Reactive power optimization method and device for power distribution network, computer device and storage medium

Abstract

The application relates to a reactive power optimization method and device of a power distribution network, computer equipment, a storage medium and a computer program product. The method comprises the following steps: screening a plurality of second reactive power optimization information from a plurality of first reactive power optimization information based on a first reactive power optimization model and a plurality of power flow indexes; screening a plurality of third reactive power optimization information from the plurality of second reactive power optimization information based on a second reactive power optimization model and a reactive power optimization cost index; taking the plurality of third reactive power optimization information as new first reactive power optimization information, and returning to the step of screening the plurality of second reactive power optimization information from the plurality of first reactive power optimization information based on the first reactive power optimization model and a plurality of preset power flow indexes until the screened reactive power optimization information meets a preset screening condition; and performing reactive power optimization on the power distribution network according to the reactive power optimization information meeting the preset screening condition. The method can improve the operation stability of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a reactive power optimization method, apparatus, computer equipment, storage medium and computer program product for distribution networks. Background Technology

[0002] Reactive power optimization of distribution networks refers to the process of adjusting control variables to achieve optimal performance indicators for a distribution network with given topology parameters and load, while meeting various constraints for stable operation of the distribution network. Reactive power optimization is crucial to the operational stability of the distribution network.

[0003] In related technologies, most reactive power optimization for distribution networks is achieved by solving for the optimal value of the distribution network under a single objective, or by converting multiple objectives into a single objective through weighted summation, and then solving for the optimal value of the distribution network under the single objective.

[0004] However, the distribution network is developing rapidly and its scale is gradually expanding, and the load situation in the distribution network is becoming more and more complex. On the one hand, it is difficult to fully consider the operation requirements of the distribution network with a single objective. On the other hand, it is difficult to accurately determine the weight coefficients of different objectives in the weighted summation process. Therefore, reactive power optimization methods based on related technologies result in low operation stability of the distribution network. Summary of the Invention

[0005] Therefore, it is necessary to provide a reactive power optimization method, device, computer equipment, computer-readable storage medium, and computer program product for the distribution network that can improve the operational stability of the aforementioned distribution network, addressing the technical problem of low operational stability.

[0006] Firstly, this application provides a reactive power optimization method for a distribution network, including:

[0007] Based on a pre-established first reactive power optimization model and multiple preset power flow indicators, multiple second reactive power optimization information is selected from multiple first reactive power optimization information.

[0008] Based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, multiple third reactive power optimization information is selected from the multiple second reactive power optimization information.

[0009] The multiple third reactive power optimization information is used as multiple new first reactive power optimization information, and the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators is returned until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0010] The power distribution network is optimized based on the reactive power optimization information that meets the preset screening conditions.

[0011] In one embodiment, the step of filtering out multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators includes:

[0012] Based on the first reactive power optimization model, the disadvantage information of each first reactive power optimization information under each power flow index is determined; the disadvantage information is used to characterize the index value of each first reactive power optimization information corresponding to each power flow index, and the degree of disadvantage among all the index values ​​of the first reactive power optimization information corresponding to each power flow index.

[0013] By integrating the disadvantage information of each first reactive power optimization information under various power flow indicators, a comprehensive disadvantage information of each first reactive power optimization information is obtained;

[0014] From the plurality of first reactive power optimization information, each first reactive power optimization information whose corresponding comprehensive disadvantage information satisfies the preset disadvantage condition is selected as the plurality of second reactive power optimization information.

[0015] In one embodiment, determining the disadvantage information of each first reactive power optimization information under each power flow index based on the first reactive power optimization model includes:

[0016] For each power flow index, the power flow index value of each first reactive power optimization information under the power flow index is determined based on the first reactive power optimization model;

[0017] For the target reactive power optimization information among the plurality of first reactive power optimization information, each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information is determined as the inferior optimization information corresponding to the target reactive power optimization information; the target reactive power optimization information is any one of the plurality of first reactive power optimization information.

[0018] Based on the disadvantage optimization information corresponding to the target reactive power optimization information and the disadvantage optimization information corresponding to the other reactive power optimization information among the multiple first reactive power optimization information, the disadvantage information of the target reactive power optimization information under the power flow index is obtained.

[0019] In one embodiment, the plurality of preset power flow indicators include system network loss indicators and voltage offset indicators;

[0020] The step of determining the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model for each power flow index includes:

[0021] Obtain the power flow data of the power distribution network;

[0022] Using the first reactive power optimization model and the power flow data, the voltage change information of each grid node in the distribution network, the conductivity information between each grid node, and the voltage difference information between each grid node are determined.

[0023] Based on the voltage change information, the conductivity information, and the voltage difference information, the system network loss index value of each first reactive power optimization information under the system network loss index is determined, and based on the voltage change information, the voltage offset index value of each first reactive power optimization information under the voltage offset index is determined.

[0024] In one embodiment, the reactive power optimization cost index is the reactive power compensation cost index;

[0025] Based on a pre-established second reactive power optimization model and a preset reactive power optimization cost index, multiple third reactive power optimization information are selected from the multiple second reactive power optimization information, including:

[0026] Based on the second reactive power optimization model, determine the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index;

[0027] From the plurality of second reactive power optimization information, each second reactive power optimization information whose corresponding reactive power compensation cost index value meets the preset reactive power compensation cost condition is selected as the plurality of third reactive power optimization information.

[0028] In one embodiment, determining the reactive power compensation cost index value for each piece of second reactive power optimization information under the reactive power compensation cost index based on the second reactive power optimization model includes:

[0029] Obtain the power flow data of the distribution network and the amount of resources spent by the distribution network in reactive power compensation;

[0030] Using the second reactive power optimization model and the power flow data, the output information of each power grid node in the distribution network after reactive power compensation and the reactive power compensation capacity of the distribution network after reactive power compensation are determined.

[0031] Based on the power output information of each power grid node after reactive power compensation, the reactive power compensation capacity of the distribution network in reactive power compensation, and the amount of resources spent by the distribution network in reactive power compensation, the reactive power compensation cost index value under the reactive power compensation cost index corresponding to each second reactive power optimization information is determined.

[0032] Secondly, this application also provides a reactive power optimization device for a power distribution network, comprising:

[0033] The first filtering module is used to filter out multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators.

[0034] The second filtering module is used to filter out multiple third reactive power optimization information from the multiple second reactive power optimization information based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index.

[0035] The filtering iteration module is used to take the multiple third reactive power optimization information as multiple new first reactive power optimization information and return the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0036] The reactive power optimization module is used to optimize the power distribution network based on the reactive power optimization information that meets the preset screening conditions.

