Photovoltaic inverter voltage threshold setting method, device and equipment and storage medium

Abstract

The invention provides a photovoltaic inverter voltage threshold setting method and device, equipment and a storage medium, and relates to the technical field of power distribution network dispatching. The method comprises the following steps: taking the minimum total network loss and the minimum total voltage deviation of the power distribution network as targets, introducing a voltage out-of-limit penalty term, and determining a target function; setting constraint conditions including power balance, node voltage and equipment state, and defining decision variables including voltage regulating equipment gear, photovoltaic inverter voltage threshold and reactive equipment output; according to the constraint condition, solving the target function through an improved fishing optimization algorithm to obtain an optimal voltage threshold value of each photovoltaic inverter; according to the improved fishing optimization algorithm, an initial population is generated through Fuch chaotic mapping, and the self-adaptive weight of the position updating algorithm is determined based on the current evaluation frequency and the maximum evaluation frequency in the exploration stage. According to the invention, photovoltaic reactive power output can be adjusted by optimizing the voltage threshold value of the photovoltaic inverter, and active real-time voltage regulation and control of the power distribution network are realized.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching technology, and in particular to a method, apparatus, equipment and storage medium for setting voltage thresholds for photovoltaic inverters. Background Technology

[0002] With the continuous development of new power systems, the increasing penetration rate of distributed photovoltaic (PV) power in distribution networks poses new challenges to network operation and dispatch. The large-scale integration of distributed PV transforms the distribution network into an active network, where power flow is no longer a simple flow from power source to load; reverse power flow can also occur when PV output is high. Because distributed PV output is significantly random and volatile due to environmental factors, the spatiotemporal mismatch between PV output and load power exacerbates problems such as voltage exceeding limits and a surge in network losses, becoming urgent issues to be addressed in the dispatch of new distribution networks.

[0003] Traditional reactive power optimization and regulation methods, such as capacitor bank configuration adjustment, are difficult to respond quickly to rapid changes in photovoltaic grid-connected power. Photovoltaic inverters possess excellent reactive power compensation capabilities, and their active power output often does not reach their rated power. Therefore, in most cases, photovoltaic inverters themselves have a large residual reactive power capacity, which can be used to quickly respond to changes in distribution network voltage. Thus, optimizing the voltage threshold of photovoltaic inverters, coordinating the reactive power output of the distribution network, and proactively achieving real-time voltage regulation of the distribution network are of great significance for power system optimization. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus, device and storage medium for setting the voltage threshold of a photovoltaic inverter, so as to adjust the reactive power output of photovoltaic power generation by optimizing the voltage threshold of the photovoltaic inverter and realize active real-time voltage regulation of the distribution network.

[0005] In a first aspect, embodiments of the present invention provide a method for setting a voltage threshold for a photovoltaic inverter, comprising: The objective function is determined by minimizing the total network loss and the total voltage deviation of the distribution network, and by introducing a voltage over-limit penalty term. Set constraints including power balance, node voltage and equipment status, and define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output; Based on the constraints, the objective function is solved using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter. The improved fishing optimization algorithm generates an initial population through Fuch chaotic mapping and determines the adaptive weights of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

[0006] In one possible implementation, the objective function includes:

[0007] in, For the total network loss of the distribution network, The total voltage deviation of the distribution network. Penalties for voltage constraints at each node, and These are the preset network loss weight and voltage deviation weight, respectively. This is the voltage over-limit penalty coefficient. This represents the total number of nodes in the distribution network.

[0008] In one possible implementation, the settings include constraints on power balance, node voltage, and device states, including: For active power power balance, constrained nodes active power This is equal to the node and all connected nodes. The total amount of active power exchanged between them through the lines; For reactive power power balance, constraint nodes reactive power This is equal to the node and all connected nodes. The total amount of reactive power exchanged between them through the lines; For node voltages, constrain the voltage of each node to be between its allowed minimum and maximum voltages; For equipment status, the apparent power of the photovoltaic inverter is constrained to be greater than or equal to 0 and not exceed the rated capacity of the photovoltaic inverter; the tap position of the on-load tap-changing transformer is constrained to be between the minimum and maximum allowable tap positions of the transformer; the absolute value of the reactive power output of the static var generator at any node at any time is constrained to be less than or equal to the maximum reactive power output of the static var generator at that node.

[0009] In one possible implementation, solving the objective function using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each of the photovoltaic inverters includes: An initial population is generated using Fuch chaotic mapping, and the fitness values ​​of individuals in the initial population are calculated according to the objective function to determine the initial optimal solution. The exploration phase and the development phase are divided according to the relationship between the number of evaluations and the maximum number of evaluations. In the exploration phase, the population search method is controlled by the catch rate parameter, and in the development phase, the population search range is determined by the Gaussian distribution. In the exploration phase, the adaptive weight of the position update algorithm is determined based on the current number of evaluations and the maximum number of evaluations, and the updated population is generated. The updated population is subjected to boundary processing to ensure that the solution vector satisfies the constraints, the fitness value is recalculated and the current optimal solution is updated. Repeat the iteration until the preset maximum number of evaluations is reached, and output the optimal solution for each decision variable; The optimal voltage threshold of the photovoltaic inverter is extracted from the optimal solutions of each decision variable.

