Transformer area three-phase balancing and regional load balancing method, device, equipment and medium

Abstract

The invention discloses a transformer area three-phase balance and regional load balance method, device, equipment and medium, and belongs to the field of power distribution network power planning, and the method comprises the steps: screening a first sub-region with unbalanced load from each sub-region according to the total energy consumption of each sub-region in a to-be-planned region and the total number of users; obtaining and preprocessing three-phase current time sequence data of each first device in the first sub-region; the energy consumption of the first equipment is greater than preset energy consumption; according to the preprocessed three-phase current time sequence data, screening out a second device with unbalanced three phases from the first devices; constructing a multi-objective optimization mathematical model by taking the three-phase current balance of each second device and the load balance of the to-be-planned area as optimization objectives; and according to a solving result of the multi-objective optimization mathematical model, redistributing a connection phase of each second device and a sub-region to which the second device belongs. According to the invention, the problem that the three-phase balance and regional load balance of the low-voltage transformer area of the power distribution network cannot be optimized in time in a dynamic environment can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network power planning, in particular to a method and device for three-phase balance and regional load balance of a transformer area, equipment and medium. BACKGROUND

[0002] With the rapid development of electric vehicles (EV), the EV penetration rate continues to rise. However, the temporal and spatial aggregation and high power characteristics of EV charging load (such as fast charging pile power up to 60-120kW) have a significant impact on low-voltage distribution transformer areas.

[0003] To solve the above problems, the existing technology usually compensates for the three-phase imbalance of the distribution transformer area to alleviate the problem of the expansion of the three-phase current difference caused by the random access of single-phase charging piles, thereby inhibiting transformer overheating, line loss increase and neutral line overload phenomenon. However, the existing technology does not fully consider the coupling relationship between three-phase balance and regional load balance, resulting in the superposition effect of the negative effects of the two not being effectively inhibited. For example, when the concentrated charging of residential areas at night is superimposed on the charging peak of commercial areas during the day, the local regional load exceeds the carrying capacity in a short period of time, and if the three-phase imbalance problem is superimposed, it will cause multiple problems such as transformer overload, line loss aggravation, and voltage quality deterioration. In addition, the existing technology relies too much on manual adjustment in the intelligent operation and management of low-voltage transformer areas in the distribution network, which lacks real-time performance and is difficult to cope with dynamic load changes, thereby reducing power supply reliability.

[0004] Therefore, how to quickly and cooperatively optimize the three-phase balance and regional load balance of the low-voltage transformer area in the dynamic environment is a technical problem to be solved at present. SUMMARY

[0005] The present application provides a method and device for three-phase balance and regional load balance of a transformer area, equipment and medium, which can solve the problem of how to quickly and simultaneously optimize the three-phase balance and regional load balance of the low-voltage transformer area in the distribution network in the prior art.

[0006] An embodiment of the present application provides a method for three-phase balance and regional load balance of a transformer area, comprising:

[0007] Statistically, the total energy consumption and the total number of users in each sub-area in the region to be planned are counted, and according to the total energy consumption and the total number of users, a first sub-area with unbalanced load is selected from each sub-area;

[0008] Obtain and preprocess the three-phase current time series data of each first device in the first sub-area; the first device is a device with energy consumption greater than a preset energy consumption;

[0009] According to the preprocessed three-phase current time series data, a second device with three-phase imbalance is selected from the first device;

[0010] A multi-objective optimization mathematical model is constructed using the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balance of the area to be planned as optimization objectives;

[0011] Solve the multi-objective optimization mathematical model, and reallocate the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

[0012] Compared with the prior art, the above embodiment has the following beneficial effects: by counting the total energy consumption and the total number of users in each sub-area in the area to be planned, first identify the first sub-area with unbalanced load from all sub-areas; then, based on the three-phase current time series data of the high-energy-consuming equipment (i.e., the first equipment) in the first sub-area, further lock the key equipment (i.e., the second equipment) that causes the three-phase imbalance problem. Through precise positioning strategies, computing resources are concentrated on local key nodes, which significantly improves the intelligent operation and maintenance efficiency of low-voltage substations in distribution networks under dynamic environments. In addition, this application constructs a multi-objective optimization mathematical model with three-phase current balance and regional load balance as the core goals, and realizes the collaborative optimization of multiple goals by dynamically adjusting the phase allocation and regional affiliation of equipment. Compared with traditional global optimization methods, this application takes into account both real-time performance and computing efficiency through a local-global collaborative optimization strategy, effectively improving power supply reliability and reducing line losses.