[0037] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0038] Based on a pre-established first reactive power optimization model and multiple preset power flow indicators, multiple second reactive power optimization information is selected from multiple first reactive power optimization information.

[0039] Based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, multiple third reactive power optimization information is selected from the multiple second reactive power optimization information.

[0040] The multiple third reactive power optimization information is used as multiple new first reactive power optimization information, and the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators is returned until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0041] The power distribution network is optimized based on the reactive power optimization information that meets the preset screening conditions.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0043] Based on a pre-established first reactive power optimization model and multiple preset power flow indicators, multiple second reactive power optimization information is selected from multiple first reactive power optimization information.

[0044] Based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, multiple third reactive power optimization information is selected from the multiple second reactive power optimization information.

[0045] The multiple third reactive power optimization information is used as multiple new first reactive power optimization information, and the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators is returned until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0046] The power distribution network is optimized based on the reactive power optimization information that meets the preset screening conditions.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] Based on a pre-established first reactive power optimization model and multiple preset power flow indicators, multiple second reactive power optimization information is selected from multiple first reactive power optimization information.

[0049] Based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, multiple third reactive power optimization information is selected from the multiple second reactive power optimization information.

[0050] The multiple third reactive power optimization information is used as multiple new first reactive power optimization information, and the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators is returned until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0051] The power distribution network is optimized based on the reactive power optimization information that meets the preset screening conditions.

[0052] The aforementioned reactive power optimization method, device, computer equipment, storage medium, and computer program product for the distribution network firstly selects multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators; then, based on the pre-established second reactive power optimization model and preset reactive power optimization cost indicators, selects multiple third reactive power optimization information from multiple second reactive power optimization information; next, the multiple third reactive power optimization information is used as new multiple first reactive power optimization information, and the process returns to the step of selecting multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information selected based on the first or second reactive power optimization model meets preset screening conditions; finally, reactive power optimization is performed on the distribution network based on the reactive power optimization information that meets the preset screening conditions. In this way, by using the first reactive power optimization model and power flow indicators, reactive power optimization information can be filtered from the power flow data dimension. By using the second optimization model and reactive power optimization cost indicators, reactive power optimization information can also be filtered from the economic cost dimension. Therefore, the operational needs of the distribution network in various aspects can be considered in the reactive power optimization process. In addition, by iteratively filtering reactive power optimization information from the power flow data dimension and the economic cost dimension, the operational needs of the distribution network in various aspects can be coupled, thereby comprehensively considering the operational needs of the distribution network in the reactive power optimization process. Therefore, the reactive power optimization information obtained based on the above process improves the operational stability of the distribution network. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a reactive power optimization method for a distribution network in one embodiment.

[0055] Figure 2 This is a flowchart illustrating the steps of filtering out multiple second reactive power optimization information from multiple first reactive power optimization information in one embodiment.

[0056] Figure 3 This is a flowchart illustrating the steps of determining the disadvantage information of each first reactive power optimization information under each power flow index based on a first reactive power optimization model in one embodiment.

[0057] Figure 4 This is a flowchart illustrating a reactive power optimization method for a distribution network in another embodiment;

[0058] Figure 5 This is a flowchart illustrating a reactive power optimization method for a distribution network based on a two-layer reactive power optimization model in one embodiment.

[0059] Figure 6 This is a structural block diagram of a reactive power optimization device for a power distribution network in one embodiment;

[0060] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0063] In one exemplary embodiment, such as Figure 1 As shown, a reactive power optimization method for a power distribution network is provided. This embodiment illustrates the method by applying it to a server. It is understood that this method can also be applied to terminals, and to systems including both servers and terminals, and is implemented through interaction between the server and terminals. The server can be a standalone server or a server cluster composed of multiple servers. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. In this embodiment, the method includes the following steps:

[0064] Step S102: Based on the pre-established first reactive power optimization model and multiple preset power flow indicators, select multiple second reactive power optimization information from multiple first reactive power optimization information.

[0065] Among them, the pre-established first reactive power optimization model is a multi-objective optimization model, and each objective function in the first reactive power optimization model corresponds to a preset power flow index.

[0066] Among them, power flow indicators are used to characterize various indicators of power flow data in the distribution network, such as system network loss indicators, voltage deviation indicators, reactive power indicators, and active power indicators.

[0067] Among them, reactive power optimization information refers to the reactive power optimization scheme of the distribution network, such as the nodes to be optimized and the reactive power compensation amount of the nodes to be optimized.

[0068] Among them, the first reactive power optimization model is used to determine the quality of each first reactive power optimization information in the power flow data dimension; it can be understood that the quality is used to characterize the numerical relationship, the larger the value, the better, and the smaller the value, the worse.

[0069] Specifically, for each piece of first reactive power optimization information, the server determines the target value of the first reactive power optimization information under the objective function corresponding to each preset power flow index based on the pre-established first reactive power optimization model, and determines the degree of merit of the first reactive power optimization information under the power flow index corresponding to the target value based on the target value. Then, the degree of merit of the first reactive power optimization information under each power flow index is integrated to obtain the comprehensive degree of merit of the first reactive power optimization information in the power flow data dimension. Finally, the server selects the reactive power optimization information with the best (or better) comprehensive degree of merit from each piece of first reactive power optimization information to obtain multiple pieces of second reactive power optimization information.

[0070] It is understandable that the objective function can be optimal in terms of maximizing or minimizing; when all objective functions are optimal in terms of maximizing, the overall quality is determined by the best (the best of the best) among the best and worst; when all objective functions are optimal in terms of minimizing, the overall quality is determined by the worst; when the objective functions include both those that are optimal in terms of maximizing and those that are optimal in terms of minimizing, the optimal standard for overall quality is determined by ranking the importance of each objective function.

[0071] For example, taking preset power flow indicators including system network loss indicators and voltage deviation indicators as an example, since the system network loss indicator and the voltage deviation indicator are both optimal, the server first calculates the system network loss indicator value and the voltage deviation indicator value for each first reactive power optimization information under the system network loss indicator and the voltage deviation indicator, respectively. Then, based on the corresponding system network loss indicator value and the corresponding voltage deviation indicator value, the server evaluates the merits of each first reactive power optimization information under the system network loss indicator and the voltage deviation indicator, and combines the merits of each merits under the system network loss indicator and the voltage deviation indicator to obtain the comprehensive merits of each first reactive power optimization information. Since both the system network loss indicator and the voltage deviation indicator are optimal, the server filters out the reactive power optimization information with the worst (or relatively bad) comprehensive merits from the multiple first reactive power optimization information information to obtain multiple second reactive power optimization information.