[0010] In one possible implementation, during the exploration phase, determining the adaptive weights of the position update algorithm based on the current number of evaluations and the maximum number of evaluations, and generating an updated population, includes: The adaptive weight is calculated based on the ratio of the current number of evaluations to the maximum number of evaluations, and the adaptive weight is positively correlated with the ratio. In the position update expression, the search range of the position update algorithm is adjusted by the adaptive weights to generate the updated population.

[0011] In one possible implementation, the optimal voltage threshold for each of the photovoltaic inverters includes a minimum value. and maximum value ; After solving the objective function using an improved fishing optimization algorithm based on the constraints to obtain the optimal voltage threshold for each photovoltaic inverter, the method further includes: When the node voltage of the photovoltaic inverter satisfy When the photovoltaic inverter does not generate reactive power, it does not generate reactive power. When the photovoltaic inverter absorbs reactive power; when At that time, the photovoltaic inverter generates reactive power.

[0012] In one possible implementation, after solving the objective function according to the constraints using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each of the photovoltaic inverters, the method further includes: when or At that time, calculate respectively with and The difference is used to determine the reactive power required for coordinated optimization of various devices in the distribution network, combined with the Jacobian matrix.

[0013] Secondly, embodiments of the present invention provide a photovoltaic inverter voltage threshold setting device, comprising: The objective function determination module is used to determine the objective function with the objectives of minimizing the total network loss and the total voltage deviation of the distribution network, and introduces a voltage over-limit penalty term. The constraint variable setting module is used to set constraints including power balance, node voltage and equipment status, and to define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output. The threshold solving module is used to solve the objective function according to the constraints using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each of the photovoltaic inverters; wherein, the improved fishing optimization algorithm generates an initial population through Fuch chaotic mapping, and determines the adaptive weight of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor executes the steps of the method as described in the first aspect or any implementation thereof.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any implementation thereof.

[0016] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: In this embodiment of the invention, the objectives are to minimize the total network loss and the total voltage deviation of the distribution network. This clarifies the optimization direction and balances the optimization requirements of reducing the total network loss and the total voltage deviation. A voltage limit violation penalty term is introduced to determine the objective function, strengthening the constraint on the voltage limit violation problem. Constraints including power balance, node voltage, and equipment status are set to ensure that the model conforms to the physical rules of power balance and safe operation of equipment in the distribution network. Decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold, and reactive power output are defined to clarify the key parameters to be optimized in order to determine the optimal photovoltaic inverter voltage threshold. Based on the constraints, the objective function is solved by an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter. The improved fishing optimization algorithm generates an initial population through Fuchs chaotic mapping and determines the adaptive weight of its position update algorithm based on the current evaluation count and the maximum evaluation count during the exploration phase. This improves the diversity of the initial population and the balance of the optimization process, making the obtained optimal voltage threshold for the photovoltaic inverter more accurate. This invention adjusts the reactive power output of photovoltaics by adjusting the voltage threshold of the photovoltaic inverter, ensuring that reactive power compensation based on the voltage threshold will not negatively affect the distribution network loss, while avoiding the risk of voltage exceeding the limit, effectively reducing the total distribution network loss and the total voltage deviation, improving the accuracy of photovoltaic inverter voltage regulation, and ensuring the stable operation of the distribution network. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of a photovoltaic inverter voltage threshold setting method according to an embodiment of the present invention; Figure 2 This is a topology diagram of an IEEE 33-node system provided in an embodiment of the present invention; Figure 3 This is a voltage curve diagram before and after optimization at the 12th hour under 24-hour rolling optimization of IEEE 33 nodes provided in an embodiment of the present invention; Figure 4 This is a network loss curve before and after 24-hour rolling optimization of IEEE 33 nodes provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a photovoltaic inverter voltage threshold setting device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0024] As the penetration rate of distributed photovoltaic (PV) power in distribution networks continues to increase, the randomness and volatility of its output not only alter the traditional single power flow distribution but also easily trigger problems such as node voltage exceeding limits and a surge in network losses, posing a severe challenge to the stable operation of the distribution network. Traditional reactive power regulation methods, such as on-load tap changers and capacitor banks, have slow response speeds and are difficult to adapt to the rapid changes in PV grid-connected power, failing to meet real-time voltage regulation requirements. PV inverters possess ample reactive power reserve capacity, making them ideal devices for real-time voltage regulation in distribution networks. However, existing voltage threshold setting methods often suffer from a mismatch between the optimization algorithm's objective and the distribution network's regulation needs, leading to unreasonable threshold settings and potential over-compensation or under-compensation of reactive power, which can exacerbate network losses or voltage deviations. Therefore, there is an urgent need for a PV inverter voltage threshold setting method that can balance distribution network regulation needs, has reliable algorithms, and adapt to multiple operating conditions, in order to fully utilize the reactive power regulation potential of PV inverters and ensure the economical and stable operation of the distribution network.