[0013] Furthermore, the selecting, from the sub-areas according to the total energy consumption and the total number of users, a first sub-area with an unbalanced load includes:

[0014] The ratio of the total energy consumption to the total number of users is used as the energy consumption density of the corresponding sub-area;

[0015] The sub-region with the highest energy consumption density is used as the second sub-region, and when the energy consumption density of the second sub-region is higher than the energy consumption density of other sub-regions by a first preset multiple, the second sub-region is used as the first sub-region.

[0016] Compared with the existing technology, the above embodiment has the following beneficial effects: by taking the ratio of total energy consumption to the total number of users as the energy consumption density, and using the energy consumption density as the screening indicator, the sub-region with significantly higher energy consumption density than other regions is selected as the first sub-region, thereby accurately identifying local areas with unbalanced load distribution, avoiding misjudgment caused by parameter fluctuations during the global calculation process, improving the accuracy of load imbalance detection, and reducing invalid calculations of non-critical areas in the subsequent optimization process, thereby improving the system response speed in a dynamic environment.

[0017] Furthermore, the pre-processing of the three-phase current time series data of each first device in the first sub-area includes:

[0018] screening a number of abnormal data points from the three-phase current time series data;

[0019] Sliding a sliding window of a preset size on the three-phase current time series data to obtain data within the sliding window before and after each abnormal data point;

[0020] And based on the data in the sliding window before and after the abnormal data point, weighted interpolation compensation is performed on the data at the abnormal data point.

[0021] Compared with the existing technology, the above embodiment has the following beneficial effects: the sliding window weighted interpolation method is used to compensate for abnormal points in the three-phase current time series data, effectively eliminating the impact of sensor noise or transient interference on data quality, and retaining the dynamic characteristics of current changes through local data fitting, thereby improving the robustness of data preprocessing and providing a reliable data basis for the subsequent accurate identification of three-phase unbalanced equipment.

[0022] Furthermore, the step of screening out the second device with three-phase imbalance from the first device based on the preprocessed three-phase current time series data includes:

[0023] Calculating the average current of each phase corresponding to the first device according to the preprocessed three-phase current time series data;

[0024] The first device that meets the preset conditions is used as the second device; wherein the preset conditions include: the average current of one phase is greater than the sum of the average currents of other phases by a second preset multiple.

[0025] Compared with the existing technology, the above embodiment has the following beneficial effects: by calculating the average current of the first device in each phase, the first device in which the average current of one phase is significantly higher than the average current of other phases is used as the second device, so as to quickly locate the device at the source of the three-phase imbalance, narrow the optimization range in the subsequent optimization calculation process, reduce the calculation complexity, and improve the targeted problem solving; at the same time, the second device is screened and obtained in the first device, and the first device is a sub-area with unbalanced load. Therefore, when the allocation decision of the second device is subsequently optimized, the coordinated optimization of three-phase current balance and regional load balance can be achieved, thereby improving the optimization efficiency.

[0026] Furthermore, the phase to which each second device needs to be connected and the sub-area in which it needs to be located are used as decision variables, and the three-phase current balance of each second device and the load balance of the area to be planned are used as optimization objectives to construct a multi-objective optimization mathematical model, including:

[0027] constructing a first optimization objective according to the cost required when the second device is connected to the phase to be connected and the three-phase current data;

[0028] constructing a second optimization objective according to the load difference between the sub-areas when the second device is in the sub-area where it is required to be located;

[0029] The weighted sum of the first optimization objective and the second optimization objective is used as the optimization objective to construct the multi-objective optimization mathematical model.

[0030] Compared to existing technologies, the above embodiment has the following beneficial effects: by constructing a multi-objective optimization model, using device phase allocation and sub-region adjustment as decision variables, it achieves the coordinated optimization of three-phase current balance and regional load balancing. Compared with single-objective optimization (such as pursuing only three-phase balance or only load balancing), this method avoids the conflict between the two optimization objectives (such as balancing three phases may lead to uneven load), and achieves a dynamic balance between the two through a weighted strategy, significantly improving the stability of distribution network operation.

[0031] Furthermore, constructing a first optimization objective based on the cost required when the second device is connected to the phase to be connected and the three-phase current data includes:

[0032] Min((max(I D,k )-min(I D,k )) / mean(I D,k )) 2 +x k,p ·C k,p

[0033] Among them, Min represents the minimization optimization target; x k,p Indicates whether device k is assigned to phase p. The three-phase current after assignment is I D,k ;max(I D,k )、min(I D,k ) and mean(I D,k ) represent the maximum current, minimum current and average current of the three-phase transformer during the period when the device k is assigned to the phase p; C k,p is the cost of moving device k to phase p. If there is no charging station at phase p in a sub-area, then C k,p To infinity.