[0072] Furthermore, the first reactive power optimization model uses the improved NSGA2 algorithm (Non-dominated Sorting Genetic Algorithm II) to filter multiple first reactive power optimization information. Compared with the traditional NSGA2 algorithm, the improvement of the improved NSGA2 algorithm is that after merging the parent and child populations and calculating the crowding distance, the population is pruned, and the number of individuals allowed to be retained in the first front end is calculated according to the set optimal front end individual coefficient, so as to more accurately filter the optimal solution. The solution process is as follows: (1) Generate the initial population; (2) Select crossover mutation to generate the child population; (3) Merge the parent and child populations; (4) Non-dominated sorting and calculate the crowding distance; (5) Prune the population; (6) Determine the termination condition. Because the improved NSGA2 algorithm prunes the population based on the non-dominated ordering and crowding distance between individuals (i.e., each first reactive power optimization information), and the traditional NSGA2 algorithm also generates a subpopulation based on the non-dominated ordering and crowding distance between individuals, the screening result of the first reactive power optimization model is related to the relationship between each reactive power optimization information input to the first reactive power optimization model (the non-dominated ordering and crowding distance between reactive power optimization information). For example, for reactive power optimization information 1, 2, 3, 4, 5, 6, 7, 8, when the first reactive power optimization model performs the first screening of the above 8 reactive power optimization information, it is based on the non-dominated ordering and crowding distance between the 8 reactive power optimization information. If the filtered result is reactive power optimization information 1, 3, 5, 7, then when the first reactive power optimization model performs the second screening of the above 4 reactive power optimization information, it is based on the non-dominated ordering and crowding distance between the 4 reactive power optimization information, and its screening result will be different from the first screening result.

[0073] Step S104: Based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, select multiple third reactive power optimization information from multiple second reactive power optimization information.

[0074] Among them, the pre-established second reactive power optimization model is a single-objective optimization model, and the objective function in the second reactive power optimization model corresponds to the preset reactive power optimization cost index.

[0075] Among them, the reactive power optimization cost index is used to characterize the economic cost of the distribution network in the reactive power optimization process, such as the investment cost of reactive power compensation equipment per unit capacity of the distribution network, the annual maintenance cost of reactive power compensation equipment per unit capacity of the distribution network, the network loss price of the distribution network, and the equivalent annual coefficient of the distribution network.

[0076] Among them, the second reactive power optimization model is used to determine the merits of each second reactive power optimization information under the economic cost of the reactive power optimization process.

[0077] Specifically, for each second reactive power optimization information, the server determines the target value of the second reactive power optimization information under the objective function corresponding to the preset reactive power optimization cost index based on the pre-established second reactive power optimization model, and determines the degree of merit of the second reactive power optimization information under the reactive power optimization cost index based on the target value. Finally, the server selects each reactive power optimization information with the best (or better) degree of merit under the reactive power optimization cost index from each second reactive power optimization information, thus obtaining multiple third reactive power optimization information.

[0078] It is understandable that the minimum reactive power optimization cost index is considered optimal. For example, since the minimum reactive power optimization cost index is considered optimal, the server first calculates the reactive power optimization cost index value of each second reactive power optimization information under the reactive power optimization cost index. Then, based on the corresponding reactive power optimization cost index value, it evaluates the merits of each second reactive power optimization information under the reactive power optimization cost index. Finally, the server selects the reactive power optimization information with the worst (or relatively poor) merits under the reactive power optimization cost index from among the multiple second reactive power optimization information, thus obtaining multiple third reactive power optimization information.

[0079] Furthermore, the second reactive power optimization model uses single-objective optimization algorithms such as genetic algorithms to filter multiple reactive power optimization information. Similar to the first reactive power optimization model, the second reactive power optimization model filters individuals based on the relationships between individuals (such as fitness in genetic algorithms, where the fitness of an individual is used to characterize the degree of survival advantage of an individual in the population environment). Therefore, the filtering result of the second reactive power optimization model is related to the relationships between the various reactive power optimization information input into the second reactive power optimization model (the fitness of the reactive power optimization information).

[0080] Step S106 involves taking multiple third reactive power optimization information as multiple new first reactive power optimization information, and returning the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0081] The preset screening conditions refer to either reaching a preset number of iterations for the reactive power optimization information or the selected reactive power optimization information reaching a convergence condition. It is understandable that one screening of reactive power optimization information refers to a screening performed by either the first reactive power optimization model or the second reactive power optimization model. Therefore, reactive power optimization information that meets the preset screening conditions can be selected by either the first or the second reactive power optimization model.

[0082] Although the reactive power optimization process of the distribution network needs to comprehensively consider the operational needs of various aspects of the distribution network and establish multiple objectives, in combination with the actual production of the power grid, safety, stability and reliability must be the top priority. Therefore, the priority of the operational needs corresponding to the power flow data should be higher than the priority of the operational needs corresponding to the economic cost. Thus, the first reactive power optimization model and the second reactive power optimization model can be regarded as a two-layer model, in which the first reactive power optimization model is the upper-layer model and the second reactive power optimization model is the lower-layer model.

[0083] The server first inputs multiple first reactive power optimization information into a first reactive power optimization model. Based on the first reactive power optimization model, it filters out multiple second reactive power optimization information that are optimal (or relatively optimal) in the power flow data dimension. Then, it inputs multiple second reactive power optimization information into a second reactive power optimization model. Based on the second reactive power optimization model, it filters out multiple third reactive power optimization information that are optimal (or relatively optimal) in the economic cost dimension. Next, the server inputs multiple third reactive power optimization information into a first reactive power optimization model. Based on the first reactive power optimization model, it filters out multiple fourth reactive power optimization information that are optimal (or relatively optimal) in the power flow data dimension. Then, the server inputs multiple fourth reactive power optimization information into a second reactive power optimization model. Based on the second reactive power optimization model, it filters out multiple fifth reactive power optimization information that are optimal (or relatively optimal) in the power flow data dimension... This iterative filtering continues until the reactive power optimization information filtered out by the first or second reactive power optimization model meets the preset filtering conditions.

[0084] It is understandable that, since the selection of the first and second reactive power optimization models is based on the relationship between the reactive power optimization information input this time, even if it is the same reactive power optimization information, the degree of superiority or inferiority obtained in each selection process of the first or second reactive power optimization model is different.

[0085] Step S108: Based on the reactive power optimization information that meets the preset screening conditions, perform reactive power optimization on the distribution network.

[0086] Specifically, after obtaining reactive power optimization information that meets the preset screening conditions, the server performs corresponding reactive power optimization processing on the distribution network based on the reactive power optimization information.