[0025] See Figure 1 This invention provides a method for setting the voltage threshold of a photovoltaic inverter, detailed below: Step S101: The objective function is determined by minimizing the total network loss and the total voltage deviation of the distribution network, and by introducing a voltage over-limit penalty term.

[0026] For example, the objective function includes:

[0027] in, For the total network loss of the distribution network, The total voltage deviation of the distribution network. Penalties for voltage constraints at each node, and These are the preset network loss weight and voltage deviation weight, respectively. This is the voltage over-limit penalty coefficient. This represents the total number of nodes in the distribution network.

[0028] The embodiments of the present invention determine an objective function that includes total network loss, total voltage deviation, and voltage over-limit penalty term, taking into account the optimization requirements of distribution network loss and voltage deviation, and strengthening the constraint on voltage over-limit.

[0029] Step S102: Set constraints including power balance, node voltage and equipment status, and define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output.

[0030] In one possible implementation, constraints are set including power balance, node voltage, and device state, including: For active power power balance, constrained nodes active power This is equal to the node and all connected nodes. The total amount of active power exchanged between them through the lines; For reactive power power balance, constraint nodes reactive power This is equal to the node and all connected nodes. The total amount of reactive power exchanged between them through the lines; For node voltages, constrain the voltage of each node to be between its allowed minimum and maximum voltages; For equipment status, the apparent power of the photovoltaic inverter is constrained to be greater than or equal to 0 and not exceed the rated capacity of the photovoltaic inverter; the tap position of the on-load tap-changing transformer is constrained to be between the minimum and maximum allowable tap positions of the transformer; the absolute value of the reactive power output of the static var generator at any node at any time is constrained to be less than or equal to the maximum reactive power output of the static var generator at that node.

[0031] This invention provides a solution to ensure the compliance of power distribution network operation and the safety of equipment by setting multi-dimensional constraints, and defines the core decision variable as the optimal threshold solution to clarify the variable object.

[0032] Step S103: Based on the constraints, the objective function is solved using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter. The improved fishing optimization algorithm generates an initial population through Fuch chaotic mapping and determines the adaptive weights of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

[0033] In this embodiment of the invention, the improved fishing optimization algorithm enhances the diversity of the initial population and the balance of the optimization process through chaotic mapping and adaptive weights; by solving the objective function based on constraints, a more accurate optimal voltage threshold can be obtained.

[0034] In this embodiment of the invention, the reactive power output of photovoltaics is adjusted by adjusting the voltage threshold of the photovoltaic inverter. This ensures that reactive power compensation based on the voltage threshold will not have a negative impact on the distribution network loss, while avoiding the risk of voltage exceeding the limit. This effectively reduces the total distribution network loss and the deviation between the total voltage and the total distribution network, improves the accuracy of the photovoltaic inverter voltage regulation, and ensures the stable operation of the distribution network.

[0035] In some embodiments, traditional optimization algorithms often suffer from strong initial population randomness and difficulty in balancing range and accuracy during the optimization process, leading to output thresholds that easily exceed safety boundaries. To address this issue, it is crucial to improve the population generation and optimization mechanisms of optimization algorithms to enhance the reliability and adaptability of threshold solutions. Therefore, solving the objective function using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter can include: An initial population is generated using Fuch chaotic mapping, and the fitness values ​​of individuals in the initial population are calculated based on the objective function to determine the initial optimal solution. The exploration phase and the development phase are divided based on the relationship between the number of evaluations and the maximum number of evaluations. In the exploration phase, the population search method is controlled by the catch rate parameter, while in the development phase, the population search range is determined by the Gaussian distribution. In the exploration phase, the adaptive weight of the position update algorithm is determined based on the current number of evaluations and the maximum number of evaluations, and the updated population is generated. Perform boundary processing on the updated population to ensure that the solution vector satisfies the constraints, recalculate the fitness value and update the current optimal solution; Repeat the iteration until the preset maximum number of evaluations is reached, and output the optimal solution for each decision variable; The optimal voltage threshold of the photovoltaic inverter is extracted from the optimal solutions of each decision variable.

[0036] The embodiments of the present invention improve the optimization performance through an improved fishing optimization algorithm, ensure the diversity of the initial population by using chaotic mapping, and adopt adaptive weights to balance the optimization range and accuracy. It can accurately output the optimal voltage threshold of the photovoltaic inverter, providing reliable support for voltage regulation and network loss optimization in the distribution network.

