power output at 1 moment For photovoltaic rated capacity, This is a voltage risk fluctuation risk indicator. , This is a preset ratio value. > , These are standardized constants; System line loss optimization target The calculation expression is: in, For the number of distribution network branches, In the scene Down t Time of the first l Line loss on one branch.

4. The active voltage control method for distribution networks based on energy storage converter regulation according to claim 2, characterized in that, When constructing the multi-objective optimization function, the constraints also include any one or more of the following: power flow constraints, branch current constraints, converter reactive power regulation capacity constraints, power factor constraints, and operating frequency constraints. The converter reactive power regulation capacity constraint ensures that the reactive power regulation of the energy storage converter is within a preset range. The branch current constraint ensures that the current of each branch at each time point is lower than a preset upper current limit. The power factor constraint ensures that the power factor of the energy storage converter is greater than a preset minimum power factor. The power flow constraints are: in, , Injection nodes Active and reactive power, , and These are the grid-connected nodes. With grid connection nodes The branch conductance, susceptance, and phase angle difference between them Number of distribution network branches; , These are the grid-connected nodes. With grid connection nodes The voltage; The action frequency constraint is: in, , These are the weighting coefficients. The penalty coefficient is... The number of adjustments made by the energy storage converter. T Indicates the statistical time period. f The regulation frequency for the energy storage converter. This is the maximum regulation frequency of the energy storage converter.

5. The active voltage control method for distribution networks based on energy storage converter regulation according to claim 1, characterized in that, The optimal operating voltage threshold includes an upper voltage threshold and a lower voltage threshold, and the step of controlling the energy storage converter at the controlled grid-connected node to perform reactive power regulation based on the comparison result includes: When the actual voltage at the target grid connection point is greater than the upper limit of the optimal action voltage threshold, the target energy storage converter is triggered to absorb reactive power to suppress overvoltage. When the actual voltage at the target grid connection point is less than the lower limit voltage threshold of the optimal action voltage threshold, the target converter is triggered to output reactive power for voltage compensation. When the actual voltage at the target grid connection point is within the range between the upper and lower voltage thresholds of the optimal operating voltage threshold, the target energy storage converter will not perform reactive power regulation.

6. The active voltage control method for distribution networks based on energy storage converter regulation according to any one of claims 1 to 5, characterized in that, The calculation expression for the required reactive power adjustment based on the aforementioned deviation value is as follows: in, for The deviation between the actual voltage of the grid-connected node and the operating voltage threshold at any given time. The actual voltage of the grid-connected node. , These are the upper and lower voltage thresholds for the action voltage threshold. for Reactive power regulation of the energy storage converter at all times , These represent the output power of the energy storage converter before and after the action. The time is the time after the action. The moment is the moment before the action. This is the upper limit of the reactive power capacity of the energy storage converter. This represents the reactive power-voltage sensitivity matrix.

7. The active voltage control method for distribution networks based on energy storage converter regulation according to any one of claims 1 to 5, characterized in that, The constructed multi-objective optimization function is solved using an improved Grey Wolf algorithm, in which a back-learning method is employed to determine the position of the Grey Wolf back-initialization. In the formula: , These represent the positions before and after the gray wolf's reverse initialization. , These represent the upper and lower boundaries of the search space, respectively. The following convergence factor is used during the iteration process: in, Let be the convergence factor for the t-th iteration. , These are the upper and lower limits of the convergence factor, respectively. t , T These are the current iteration count and the maximum iteration count, respectively. This is the decay function.

8. The active voltage control method for distribution networks based on energy storage converter regulation according to claim 7, characterized in that, In the improved gray wolf algorithm, the foraging mechanism based on the parrot algorithm is followed in the later stages of iteration. Determine the individual's position after each iteration during the iterative process. , , These represent the individual's position after iteration, the individual's position before iteration, and the position of the globally optimal individual, respectively. The folding factor, , All are random numbers between [0, 1]; In the later stages of iteration, the Lévy flight strategy is used to generate variable-asynchronous lengths for individual positions to help escape local optima. in, For variable asynchronous length, Indicates the direction of movement.

9. An electronic device comprising a processor and a memory, the memory being used to store a computer program, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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