[0087] It is understandable that if there are multiple reactive power optimization information that meet the preset screening conditions, the corresponding reactive power optimization information can be selected from the multiple reactive power optimization information according to the actual needs of the distribution network.

[0088] In the aforementioned reactive power optimization method for the distribution network, the server first selects multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators; then, based on the pre-established second reactive power optimization model and preset reactive power optimization cost indicators, it selects multiple third reactive power optimization information from multiple second reactive power optimization information; next, it uses the multiple third reactive power optimization information as new multiple first reactive power optimization information, and returns to the step of selecting multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information selected based on the first or second reactive power optimization model meets the preset selection conditions; finally, reactive power optimization is performed on the distribution network based on the reactive power optimization information that meets the preset selection conditions. In this way, the server can filter reactive power optimization information from the perspective of power flow data through the first reactive power optimization model and power flow indicators, and also filter it from the perspective of economic cost through the second optimization model and reactive power optimization cost indicators. Therefore, it can consider the operational needs of the distribution network from multiple aspects during the reactive power optimization process. In addition, by iteratively filtering reactive power optimization information from the perspectives of power flow data and economic cost, the server can also couple the operational needs of the distribution network from various aspects, thus comprehensively considering the operational needs of the distribution network from all aspects during the reactive power optimization process. Therefore, the reactive power optimization information obtained based on the above process improves the operational stability of the distribution network.

[0089] like Figure 2 As shown, in an exemplary embodiment, step S102, based on a pre-established first reactive power optimization model and multiple preset power flow indicators, selects multiple second reactive power optimization information from multiple first reactive power optimization information, specifically including the following steps:

[0090] Step S202: Based on the first reactive power optimization model, determine the disadvantage information of each first reactive power optimization information under each power flow index.

[0091] Step S204: Integrate the disadvantage information of each first reactive power optimization information under various power flow indicators to obtain the comprehensive disadvantage information of each first reactive power optimization information.

[0092] Step S206: From multiple first reactive power optimization information, select each first reactive power optimization information whose corresponding comprehensive disadvantage information meets the preset disadvantage conditions, and use it as multiple second reactive power optimization information.

[0093] Among them, the disadvantage information is used to characterize the index value of each first reactive power optimization information corresponding to each power flow index, and the degree of disadvantage among all the index values ​​of the first reactive power optimization information corresponding to each power flow index.

[0094] Among them, the comprehensive disadvantage information is used to characterize the overall degree of disadvantage of each first reactive power optimization information under all power flow indicators.

[0095] Among them, the preset disadvantage condition is a preset threshold for comprehensive disadvantage information.

[0096] Furthermore, the server can use rough set theory to determine the comprehensive disadvantage information of each first reactive power optimization information. In rough set theory, the superiority or inferiority relationship between each element and the objective function (i.e., the function value of each element under the objective function) can be determined based on the mapping relationship between each element and the objective function. Specifically, for each element in the element set, the remaining elements whose corresponding function values ​​in the element set are greater than or equal to that element form the element's superiority class, and the remaining elements whose corresponding function values ​​in the element set are less than or equal to that element form the element's inferiority class. The degree to which the element is superior to or inferior to each of the remaining elements can be calculated by the inclusion degree. By weighted fusion of the various inclusion degrees corresponding to the element, the degree of superiority or inferiority of the element under the objective function can be obtained.

[0097] Specifically, the first reactive power optimization model includes an objective function corresponding to each power flow index. Based on the first reactive power optimization model, the server first determines the function value of each first reactive power optimization information under the objective function corresponding to each power flow index. For each power flow index, based on the function value of each first reactive power optimization information under the objective function corresponding to that index, the server can obtain the inferiority class of each first reactive power optimization information under that index. This allows the server to calculate the degree to which each first reactive power optimization information is inferior to the other first reactive power optimization information under that index (i.e., the inclusion degree in rough set theory). Then, for... For each first reactive power optimization piece of information, the server weighted and fused the degree to which this first reactive power optimization piece of information was inferior to all other first reactive power optimization pieces of information, to obtain the disadvantage information of the first reactive power optimization piece of information under the power flow index (i.e., the degree of disadvantage in rough set theory). Then, the server fused the disadvantage information of each first reactive power optimization piece of information under various power flow indices to obtain the comprehensive disadvantage information of each first reactive power optimization piece of information under the power flow data dimension. Finally, the server selected each first reactive power optimization piece of information whose comprehensive disadvantage information was greater than or equal to a preset threshold from the multiple first reactive power optimization pieces of information, and used them as multiple second reactive power optimization pieces of information.

[0098] It is understandable that, since the first reactive power optimization model takes the worst as the best, the reactive power optimization information with a greater degree of disadvantage (the greater the comprehensive disadvantage information) is more likely to meet the screening objective of the first reactive power optimization model.

[0099] For example, suppose we have the first reactive power optimization information. In the first reactive power optimization model, the objective function corresponding to each power flow index is: .

[0100] With objective function For example, the server first calculates the first reactive power optimization information. In the objective function function value below And based on function values Determine the first reactive power optimization information Disadvantages .

[0101] Next, the first reactive power optimization information is used. For example, the server optimizes the first reactive power information. Disadvantages And the rest of the first reactive power optimization information Disadvantages Calculate the first reactive power optimization information respectively. Inferior to all other first reactive power optimization information Degree information .

[0102] Next, the server performs weighted fusion of the first reactive power optimization information. Inferior to all other first reactive power optimization information Degree information The first reactive power optimization information is obtained. In the objective function The following disadvantageous information .

[0103] Finally, the server integrates the first reactive power optimization information. The first reactive power optimization information can be obtained by analyzing the disadvantage information under each objective function. Comprehensive disadvantage information in the dimension of trend data .

[0104] In this embodiment, the server can determine the degree of inferiority of each first reactive power optimization information under each power flow index by using the information on how inferior each first reactive power optimization information is to the other first reactive power optimization information under each power flow index. This allows the server to obtain the comprehensive degree of inferiority of each first reactive power optimization information under the power flow data dimension composed of various power flow indices. As a result, the server can select the most inferior (or relatively inferior) second reactive power optimization information from multiple first reactive power optimization information under the power flow data dimension. Therefore, the server can consider the operational needs of the distribution network in the power flow data dimension during the reactive power optimization process, thereby improving the operational stability of the distribution network.

[0105] like Figure 3 As shown, in an exemplary embodiment, step S202 above, based on the first reactive power optimization model, determines the disadvantage information of each first reactive power optimization information under each power flow index, specifically including the following steps:

[0106] Step S302: For each power flow index, determine the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model.