[0037] In some embodiments, the position update formula in the exploration phase of traditional fishing optimization algorithms lacks a dynamic adjustment strategy, which can easily lead to insufficient exploration range in the early stages of iteration or population chaos in the later stages of iteration, making it difficult to adapt to the optimization requirements of photovoltaic inverter threshold solving. Different iteration phases have different requirements for population exploration capabilities, necessitating flexible adjustment of the position update range to improve the rationality and adaptability of the algorithm's optimization during the exploration phase. Therefore, in the exploration phase, based on the current number of evaluations and the maximum number of evaluations, determining the adaptive weights of the position update algorithm to generate the updated population can include: The adaptive weight is calculated based on the ratio of the current number of evaluations to the maximum number of evaluations, and the adaptive weight is positively correlated with the ratio. In the position update expression, the search range of the position update algorithm is adjusted by adaptive weights to generate the updated population.

[0038] In this embodiment of the invention, the adaptive weights are dynamically adjusted according to the proportion of evaluation times, which can adapt to the optimization needs at different iteration stages of the exploration phase and avoid exploration bias caused by fixed weights. By dynamically adjusting the position update range by weights, a population that is more in line with the optimization goal can be generated, improving the rationality of population updates and optimization efficiency in the exploration phase.

[0039] In some embodiments, once the voltage threshold of the photovoltaic inverter is determined, the lack of quantitative basis for coordinated adjustment of various devices when the voltage exceeds the limit can easily lead to delayed voltage regulation response or unreasonable output, affecting the voltage stability and optimization effect of the distribution network. Therefore, the optimal voltage threshold for each photovoltaic inverter may include the minimum value. and maximum value Based on the constraints, the objective function is solved using an improved fishing optimization algorithm. After obtaining the optimal voltage threshold for each photovoltaic inverter, the following can be included: When the node voltage of the photovoltaic inverter satisfy When the photovoltaic inverter does not generate reactive power, it does not generate reactive power. When the photovoltaic inverter absorbs reactive power; when At that time, the photovoltaic inverter generates reactive power.

[0040] Based on the constraints, after solving the objective function using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter, the following can also be included: when or At that time, calculate respectively with and The difference is used to determine the reactive power required for coordinated optimization of various devices in the distribution network, combined with the Jacobian matrix.

[0041] The present invention clarifies the criteria for judging the reactive power output of photovoltaic inverters, realizes no action when the voltage is within the threshold range and accurate response when the voltage exceeds the limit, and combines the voltage difference and Jacobian matrix to quantify the reactive power demand of each device, thereby improving the coordination and accuracy of voltage regulation in the distribution network.

[0042] This invention provides a method for setting the voltage threshold of a photovoltaic inverter, detailed below: (1) Define the photovoltaic inverter voltage threshold setting problem, including the number of photovoltaic units connected to the distribution network, and clarify the optimization objective. To enable the photovoltaic inverter to provide reactive power compensation to the distribution network when voltage limits are exceeded, thereby improving the voltage limit exceeding problem, considering that over-compensation or under-compensation during reactive power regulation will increase network losses, network losses are considered as one of the optimization objectives to make the set voltage threshold more reasonable and improve the economic efficiency of grid operation. Therefore, minimizing voltage deviation and minimizing network losses are taken as the optimization objectives of the photovoltaic inverter voltage threshold setting method. See [link to relevant documentation] Figure 2 The present invention provides an IEEE 33-node system topology diagram, which is modified from the standard IEEE 33-node system topology: three distributed photovoltaic systems are connected to nodes 8, 12, and 16, with installed capacities of 0.3MW, 0.3MW, and 0.4MW, respectively.

[0043] Meanwhile, considering the spatiotemporal mismatch between photovoltaic power output and load power, in order to ensure that the photovoltaic inverter voltage threshold setting method proposed in this invention can reasonably set the photovoltaic inverter voltage threshold according to the distribution network operating status under different time periods and different operating conditions to meet the distribution network voltage regulation requirements, this embodiment of the invention adopts a 24-hour rolling optimization mode, with 1 hour as a rolling time window. The photovoltaic inverter voltage threshold is periodically updated according to the real-time operating conditions of the distribution network. Based on the comparison results between the real-time voltage of each photovoltaic access node and the updated threshold, the reactive power output adjustment direction and amplitude of each photovoltaic inverter in the distribution network are controlled, ultimately achieving 24-hour distribution network reactive power compensation optimization.

[0044] (2) Establish a mathematical model for voltage regulation of photovoltaic inverters in distribution networks, including establishing an improved distribution network node system, determining the objective function and constraints of the voltage threshold setting optimization model, including power balance constraints, equipment operation constraints and equipment status constraints; define the decision variables of the system including the tap position of the on-load tapchanger (OLTC), the voltage threshold of each photovoltaic inverter, and the output of the static var generator (SVG).

[0045] See Figure 2 In this embodiment of the invention, SVG is connected at nodes 10 and 28, with reactive power capacities of 0.85 Mvar and 1.25 Mvar, respectively; an OLTC is added to the transformer between nodes 0 and 1, with a turns ratio range of 0.95~1.05 and an adjustment step size of 1.25%. The system has a total of 9 control variables: the tap position of one OLTC. tap Each of the three distributed photovoltaic systems has its own upper and lower voltage thresholds. , , , , , Reactive power output of 2 SVGs and .