[0034] Compared with the prior art, the above embodiment has the following beneficial effects: taking the sum of the square of the current imbalance and the phase switching cost as the first optimization target, it not only quantifies the degree of imbalance of the three-phase current, but also introduces the economic cost of phase switching, thereby minimizing the resource consumption of operation and maintenance operations while ensuring the power quality, reducing the adjustment frequency in a dynamic environment, and improving power supply reliability; at the same time, when calculating the sum of the switching costs, a cost parameter is introduced, thereby coupling the three-phase balance optimization with the subsequent regional allocation optimization, and realizing coordinated optimization between the two goals.

[0035] Furthermore, constructing a second optimization objective according to the load difference between the sub-areas when the second device is in the sub-area where it is required to be located includes:

[0036]

[0037] Among them, Min represents the minimization optimization target; R represents the area to be planned; L r represents the total load of sub-region r; mean(L) represents the average load of the sub-region; x k,p Indicates whether device k is assigned to phase p; I k,p Represents the current value when device k is connected to phase p.

[0038] Compared with the existing technology, the above embodiment has the following beneficial effects: by minimizing the variance between the load of each sub-region and the average load, balanced distribution of regional load is achieved, thereby effectively suppressing the thermal effects and line loss problems caused by load overload in local areas, extending the service life of equipment, and at the same time improving the adaptability of the distribution network to dynamic load fluctuations, thereby enhancing the overall stability of the system.

[0039] Another embodiment of the present application further provides a substation three-phase balancing and regional load balancing device, comprising: a first sub-region screening module, a data processing module, a second device screening module, a model building module, and a model solving module;

[0040] The first sub-region screening module is configured to count the total energy consumption and the total number of users of each sub-region in the area to be planned, and screen the first sub-region with unbalanced load from each sub-region based on the total energy consumption and the total number of users;

[0041] The data processing module is used to obtain and pre-process the three-phase current time series data of each first device in the first sub-area; the first device is a device whose energy consumption is greater than a preset energy consumption;

[0042] The second device screening module is configured to screen out second devices with three-phase imbalance from the first devices based on the preprocessed three-phase current time series data;

[0043] The model building module is configured to construct a multi-objective optimization mathematical model using the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balancing of the area to be planned as optimization objectives;

[0044] The model solving module is used to solve the multi-objective optimization mathematical model and reallocate the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

[0045] Another embodiment of the present application also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the substation three-phase balancing and regional load balancing method of the present application are implemented.

[0046] Another embodiment of the present application also provides a computer-readable storage medium item, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the substation three-phase balancing and regional load balancing method of the present application when the computer program is running. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A flow chart of a method for three-phase balancing and regional load balancing in a transformer area provided in some embodiments of the present application;

[0049] Figure 2 This is a structural diagram of a substation three-phase balancing and regional load balancing device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0052] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0053] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0055] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0056] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0057] Existing technologies usually alleviate the problem of widening three-phase current differences in distribution areas caused by random access of single-phase charging piles by compensating for the three-phase imbalance in distribution areas, thereby suppressing transformer overheating, increased line loss, and neutral line overload. However, existing technologies do not fully consider the coupling relationship between three-phase balance and regional load balancing, resulting in the superposition effect of the two negative effects not being effectively suppressed. For example, when concentrated charging at night in residential areas and peak charging in commercial areas during the day overlap, the load in the local area exceeds the carrying capacity in a short period of time. If the three-phase imbalance problem is superimposed, it will lead to multiple problems such as transformer overload, increased line loss, and deterioration of voltage quality. In addition, existing technologies rely too much on manual adjustments in the intelligent operation and maintenance management of low-voltage distribution areas in the distribution network, lack real-time performance, and are difficult to cope with dynamic load changes, which reduces power supply reliability.

[0058] Please refer to Figure 1 To solve the problem of how to quickly and collaboratively optimize the three-phase balance and regional load balancing of the low-voltage distribution network in a dynamic environment in the prior art, the present application provides a method for regional three-phase balance and regional load balancing, including S101 to S105, specifically:

[0059] S101: Counting the total energy consumption and the total number of users of each sub-area in the area to be planned, and selecting a first sub-area with an unbalanced load from each sub-area according to the total energy consumption and the total number of users.