[0107] Step S304: For the target reactive power optimization information among the multiple first reactive power optimization information, determine each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information, and use it as the inferior optimization information corresponding to the target reactive power optimization information.

[0108] Step S306: Based on the disadvantage optimization information corresponding to the target reactive power optimization information and the disadvantage optimization information corresponding to the other reactive power optimization information among the multiple first reactive power optimization information, obtain the disadvantage information of the target reactive power optimization information under the power flow index.

[0109] The target reactive power optimization information is any one of the multiple first reactive power optimization information. The remaining reactive power optimization information is any one of the multiple first reactive power optimization information other than the target reactive power optimization information.

[0110] The first reactive power optimization model calculates the function value of each first reactive power optimization information under each objective function through the objective function corresponding to each power flow index, and uses the function value under each objective function as the index value under each objective function corresponding to the power flow index.

[0111] Among them, the disadvantage optimization information is the disadvantage class mentioned above.

[0112] Specifically, for each power flow index, the server determines the power flow index value of each piece of first reactive power optimization information under that power flow index, based on the objective function corresponding to that power flow index in the first reactive power optimization model; for example, through the objective function... Calculate the first reactive power optimization information In the objective function The corresponding trend indicator value .

[0113] Then, for each target reactive power optimization information among multiple first reactive power optimization information, the server determines the first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information, and uses these as the inferior optimization information corresponding to the target reactive power optimization information; for example, using the objective function... For example, regarding the target reactive power optimization information Determine target reactive power optimization information In the objective function Disadvantages under corresponding trend indicators .

[0114] Next, based on the inferior optimization information corresponding to the target reactive power optimization information and the inferior optimization information corresponding to each of the other reactive power optimization information, the server obtains information on the degree to which the target reactive power optimization information is inferior to each of the other reactive power optimization information; for example, using the objective function... For example, the server obtains the target reactive power optimization information using the following formula 1. In the objective function Under the corresponding power flow indicators, it is inferior to each of the first reactive power optimization information. Degree information :

[0115] (Formula 1)

[0116] in, This indicates the number of multiple first reactive power optimization information entries, and ~ indicates the negation operation. This represents the number of elements in the set, i.e. express and The number of elements in the antiset of the union of the sets.

[0117] Then, the server weightedly integrates the degree to which the target reactive power optimization information is inferior to other reactive power optimization information to obtain the disadvantage information of the target reactive power optimization information under the power flow index; for example, using the objective function For example, the server obtains the first reactive power optimization information using the following formula 2. In the objective function Disadvantage information under corresponding trend indicators .

[0118] (Formula 2)

[0119] In this embodiment, for each power flow index, the server can determine the disadvantage class of each first reactive power optimization information under the power flow index by calculating the index value of each first reactive power optimization information under the power flow index, thereby obtaining the disadvantage information of each first reactive power optimization information under the power flow index.

[0120] In one exemplary embodiment, a plurality of preset power flow indicators include system network loss indicators and voltage offset indicators.

[0121] Step S302 above, for each power flow index, determines the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model. Specifically, it includes the following: acquiring power flow data of the distribution network; determining the voltage change information of each grid node in the distribution network, the conductivity information between each grid node, and the voltage difference information between each grid node through the first reactive power optimization model and the power flow data; determining the system network loss index value of each first reactive power optimization information under the system network loss index based on the voltage change information, conductivity information, and voltage difference information; and determining the voltage deviation index value of each first reactive power optimization information under the voltage deviation index based on the voltage change information.

[0122] Among them, voltage change information can be characterized by the voltage amplitude of the grid nodes, conductivity information can be characterized by the conductance between grid nodes, and voltage difference information can be characterized by the voltage phase angle difference between grid nodes.

[0123] Specifically, the server first obtains the power flow data of the distribution network, and then calls the second reactive power optimization model to calculate the voltage amplitude of each grid node in the distribution network, the conductance between each grid node, and the voltage phase angle difference between each grid node based on the power flow data.

[0124] Then, based on the voltage amplitude of each grid node, the conductance between each grid node, and the voltage phase angle difference between each grid node, the server calculates the system network loss index value of each piece of first reactive power optimization information under the system network loss index through the objective function corresponding to the system network loss index in the first reactive power optimization model, i.e., the following formula 3:

[0125] (Formula 3)

[0126] in, This represents the system network loss index value under the system network loss index; This represents the set of transmission lines in a power distribution network. and These represent the node identifiers of the power grid nodes on the transmission line; Represents a power grid node and power grid nodes The electrical conductance between them; and Representing the power grid nodes and power grid nodes The voltage amplitude; Represents a power grid node and power grid nodes The voltage phase angle difference between them.

[0127] Furthermore, based on the voltage amplitude, the server calculates the voltage offset index value of each piece of first reactive power optimization information under the voltage offset index through the objective function corresponding to the voltage offset index in the first reactive power optimization model, i.e., the following formula 4:

[0128] (Formula 4)

[0129] in, This indicates the voltage offset index value under the voltage offset index; This represents the set of PQ nodes in a distribution network (P represents active power, Q represents reactive power; PQ nodes are nodes with known active and reactive power). These represent the node identifiers of the power grid nodes in the PQ node; , , , Representing the power grid nodes The voltage amplitude, voltage reference value, voltage upper limit value, and voltage lower limit value.

[0130] In addition, the first reactive power optimization model also includes the following constraints; under the constraints of each constraint, the first reactive power optimization model selects multiple second reactive power optimization information from multiple first reactive power optimization information:

[0131] (Formula 5)

[0132] (Formula 6)

[0133] in, This represents the number of power grid nodes in the distribution network; The node identifier represents a power grid node in the distribution network; This represents the set of PV nodes in a distribution network (P represents active power, V represents node voltage; PV nodes are nodes with known active power and node voltage). These represent the node identifiers of the grid nodes in the parallel set of PQ nodes and PV nodes, respectively. Represents a power grid node The amount of active power injected; Represents a power grid node The voltage amplitude; Represents a power grid node The voltage amplitude; Represents a power grid node and power grid nodes The voltage phase angle difference between them; The admittance matrix represents the first... line, number The imaginary part of the column elements.

[0134] In addition, the first reactive power optimization model also includes upper and lower constraints on the various power flow physical quantities of each power grid node.

[0135] In this embodiment, the server can calculate the system network loss index value and voltage offset index value corresponding to each first reactive power optimization information by using the objective function corresponding to the system network loss index and the objective function corresponding to the voltage offset index in the first reactive power optimization model.

[0136] In an exemplary embodiment, the reactive power optimization cost index is the reactive power compensation cost index.