[0046] Considering the potential for overcompensation and undercompensation in reactive power regulation of distribution networks, this embodiment of the invention aims to minimize system network losses. It combines the photovoltaic inverter voltage threshold to obtain the optimal reactive power output scheme, avoiding increased network losses caused by excessive or insufficient reactive power output. Within each rolling optimization time window, the network loss is expressed as:

[0047] In the formula, This represents the total number of nodes in the distribution network. , They are nodes ,node voltage, For nodes ,node The phase angle difference between the voltages, For nodes and nodes Mutual conduction between them.

[0048] Node voltage is one of the important indicators for measuring system stability and power quality. Therefore, this embodiment of the invention uses minimizing voltage deviation as the second optimization objective to ensure that the voltage of each node in the distribution network fluctuates near its rated value and within a reasonable range. Within each rolling optimization time window, the expression for voltage deviation is:

[0049] in, This is the rated voltage of the power distribution network.

[0050] To strictly prevent voltage exceedances in the distribution network, a penalty term is added to the objective function when a node voltage exceeds its limit. The expression for the voltage penalty term is:

[0051] In summary, this network optimization process prioritizes network loss. and voltage deviation The objective function is minimized, and considering voltage constraints at each node, the voltage range is required to be between 0.95 and 1.05. The specific expression of the objective function is:

[0052] in, , These represent network losses respectively. and voltage deviation The weight is set as follows in this embodiment of the invention: , .

[0053] The expressions for each constraint are as follows: Power balance constraints:

[0054] in, This represents the total number of nodes in the distribution network. and Distributed photovoltaic at nodes The active and reactive power output at the point; and They are respectively at the node The active and reactive power of the load at the location; and They are nodes and nodes Mutual conductance and mutual susceptance between them.

[0055] Node voltage constraints:

[0056] in, and These represent the minimum and maximum values ​​of the specified node voltage, set to 0.95 and 1.05 respectively.

[0057] Equipment status constraints:

[0058]

[0059]

[0060] in, and They are respectively Time Node The active and reactive power output of photovoltaic power generation, For nodes Rated capacity of the photovoltaic system; For nodes SVG in Moments of effortless exertion For nodes The maximum allowable reactive power output of the SVG; For OLTC gear, and These are the minimum and maximum gears for the OLTC.

[0061] (3) The Catch Fish Optimization Algorithm (CFOA) was selected as the optimization algorithm for the example. Based on the original CFOA, two improvements were made: the population was initialized using Fuchs chaotic mapping to increase the diversity of the population, and adaptive weights were introduced in the algorithm exploration stage. To improve algorithm performance.

[0062] Generate a scale of using the Fuch chaotic mapping formula. The initial population is used to calculate the fitness value of each individual and obtain the optimal solution. ,in, Indicates the number of individuals in the population. and They represent the 1st and 2nd in the population, respectively. Individual.

[0063] To increase population diversity, a Fuch chaotic mapping is added to the original CFOA to initialize the population. The expression for the Fuch function is:

[0064] in, Indicates the first In the nth iteration Individual.

[0065] The population is initialized by generating a set of random numbers using the Fuch function, as shown in the expression:

[0066] in, and They represent the first Upper and lower limits of the control variable; This indicates that the Fuch function generates the first... The individual The position of the dimension.

[0067] (5) Determine whether the maximum number of evaluations has been reached. If so, output the global optimal solution of the algorithm, including the specific output of each control variable; otherwise, execute (6).

[0068] (6) Determine the number of evaluations Has it been achieved? Half of it. If CFOA is currently in the exploratory stage, involving the shuffling of individual population sequences and the incorporation of catch rate parameters. Random numbers randomly distributed between [0,1] Compare them. If satisfied... Then the population is updated using the independent search formula during the exploration phase; if the following conditions are met... The population is then updated using the group encirclement formula during the exploration phase, and its catch rate parameter... The expression is:

[0069] In the early stages of fishing, fish populations were abundant, resulting in peak catch rates. However, as fishermen continued fishing, catches gradually declined. Initially, during exploration, fishermen primarily employed individual searches and disturbed waters, supplemented by group search strategies. As exploration progressed, the environmental advantage shifted from fish populations to fishermen, manifested in increased water turbidity, decreased fish visibility, and reduced fish numbers, thus lowering the catch rate. Subsequently, fishermen shifted their strategy to primarily using group searches, supplemented by individual exploration.