[0060] Furthermore, in some embodiments of the present application, the statistical total energy consumption and the total number of users of each sub-area in the area to be planned include S1011 to S1013, specifically:

[0061] S1011: Through the intelligent measurement switch, load identification data, user data, and transformer minute-level current data are obtained in real time; the load identification data includes: the load type of the equipment used by each user and the power consumption characteristics of each equipment (such as power, start and stop time, etc.), among which the load type includes charging piles or air conditioners connected to high-power equipment (such as electric vehicles).

[0062] S1012: Calculate the energy consumption of each device according to the power consumption characteristics of the device, and take the sum of the energy consumption of all devices in each sub-area as the total energy consumption of the corresponding sub-area.

[0063] S1013: The user data includes user relationship topology information of each sub-area. It should be noted that in the embodiment of the present application, the transformers and the sub-areas correspond to each other one-to-one, and each transformer needs to collect corresponding transformer data, specifically including: rated capacity / current / voltage, three-phase current / voltage time series data; further, based on the user relationship topology information, the total number of users in the corresponding sub-area is counted.

[0064] Furthermore, in some embodiments of the present application, selecting a first sub-region with an unbalanced load from each of the sub-regions according to the total energy consumption and the total number of users includes:

[0065] The ratio of the total energy consumption to the total number of users is used as the energy consumption density of the corresponding sub-area;

[0066] The sub-region with the highest energy consumption density is used as the second sub-region, and when the energy consumption density of the second sub-region is higher than the energy consumption density of other sub-regions by a first preset multiple, the second sub-region is used as the first sub-region.

[0067] Preferably, in some embodiments of the present application, the calculation formula of the energy consumption density is:

[0068]

[0069] Among them, D r is the energy consumption density of sub-region r; E r is the total energy consumption of sub-region r; n r is the total number of users in sub-region r.

[0070] Preferably, in some embodiments of the present application, the first preset multiple is set to 1.5 times, that is, when there is a sub-region r of D r When it is greater than 1.5 times the energy consumption density of all other sub-regions, the sub-region r is considered to be the first sub-region, which also means that the regional load of the first sub-region is currently in an unbalanced state.

[0071] It can be seen from the above preferred embodiments that the present application uses the ratio of total energy consumption to the total number of users as energy consumption density, and energy consumption density as a screening indicator, and selects the sub-region with significantly higher energy consumption density than other regions as the first sub-region, thereby accurately identifying local areas with unbalanced load distribution, avoiding misjudgment caused by parameter fluctuations during global calculations, improving the accuracy of load imbalance detection, and reducing invalid calculations of non-critical areas in subsequent optimization processes, thereby improving the system response speed in a dynamic environment.

[0072] S102: Acquire and pre-process three-phase current time series data of each first device in the first sub-area; the first device is a device whose energy consumption is greater than a preset energy consumption.

[0073] Preferably, in some embodiments of the present application, obtaining three-phase current time series data for each first device in the first sub-area includes: the three-phase current time series data is minute-level current data of the transformer obtained in S1011, where minute-level represents current data collected every minute. The three-phase current time series data includes A / B / C phase current time series data of each device.

[0074] Furthermore, in some embodiments of the present application, the pre-processing of the three-phase current time series data of each first device in the first sub-area includes:

[0075] screening a number of abnormal data points from the three-phase current time series data;

[0076] Sliding a sliding window of a preset size on the three-phase current time series data to obtain data within the sliding window before and after each abnormal data point;

[0077] And based on the data in the sliding window before and after the abnormal data point, weighted interpolation compensation is performed on the data at the abnormal data point.

[0078] Preferably, in some embodiments of the present application, weighted interpolation compensation is performed on the data at the abnormal data point based on the data in the sliding window before and after the abnormal data point, specifically:

[0079] In order to ensure the synchronization of the three-phase current time series data, if there is data missing, considering that the abnormal data in the three-phase current time series data needs to maintain its trend, a sliding window with a preset size of five minutes is used in the embodiment of the present application. Combined with the statistical characteristics of the front and back windows, weighted interpolation compensation is performed on the abnormal data. The formula is as follows:

[0080]

[0081] Among them, I ′ t is the three-phase current time series data at time t after preprocessing; I t is the three-phase current time series data at time t before preprocessing; w k is the time decay weight at time k, w k =e -λ|t-k| ; n is the size of the sliding window; I k is the three-phase current time series data at time k.