[0137] Step S104 above, based on the pre-established second reactive power optimization model and the preset reactive power optimization cost index, selects multiple third reactive power optimization information from multiple second reactive power optimization information, specifically including the following: based on the second reactive power optimization model, determining the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index; selecting each second reactive power optimization information whose corresponding reactive power compensation cost index value meets the preset reactive power compensation cost condition from multiple second reactive power optimization information, as multiple third reactive power optimization information.

[0138] Among them, the preset reactive power compensation cost condition is the preset threshold of reactive power compensation cost.

[0139] Specifically, the second reactive power optimization model includes an objective function corresponding to the reactive power compensation cost index. Based on the second reactive power optimization model, the server first determines the function value of each second reactive power optimization information under the objective function corresponding to the reactive power compensation cost index, which is the reactive power compensation cost index value of each second reactive power optimization information, thus obtaining the reactive power compensation cost corresponding to each second reactive power optimization information. Then, the server selects each second reactive power optimization information whose corresponding reactive power compensation cost is less than or equal to a preset threshold from multiple second reactive power optimization information, and uses them as multiple third reactive power optimization information.

[0140] In this embodiment, by calculating the reactive power compensation cost of each second reactive power optimization information, the server can select multiple third reactive power optimization information that has the lowest (or lower) economic cost from multiple second reactive power optimization information. Therefore, it can consider the operation requirements of the distribution network in the economic cost dimension during the reactive power optimization process, thereby improving the operation stability of the distribution network.

[0141] In an exemplary embodiment, based on the second reactive power optimization model, the reactive power compensation cost index value under the reactive power compensation cost index for each second reactive power optimization information is determined. Specifically, this includes the following: acquiring power flow data of the distribution network and the amount of resources spent by the distribution network in reactive power compensation; determining the output information of each grid node in the distribution network after reactive power compensation and the reactive power compensation capacity of the distribution network in reactive power compensation through the second reactive power optimization model and power flow data; and determining the reactive power compensation cost index value under the reactive power compensation cost index corresponding to each second reactive power optimization information based on the output information of each grid node after reactive power compensation, the reactive power compensation capacity of the distribution network in reactive power compensation, and the amount of resources spent by the distribution network in reactive power compensation.

[0142] The amount of resources spent by the distribution network in the historical reactive power compensation process includes the investment cost of reactive power compensation equipment per unit capacity of the distribution network, the annual maintenance cost of reactive power compensation equipment per unit capacity of the distribution network, the network loss price of the distribution network, and the equivalent annual coefficient of the distribution network.

[0143] Specifically, the server first obtains the power flow data of the distribution network, the investment cost of reactive power compensation equipment per unit capacity of the distribution network, the annual maintenance cost of reactive power compensation equipment per unit capacity of the distribution network, the network loss price of the distribution network, and the equivalent annual coefficient of the distribution network; then, the server calls the second reactive power optimization model to determine the output information of each grid node in the distribution network after reactive power compensation and the reactive power compensation capacity of the distribution network in reactive power compensation based on the power flow data, and calculates the terminal voltage of each grid line in the distribution network using the following formula 7:

[0144] (Formula 7)

[0145] in, Line identifiers for power grid lines in a distribution network; Indicates power grid lines Active power loss; Indicates power grid lines The meritorious trend; Indicates power grid lines The unproductive current; Indicates power grid lines The resistance; Indicates power grid lines The terminal voltage.

[0146] Next, based on the power output information of each power grid node after reactive power compensation, the reactive power compensation capacity of the distribution network in reactive power compensation, the terminal voltage of each power grid line, and the amount of resources spent by the distribution network in the reactive power compensation process, the server calculates the reactive power compensation cost index value of each piece of second reactive power optimization information under the reactive power compensation cost index through the objective function corresponding to the reactive power compensation cost index in the second reactive power optimization model, i.e., the following formula 8:

[0147] (Formula 8)

[0148] in, This represents the value of the reactive power compensation cost index under the reactive power compensation cost index. Represents a power grid node Reactive power after reactive power compensation; Represents a power grid node Active power after reactive power compensation; Represents a power grid node The power grid line where it is located The terminal voltage; Represents a power grid node The power grid line where it is located The resistance; This represents the annual operating hours of reactive power optimization. This indicates the electricity price for network losses in the distribution network; Indicates the isochronous value coefficient; This indicates the investment cost per unit capacity of reactive power compensation equipment; This indicates the reactive power compensation capacity of the distribution network in reactive power compensation. This represents the annual maintenance cost per unit capacity of reactive power compensation equipment in a power distribution network.

[0149] In this embodiment, the server can calculate the reactive power compensation cost index value corresponding to each piece of second reactive power optimization information through the objective function corresponding to the reactive power compensation cost index in the second reactive power optimization model.

[0150] In one exemplary embodiment, such as Figure 4 As shown, another reactive power optimization method for power distribution networks is provided. Taking the application of this method to a server as an example, the method includes the following steps:

[0151] Step S401: For each preset power flow index, determine the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model.

[0152] Step S402: For the target reactive power optimization information among the multiple first reactive power optimization information, determine each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information, and use it as the inferior optimization information corresponding to the target reactive power optimization information.

[0153] Step S403: Based on the disadvantage optimization information corresponding to the target reactive power optimization information and the disadvantage optimization information corresponding to the other reactive power optimization information among the multiple first reactive power optimization information, obtain the disadvantage information of the target reactive power optimization information under the power flow index.

[0154] Step S404: Integrate the disadvantage information of each first reactive power optimization information under various power flow indicators to obtain the comprehensive disadvantage information of each first reactive power optimization information.

[0155] Step S405: From multiple first reactive power optimization information, select each first reactive power optimization information whose corresponding comprehensive disadvantage information meets the preset disadvantage conditions, and use them as multiple second reactive power optimization information.

[0156] Step S406: Based on the second reactive power optimization model, determine the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index.

[0157] Step S407: From multiple second reactive power optimization information, select each second reactive power optimization information whose corresponding reactive power compensation cost index value meets the preset reactive power compensation cost condition, and use them as multiple third reactive power optimization information.

[0158] Step S408 involves taking multiple third reactive power optimization information as multiple new first reactive power optimization information, and returning the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0159] Step S409: Perform reactive power optimization on the distribution network based on the reactive power optimization information that meets the preset screening conditions.

[0160] In this embodiment, the server can filter reactive power optimization information from the perspective of power flow data using a first reactive power optimization model and power flow indicators. It can also filter reactive power optimization information from the perspective of economic cost using a second optimization model and reactive power optimization cost indicators. Therefore, the server can consider the operational needs of the distribution network from multiple perspectives during the reactive power optimization process. Furthermore, by iteratively filtering reactive power optimization information from both the power flow data and economic cost dimensions, the server can also couple the operational needs of the distribution network from various aspects, thus comprehensively considering the operational needs of the distribution network during the reactive power optimization process. Therefore, the reactive power optimization information obtained based on the above process improves the operational stability of the distribution network.