[0070] Independent search: In the search for schools of fish, fishermen deliberately disturb the surrounding water, making it turbid to induce fish to surface for air or create bubbles, forming ripples on the surface. Based on their accumulated fishing experience, fishermen can independently determine the potential location of fish based on the surface disturbances, and adjust their own search direction and position according to the catches of others. When a random number is generated... Less than When the position is updated, follow the formula below:

[0071]

[0072]

[0073] in, Indicates the first The fitness value of each individual; This represents an individual randomly selected from the population, and ,so For the first The fitness value of each individual; and These represent the worst and best fitness values, respectively. Indicates the first The iteration of the ... The individual in the first The position of the dimension; Indicates the first The iteration of the ... The individual in the first The position of the dimension; A random number between (0,1) The dimension is A random unit vector; Indicates the scope of an individual's survey.

[0074] in, For the first The Euclidean distance between an individual and a reference object The empirical analysis value is obtained by taking any position as the reference object.

[0075] Group search: Fishermen use fishing nets to improve their catch efficiency while randomly forming groups of 3-4 people to collaboratively surround suspicious areas. As fish are gradually caught and the groups gradually concentrate, the fishermen's movement deviations decrease. By utilizing each person's unique mobility, the area can be explored more precisely. When generating random numbers... When satisfied At that time, the position update formula is:

[0076]

[0077] in, Indicates by The target location is surrounded by a group of individuals; For the first After the next iteration update The location of each individual; In order to be in Next update The first in the group The individual in the first The position of the dimension; A random number between (0,1) represents the speed at which an individual approaches the target; is a random number between (-1, 1), representing the offset of the movement.

[0078] Introducing adaptive weights during the exploration phase ,in The expression is:

[0079] The position update expression for CFOA with adaptive weights during the exploration phase is:

[0080] in, Indicates the first The iteration of the ... The individual in the first The position of the dimension.

[0081] (7) When satisfied Under certain conditions, CFOA enters the development phase, using a Gaussian distribution to update the population.

[0082] During the exploration phase of fishing, some fish escape without being caught by the fishermen. In the development phase, the fishermen purposefully drive the lost and hidden fish to the same location and surround them. Fishermen in the center catch the schools of fish, while those on the periphery catch the escaped fish, collectively increasing the catch rate. The population position update formula during the development phase is:

[0083]

[0084] in, For the first The result obtained after the update The location of each individual This is the globally optimal solution. To generate a random number from the range {1, 2, 3}, It follows a Gaussian distribution; It is the population variance of the Gaussian distribution.

[0085] (8) After completing the population update, the updated population is subjected to boundary processing to ensure that all solution vectors in the population are within the allowable range. The fitness value of the updated population is calculated, and each fitness value corresponds to a feasible solution in each group of the population. The photovoltaic reactive power output state corresponding to each distributed photovoltaic in each solution is determined according to the voltage threshold of each distributed photovoltaic in each solution, and the corresponding equipment state in the IEEE 33-node network is updated according to the other variables in each solution. After the power flow calculation of the distribution network, the voltage deviation, network loss and voltage limit penalty term corresponding to each solution are obtained, and then the fitness value of each individual is obtained. Finally, the individual with the smallest fitness value in the population is selected as the optimal solution.

[0086] (9) Increment the iteration count by 1 and return to (5).

[0087] After obtaining the optimal solution for the photovoltaic inverter voltage threshold based on the improved CFOA optimization, the voltage threshold of the photovoltaic nodes is then optimized. and Controlling the reactive power output of photovoltaic systems. When the photovoltaic node voltage... satisfy At times, photovoltaic power generation is inactive and produces no output; when... When photovoltaic power absorbs reactive power; when At that time, the photovoltaic system generates reactive power. When the photovoltaic voltage... Exceeding the threshold and At that time, calculate Difference between the threshold and the threshold By combining the Jacobian matrix, the reactive power required for collaborative optimization of each device is calculated.

[0088] When a photovoltaic node voltage exceeds the limit in the distribution network system, voltage coordinated regulation is carried out using distributed photovoltaic, SVG and OLTC taps to avoid the risk of voltage exceeding the limit and ensure the stable operation of the distribution network.

[0089] See Figure 3 This invention provides voltage curves before and after optimization at 12 o'clock under IEEE 33-node 24-hour rolling optimization. 12 o'clock is the peak period for distributed photovoltaic power output during the day, and the voltage fluctuation of the distribution network is relatively large due to the combined effects of load fluctuations and other factors. Figure 3 It can be seen that, based on the photovoltaic inverter voltage threshold setting method provided in the embodiments of the present invention, distributed photovoltaic power generation in conjunction with other reactive power sources can significantly reduce system voltage deviation under extreme operating conditions, and the overall voltage level of the distribution network is significantly improved.

[0090] See Figure 4 This invention provides network loss curves before and after 24-hour rolling optimization of IEEE 33 nodes. Based on the photovoltaic inverter voltage threshold setting method provided by this invention, in addition to the significant improvement of the grid voltage over-limit problem, the overall network loss of the system is also significantly reduced compared to before optimization. The photovoltaic inverter voltage threshold setting method proposed in this invention has significant advantages in both voltage regulation and loss reduction.