[0082] Preferably, in some embodiments of the present application, in addition to determining the first device based on the energy consumption of the device and the preset energy consumption, devices of certain load types can also be preset as the first device, such as the high-power electric vehicle mentioned in S1011, mainly to ensure that the first device is a high-energy-consuming device.

[0083] It can be seen from the above preferred embodiments that the present application adopts the sliding window weighted interpolation method to compensate for abnormal points in the three-phase current time series data, effectively eliminating the impact of sensor noise or instantaneous interference on data quality, and retaining the dynamic characteristics of current changes through local data fitting, thereby improving the robustness of data preprocessing and providing a reliable data basis for the subsequent accurate identification of three-phase unbalanced equipment.

[0084] S103: Filter out second devices with three-phase imbalance from the first devices according to the preprocessed three-phase current time series data.

[0085] Furthermore, in some embodiments of the present application, screening out the second device with three-phase imbalance from the first device based on the preprocessed three-phase current time series data includes:

[0086] Calculating the average current of each phase corresponding to the first device according to the preprocessed three-phase current time series data;

[0087] The first device that meets the preset conditions is used as the second device; wherein the preset conditions include: the average current of one phase is greater than the sum of the average currents of other phases by a second preset multiple.

[0088] Preferably, in some embodiments of the present application, calculating the average current of each phase corresponding to the first device according to the preprocessed three-phase current time series data includes:

[0089] Calculate the average current of the first device in each phase using the following formula:

[0090]

[0091] Among them, I k,p is the average current of the first device k in phase p, p∈{A,B,C}; T is the length of the three-phase current time series data; I k.p (t) is the current value of the first device k when it is in phase p and at time t.

[0092] Preferably, in some embodiments of the present application, the second preset multiple is 1.5 times, that is, the first device satisfies I k,A >1.5(I k,B +I k,C ), the first device is considered to be the source of the three-phase imbalance.

[0093] It can be seen from the above preferred embodiments that the present application calculates the average current of the first device in each phase, and takes the first device in which the average current of one phase is significantly higher than the average current of other phases as the second device, thereby quickly locating the device at the source of the three-phase imbalance, narrowing the optimization range in the subsequent optimization calculation process, reducing the calculation complexity, and improving the targeted problem solving; at the same time, the second device is screened and obtained in the first device, and the first device is a sub-area with unbalanced load. Therefore, when the allocation decision of the second device is subsequently optimized, the coordinated optimization of three-phase current balance and regional load balance can be achieved, thereby improving the optimization efficiency.

[0094] S104: Taking the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balance of the area to be planned as optimization goals, a multi-objective optimization mathematical model is constructed.

[0095] Furthermore, in some embodiments of the present application, the phase to which each second device needs to be connected and the sub-area in which it needs to be located are used as decision variables, and the three-phase current balance of each second device and the load balancing of the area to be planned are used as optimization objectives to construct a multi-objective optimization mathematical model, including:

[0096] constructing a first optimization objective according to the cost required when the second device is connected to the phase to be connected and the three-phase current data;

[0097] constructing a second optimization objective according to the load difference between the sub-areas when the second device is in the sub-area where it is required to be located;

[0098] The weighted sum of the first optimization objective and the second optimization objective is used as the optimization objective to construct the multi-objective optimization mathematical model.

[0099] Furthermore, in some embodiments of the present application, constructing a first optimization objective based on the cost required when the second device is connected to the phase to be connected and the three-phase current data includes:

[0100] Min((max(I D,k )-min(I D,k )) / mean(I D,k )) 2 +x k,p ·C k,p

[0101] Among them, Min represents the minimization optimization target; x k,p Indicates whether device k is assigned to phase p. The three-phase current after assignment is I D,k ;max(I D,k )、min(I D,k ) and mean(I D,k ) represent the maximum current, minimum current and average current of the three-phase transformer during the period when the device k is assigned to the phase p; C k,p is the cost of moving device k to phase p. If there is no charging station at phase p in a sub-area, then C k,p is infinite. It should be noted that x k,p is a 0-1 variable, i.e. x k,p =1, it means that device k is assigned to phase p, x k,p=0, it means that device k is not assigned to phase p.