[0161] To more clearly illustrate the reactive power optimization method for distribution networks provided in the embodiments of this application, a specific embodiment is used below to describe the reactive power optimization method for distribution networks. However, it should be understood that the embodiments of this application are not limited thereto. Figure 5 As shown in an exemplary embodiment, this application also provides a reactive power optimization method for a distribution network based on a two-level reactive power optimization model, specifically including the following steps:

[0162] 1. Construct a two-layer model.

[0163] Taking into account both the stability and economy of the power grid, as well as the priority between the two, an upper-level model is established with system network loss and voltage deviation as objectives and generator node voltage amplitude, parallel capacitor compensation capacity and adjustable transformer turns ratio as control variables, and a lower-level model with economic cost as the objective, thus obtaining a two-level optimization model.

[0164] 2. Solve for the optimal solution in the upper-level model.

[0165] By improving the hybrid algorithm that combines NSGA2 and rough set theory, the optimal solution among multiple reactive power optimization schemes is obtained in the upper-level model.

[0166] 3. Solve for the optimal solution in the lower-level model.

[0167] The optimal solution obtained from the upper-level model is input into the lower-level model, where the optimal solution among multiple reactive power optimization schemes is obtained.

[0168] 4. Obtain the optimal solution of the two-layer model through iteration.

[0169] The optimal solution obtained by the lower-level model is fed back to the upper-level model. Through the mutual transfer between the upper-level and lower-level models, the final optimal solution among multiple reactive power optimization schemes is obtained in the upper-level or lower-level model.

[0170] In this embodiment, the stability and economy of the power grid, as well as the priority between the two, are comprehensively considered to establish a two-layer multi-objective optimization model, which takes into account the actual situation and avoids the setting of weights. In order to overcome the limitations of existing genetic algorithms, the characteristics of rough set theory are also utilized, which can handle multi-objective optimization problems well. Combining and improving rough set theory with artificial intelligence algorithms can better solve the disadvantages of existing algorithms and obtain the optimal solution.

[0171] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0172] Based on the same inventive concept, this application also provides a reactive power optimization device for implementing the reactive power optimization method for the distribution network described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the reactive power optimization device for the distribution network provided below can be found in the limitations of the reactive power optimization method for the distribution network described above, and will not be repeated here.

[0173] In one exemplary embodiment, such as Figure 6 As shown, a reactive power optimization device for a power distribution network is provided, comprising: a first screening module 602, a second screening module 604, a screening iteration module 606, and a reactive power optimization model 608, wherein:

[0174] The first screening module 602 is used to screen out multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators.

[0175] The second filtering module 604 is used to filter out multiple third reactive power optimization information from multiple second reactive power optimization information based on a pre-established second reactive power optimization model and a preset reactive power optimization cost index.

[0176] The filtering iteration module 606 is used to take multiple third reactive power optimization information as multiple new first reactive power optimization information and return the steps of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions.

[0177] The reactive power optimization module 608 is used to optimize the reactive power of the distribution network based on reactive power optimization information that meets preset screening conditions.

[0178] In an exemplary embodiment, the first screening module 602 is further configured to determine the disadvantage information of each first reactive power optimization information under each power flow index based on the first reactive power optimization model; the disadvantage information is used to characterize the index value of each first reactive power optimization information corresponding to each power flow index, and the degree of disadvantage among all the index values ​​of the first reactive power optimization information corresponding to each power flow index; to fuse the disadvantage information of each first reactive power optimization information under each power flow index to obtain the comprehensive disadvantage information of each first reactive power optimization information; and to select from the multiple first reactive power optimization information that the corresponding comprehensive disadvantage information satisfies the preset disadvantage condition as multiple second reactive power optimization information.

[0179] In an exemplary embodiment, the first screening module 602 is further configured to, for each power flow index, determine the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model; for the target reactive power optimization information among the multiple first reactive power optimization information, determine each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information, as the inferior optimization information corresponding to the target reactive power optimization information; the target reactive power optimization information is any one of the multiple first reactive power optimization information; based on the inferior optimization information corresponding to the target reactive power optimization information and the inferior optimization information corresponding to the remaining reactive power optimization information among the multiple first reactive power optimization information, obtain the inferior information of the target reactive power optimization information under the power flow index.

[0180] In one exemplary embodiment, a plurality of preset power flow indicators include system network loss indicators and voltage offset indicators.

[0181] The first screening module 602 is also used to acquire power flow data of the distribution network; through the first reactive power optimization model and power flow data, it determines the voltage change information of each grid node in the distribution network, the conductivity information between each grid node, and the voltage difference information between each grid node; based on the voltage change information, conductivity information, and voltage difference information, it determines the system network loss index value of the distribution network under the system network loss index, and based on the voltage change information, it determines the voltage deviation index value of the distribution network under the voltage deviation index.

[0182] In an exemplary embodiment, the reactive power optimization cost index is the reactive power compensation cost index.

[0183] The second filtering module 604 is also used to determine the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index based on the second reactive power optimization model; and to filter out each second reactive power optimization information whose corresponding reactive power compensation cost index value meets the preset reactive power compensation cost condition from multiple second reactive power optimization information, as multiple third reactive power optimization information.

[0184] In an exemplary embodiment, the second filtering module 604 is further configured to acquire power flow data of the distribution network and the amount of resources spent by the distribution network in reactive power compensation; determine the output information of each power grid node in the distribution network after reactive power compensation and the reactive power compensation capacity of the distribution network in reactive power compensation through the second reactive power optimization model and power flow data; and determine the reactive power compensation cost index value under the reactive power compensation cost index corresponding to each second reactive power optimization information based on the output information of each power grid node after reactive power compensation, the reactive power compensation capacity of the distribution network in reactive power compensation, and the amount of resources spent by the distribution network in reactive power compensation.

[0185] Each module in the aforementioned reactive power optimization device for the power distribution network can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0186] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores power flow data and reactive power optimization data for the distribution network. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a reactive power optimization method for the distribution network.