[0091] In this embodiment of the invention, a 24-hour rolling optimization mode combined with a multi-objective function containing a voltage limit penalty term can not only adapt to the spatiotemporal mismatch between photovoltaics and loads, but also effectively reduce distribution network losses and voltage deviations, avoiding overcompensation or undercompensation problems in reactive power regulation. The improved fishing optimization algorithm enhances population diversity with the help of Fuch chaotic mapping, and the adaptive weights in the exploration phase improve the optimization accuracy. Combined with multi-device collaborative control and Jacobian matrix-assisted analysis, it can accurately output the voltage threshold of photovoltaic inverters and determine the reactive power output required for collaborative optimization of each device, ensuring the voltage stability and economical operation of the distribution network.

[0092] See Figure 5 This invention provides a photovoltaic inverter voltage threshold setting device 5, comprising: The objective function determination module 51 is used to determine the objective function with the goal of minimizing the total network loss and the total voltage deviation of the distribution network, and introduces a voltage over-limit penalty term. The constraint variable setting module 52 is used to set constraints including power balance, node voltage and equipment status, and to define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output. The threshold solving module 53 is used to solve the objective function according to the constraints using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter. The improved fishing optimization algorithm generates an initial population through Fuch chaotic mapping and determines the adaptive weight of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

[0093] In one possible implementation, the constraint variable setting module 52 is used for power balance of active power, constraining nodes. active power This is equal to the node and all connected nodes. The total amount of active power exchanged between them through the lines; for reactive power power balance, the constraint nodes... reactive power This is equal to the node and all connected nodes. The total amount of reactive power exchanged between the lines; for node voltage, constrain the voltage of each node to be between its minimum and maximum allowable voltage; for equipment status, constrain the apparent power of the photovoltaic inverter to be greater than or equal to 0 and not exceed the rated capacity of the photovoltaic inverter; constrain the tap position of the on-load tap changer to be between the minimum and maximum allowable tap position of the transformer; constrain the absolute value of the reactive power output of the static var generator of each node at any time to be less than or equal to the maximum reactive power output of the static var generator of that node.

[0094] In one possible implementation, the threshold solving module 53 is used to generate an initial population through Fuchs chaotic mapping, calculate the fitness value of individuals in the initial population according to the objective function, and determine the initial optimal solution; divide the exploration phase and the development phase according to the relationship between the number of evaluations and the maximum number of evaluations, wherein the exploration phase controls the population search method through the catch rate parameter, and the development phase determines the population search range through Gaussian distribution; in the exploration phase, based on the current number of evaluations and the maximum number of evaluations, the adaptive weight of the position update algorithm is determined, and an updated population is generated; the updated population is subjected to boundary processing to ensure that the solution vector satisfies the constraints, the fitness value is recalculated and the current optimal solution is updated; the iteration is repeated until the preset maximum number of evaluations is reached, and the optimal solution of each decision variable is output; from the optimal solutions of each decision variable, the optimal voltage threshold of the photovoltaic inverter is extracted.

[0095] In one possible implementation, the threshold solving module 53 is also used to calculate an adaptive weight based on the ratio of the current number of evaluations to the maximum number of evaluations, and the adaptive weight is positively correlated with the ratio; in the position update expression, the search range of the position update algorithm is adjusted by the adaptive weight to generate the updated population.

[0096] In this embodiment of the invention, the photovoltaic inverter voltage threshold setting device 5, through multi-module collaboration, takes into account both distribution network loss and voltage deviation optimization and strengthens voltage over-limit constraints. At the same time, relying on the improved fishing optimization algorithm to accurately solve the problem, it ensures the reliability of the optimal voltage threshold of the photovoltaic inverter. Its strict control over the constraints and the adaptive optimization design of the algorithm improve the scientificity and adaptability of the threshold setting.

[0097] See Figure 6 The diagram shows a schematic of the electronic device 6 provided in an embodiment of the present invention, which is described in detail below: like Figure 6 As shown, the electronic device 6 of this embodiment includes a processor 60 and a memory 61. The memory 61 stores a computer program 62. When the processor 60 executes the computer program 62, it implements the steps in the various method embodiments described above. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module in the various device embodiments described above.

[0098] For example, computer program 62 may be divided into one or more modules / units, which are stored in memory 61 and executed by processor 60 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in electronic device 6.

[0099] Electronic device 6 may include, but is not limited to, processor 60 and memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 6 may also include input / output devices, network access devices, buses, etc.

[0100] The processor 60 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.

[0101] The memory 61 can be an internal storage unit of the electronic device 6, such as a hard disk or RAM. The memory 61 can also be an external storage device of the electronic device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 61 can include both internal and external storage units of the electronic device 6. The memory 61 is used to store the computer program 62 and other programs and data required by the electronic device 6. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0102] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0103] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0104] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0105] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying 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.

[0106] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0107] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for setting a voltage threshold for a photovoltaic inverter, characterized in that, include: The objective function is determined by minimizing the total network loss and the total voltage deviation of the distribution network, and by introducing a voltage over-limit penalty term. Set constraints including power balance, node voltage and equipment status, and define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output; Based on the constraints, the objective function is solved using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter. The improved fishing optimization algorithm generates an initial population through Fuchs chaotic mapping and determines the adaptive weights of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

2. The photovoltaic inverter voltage threshold setting method according to claim 1, characterized in that, The objective function includes: in, For the total network loss of the distribution network, The total voltage deviation of the distribution network. Penalties for voltage constraints at each node, and These are the preset network loss weight and voltage deviation weight, respectively. This is the voltage over-limit penalty coefficient. This represents the total number of nodes in the distribution network.

3. The photovoltaic inverter voltage threshold setting method according to claim 1 or 2, characterized in that, The settings include constraints on power balance, node voltage, and device status, including: For active power power balance, constrained nodes active power This is equal to the node and all connected nodes. The total amount of active power exchanged between them through the lines; For reactive power power balance, constraint nodes reactive power This is equal to the node and all connected nodes. The total amount of reactive power exchanged between them through the lines; For node voltages, constrain the voltage of each node to be between its allowed minimum and maximum voltages; For equipment status, the apparent power of the photovoltaic inverter is constrained to be greater than or equal to 0 and not exceed the rated capacity of the photovoltaic inverter; the tap position of the on-load tap-changing transformer is constrained to be between the minimum and maximum allowable tap positions of the transformer; the absolute value of the reactive power output of the static var generator at any node at any time is constrained to be less than or equal to the maximum reactive power output of the static var generator at that node.

4. The photovoltaic inverter voltage threshold setting method according to claim 1 or 2, characterized in that, The step of solving the objective function using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each photovoltaic inverter includes: An initial population is generated using Fuch chaotic mapping, and the fitness values ​​of individuals in the initial population are calculated according to the objective function to determine the initial optimal solution. The exploration phase and the development phase are divided according to the relationship between the number of evaluations and the maximum number of evaluations. In the exploration phase, the population search method is controlled by the catch rate parameter, and in the development phase, the population search range is determined by the Gaussian distribution. In the exploration phase, the adaptive weight of the position update algorithm is determined based on the current number of evaluations and the maximum number of evaluations, and the updated population is generated. The updated population is subjected to boundary processing to ensure that the solution vector satisfies the constraints, the fitness value is recalculated and the current optimal solution is updated. Repeat the iteration until the preset maximum number of evaluations is reached, and output the optimal solution for each decision variable; The optimal voltage threshold of the photovoltaic inverter is extracted from the optimal solutions of each decision variable.

5. The photovoltaic inverter voltage threshold setting method according to claim 4, characterized in that, In the exploration phase, based on the current number of evaluations and the maximum number of evaluations, an adaptive weight for the position update algorithm is determined, and an updated population is generated, including: The adaptive weight is calculated based on the ratio of the current number of evaluations to the maximum number of evaluations, and the adaptive weight is positively correlated with the ratio. The search range of the position update algorithm is adjusted by the adaptive weights to generate the updated population.

6. The photovoltaic inverter voltage threshold setting method according to any one of claims 1 or 2, characterized in that, The optimal voltage threshold for each of the photovoltaic inverters includes the minimum value. and maximum value ; After solving the objective function using an improved fishing optimization algorithm based on the constraints to obtain the optimal voltage threshold for each photovoltaic inverter, the method further includes: When the node voltage of the photovoltaic inverter satisfy When the photovoltaic inverter does not generate reactive power, it does not generate reactive power. When the photovoltaic inverter absorbs reactive power; when At that time, the photovoltaic inverter generates reactive power.

7. The photovoltaic inverter voltage threshold setting method according to claim 6, characterized in that, After solving the objective function using an improved fishing optimization algorithm based on the constraints to obtain the optimal voltage threshold for each photovoltaic inverter, the method further includes: when or At that time, calculate respectively with and The difference is used to determine the reactive power required for coordinated optimization of various devices in the distribution network, combined with the Jacobian matrix.

8. A photovoltaic inverter voltage threshold setting device, characterized in that, include: The objective function determination module is used to determine the objective function with the objectives of minimizing the total network loss and the total voltage deviation of the distribution network, and introduces a voltage over-limit penalty term. The constraint variable setting module is used to set constraints including power balance, node voltage and equipment status, and to define decision variables including voltage regulation equipment level, photovoltaic inverter voltage threshold and reactive power output. The threshold solving module is used to solve the objective function according to the constraints using an improved fishing optimization algorithm to obtain the optimal voltage threshold for each of the photovoltaic inverters; wherein, the improved fishing optimization algorithm generates an initial population through Fuch chaotic mapping, and determines the adaptive weight of its position update algorithm based on the current number of evaluations and the maximum number of evaluations during the exploration phase.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method 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 by a processor, implements the method as described in any one of claims 1 to 7.

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