[0187] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0188] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0189] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0190] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0191] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0192] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0193] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A reactive power optimization method for a power distribution network, characterized in that, The method includes: Based on a pre-established first reactive power optimization model and multiple preset power flow indicators, multiple second reactive power optimization information is selected from multiple first reactive power optimization information; further comprising: based on the first reactive power optimization model, determining the disadvantage information of each first reactive power optimization information under each power flow indicator; the disadvantage information is used to characterize the degree of disadvantage of each first reactive power optimization information corresponding to each power flow indicator among all the first reactive power optimization information corresponding to each power flow indicator; fusing the disadvantage information of each first reactive power optimization information under each power flow indicator to obtain the comprehensive disadvantage information of each first reactive power optimization information; selecting from the multiple first reactive power optimization information the first reactive power optimization information whose corresponding comprehensive disadvantage information satisfies the preset disadvantage conditions, as the multiple second reactive power optimization information; Based on a pre-established second reactive power optimization model and a preset reactive power optimization cost index, multiple third reactive power optimization information is selected from the multiple second reactive power optimization information; the reactive power optimization cost index is a reactive power compensation cost index; further comprising: based on the second reactive power optimization model, determining the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index; selecting from the multiple second reactive power optimization information each second reactive power optimization information whose corresponding reactive power compensation cost index value satisfies the preset reactive power compensation cost condition, as the multiple third reactive power optimization information; The multiple third reactive power optimization information is used as multiple new first reactive power optimization information, and the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators is returned until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions. Reactive power optimization is performed on the distribution network based on reactive power optimization information that meets the preset screening conditions.

2. The method according to claim 1, characterized in that, The step of determining the disadvantage information of each first reactive power optimization information under each power flow index based on the first reactive power optimization model includes: For each power flow index, the power flow index value of each first reactive power optimization information under the power flow index is determined based on the first reactive power optimization model; For the target reactive power optimization information among the plurality of first reactive power optimization information, each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information is determined as the inferior optimization information corresponding to the target reactive power optimization information; the target reactive power optimization information is any one of the plurality of first reactive power optimization information. Based on the disadvantage optimization information corresponding to the target reactive power optimization information and the disadvantage optimization information corresponding to the remaining reactive power optimization information among the plurality of first reactive power optimization information, the disadvantage information of the target reactive power optimization information under the power flow index is obtained.

3. The method according to claim 2, characterized in that, The preset power flow indicators include system network loss indicators and voltage offset indicators; The step of determining the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model for each power flow index includes: Obtain the power flow data of the power distribution network; Using the first reactive power optimization model and the power flow data, the voltage change information of each grid node in the distribution network, the conductivity information between each grid node, and the voltage difference information between each grid node are determined. Based on the voltage change information, the conductivity information, and the voltage difference information, the system network loss index value of each first reactive power optimization information under the system network loss index is determined, and based on the voltage change information, the voltage offset index value of each first reactive power optimization information under the voltage offset index is determined.

4. The method according to claim 1, characterized in that, The step of determining the reactive power compensation cost index value under the reactive power compensation cost index for each piece of second reactive power optimization information based on the second reactive power optimization model includes: Obtain the power flow data of the distribution network and the amount of resources spent by the distribution network in reactive power compensation; Using the second reactive power optimization model and the power flow data, the output information of each power grid node in the distribution network after reactive power compensation and the reactive power compensation capacity of the distribution network in reactive power compensation are determined. Based on the power output information of each power grid node after reactive power compensation, the reactive power compensation capacity of the distribution network in reactive power compensation, and the amount of resources spent by the distribution network in reactive power compensation, the reactive power compensation cost index value under the reactive power compensation cost index corresponding to each second reactive power optimization information is determined.

5. A reactive power optimization device for a power distribution network, characterized in that, The device includes: The first filtering module is used to filter out multiple second reactive power optimization information from multiple first reactive power optimization information based on a pre-established first reactive power optimization model and multiple preset power flow indicators; the first filtering module is further used to determine the disadvantage information of each first reactive power optimization information under each power flow indicator based on the first reactive power optimization model; the disadvantage information is used to characterize the degree of disadvantage of each first reactive power optimization information corresponding to each power flow indicator among all the first reactive power optimization information corresponding to each power flow indicator; the disadvantage information of each first reactive power optimization information under each power flow indicator is fused to obtain the comprehensive disadvantage information of each first reactive power optimization information; from the multiple first reactive power optimization information, each first reactive power optimization information whose corresponding comprehensive disadvantage information satisfies the preset disadvantage condition is selected as the multiple second reactive power optimization information. The second filtering module is used to filter out multiple third reactive power optimization information from the multiple second reactive power optimization information based on a pre-established second reactive power optimization model and a preset reactive power optimization cost index; the reactive power optimization cost index is a reactive power compensation cost index; the second filtering module is further used to determine the reactive power compensation cost index value of each second reactive power optimization information under the reactive power compensation cost index based on the second reactive power optimization model; and to filter out each second reactive power optimization information whose corresponding reactive power compensation cost index value satisfies the preset reactive power compensation cost condition from the multiple second reactive power optimization information, as the multiple third reactive power optimization information; The filtering iteration module is used to take the multiple third reactive power optimization information as multiple new first reactive power optimization information and return the step of filtering multiple second reactive power optimization information from multiple first reactive power optimization information based on the pre-established first reactive power optimization model and multiple preset power flow indicators, until the reactive power optimization information filtered based on the first reactive power optimization model or the second reactive power optimization model meets the preset filtering conditions. The reactive power optimization module is used to optimize the power distribution network based on the reactive power optimization information that meets the preset screening conditions.

6. The apparatus according to claim 5, characterized in that, The first filtering module is further configured to, for each power flow index, determine the power flow index value of each first reactive power optimization information under the power flow index based on the first reactive power optimization model; and for the target reactive power optimization information among the plurality of first reactive power optimization information, determine each first reactive power optimization information whose corresponding power flow index value is less than or equal to the power flow index value of the target reactive power optimization information, as the inferior optimization information corresponding to the target reactive power optimization information. The target reactive power optimization information is any one of the plurality of first reactive power optimization information; Based on the disadvantage optimization information corresponding to the target reactive power optimization information and the disadvantage optimization information corresponding to the remaining reactive power optimization information among the plurality of first reactive power optimization information, the disadvantage information of the target reactive power optimization information under the power flow index is obtained.

7. The apparatus according to claim 6, characterized in that, The preset power flow indicators include system network loss indicators and voltage offset indicators; The first filtering module is further configured to acquire power flow data of the distribution network; determine voltage change information of each grid node in the distribution network, conductivity information between each grid node, and voltage difference information between each grid node through the first reactive power optimization model and the power flow data; determine the system network loss index value of each first reactive power optimization information under the system network loss index based on the voltage change information, the conductivity information, and the voltage difference information; and determine the voltage offset index value of each first reactive power optimization information under the voltage offset index based on the voltage change information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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