[0102] Furthermore, in some embodiments of the present application, constructing a second optimization objective based on the load difference between the sub-areas when the second device is in the sub-area where it is required to be located includes:

[0103]

[0104] Among them, Min represents the minimization optimization target; R represents the area to be planned; L r represents the total load of sub-region r; mean(L) represents the average load of the sub-region; x k,p Indicates whether device k is assigned to phase p; I k,p represents the current value when device k is connected to phase p. It can be seen that L r -mean(L) represents the load difference between sub-region r and the average load of each sub-region. By accumulating the load difference between each sub-region and the average load of each sub-region, the load difference between the sub-regions when the second device is in the required sub-region is obtained.

[0105] Furthermore, in some embodiments of the present application, the multi-objective optimization mathematical model further includes a constraint condition of the total load, specifically:

[0106]

[0107] Among them, S r,trans and U r,trans They represent the rated capacity and rated voltage of the transformer in sub-area r respectively, and the values ​​are obtained through the data collected in S1013.

[0108] Furthermore, in some embodiments of the present application, taking the weighted sum of the first optimization objective and the second optimization objective as the optimization objective includes:

[0109] The three-phase balance and regional balance objectives are integrated into a multi-objective optimization problem through the following formula:

[0110] Minα·First optimization target + (1-α)·Second optimization target

[0111] Among them, α is the weight value, ɑ∈[0,1], and the default setting is 0.5.

[0112] As can be seen from the above embodiments, this application achieves the coordinated optimization of three-phase current balance and regional load balance by constructing a multi-objective optimization model and taking equipment phase allocation and sub-region adjustment as decision variables. Compared with single-objective optimization (such as only pursuing three-phase balance or only pursuing load balance), this method avoids the conflict between the two optimization objectives (such as balancing three phases may lead to uneven load), and achieves a dynamic balance between the two through a weighted strategy, significantly improving the stability of distribution network operation.

[0113] S105: Solving the multi-objective optimization mathematical model, and reallocating the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

[0114] Preferably, in some embodiments of the present application, the multi-objective optimization mathematical model is solved by any mathematical model solver or any multi-objective optimization algorithm, and the present application does not limit the solution method.

[0115] In summary, the method for three-phase balance and regional load balancing of a substation provided by the embodiment of the present application has the following beneficial effects compared with the existing technology: by counting the total energy consumption and the total number of users in each sub-area in the area to be planned, first identify the first sub-area with unbalanced load from all sub-areas; then, based on the three-phase current time series data of the high-energy-consuming equipment (i.e., the first equipment) in the first sub-area, further lock the key equipment (i.e., the second equipment) that causes the three-phase imbalance problem. Through precise positioning strategies, computing resources are concentrated on local key nodes, significantly improving the intelligent operation and maintenance efficiency of low-voltage substations in distribution networks under dynamic environments. In addition, the present application constructs a multi-objective optimization mathematical model with three-phase current balance and regional load balancing as the core goals, and realizes the collaborative optimization of multiple goals by dynamically adjusting the phase allocation and regional affiliation of equipment. Compared with traditional global optimization methods, the present application uses a local-global collaborative optimization strategy to take into account real-time performance and computing efficiency, effectively improve power supply reliability and reduce line losses. In addition, the present application generates an optimal load distribution strategy based on real-time data of intelligent measurement switches, without the need to modify existing distribution equipment.

[0116] like Figure 2 As shown, based on the above-mentioned method embodiment, an embodiment of the present application provides a substation three-phase balancing and regional load balancing device, including: a first sub-area screening module 201, a data processing module 202, a second equipment screening module 203, a model building module 204 and a model solving module 205.

[0117] Furthermore, in some embodiments of the present application, the first sub-area screening module 201 is used to count the total energy consumption and the total number of users of each sub-area in the area to be planned, and based on the total energy consumption and the total number of users, screen out the first sub-area with unbalanced load from each sub-area; the data processing module 202 is used to obtain and pre-process the three-phase current timing data of each first device in the first sub-area; the first device is a device with energy consumption greater than the preset energy consumption; the second device screening module 203 is used to screen out the second device with three-phase unbalance from the first device based on the preprocessed three-phase current timing data; the model construction module 204 is used to take the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and to construct a multi-objective optimization mathematical model with the three-phase current balance of each second device and the load balance of the area to be planned as optimization goals; the model solving module 205 is used to solve the multi-objective optimization mathematical model, and reallocate the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

[0118] Furthermore, in some embodiments of the present application, the first sub-region with unbalanced load is screened from each sub-region based on the total energy consumption and the total number of users, including: taking the ratio of the total energy consumption to the total number of users as the energy consumption density of the corresponding sub-region; taking the sub-region with the largest energy consumption density as the second sub-region, and when the energy consumption density of the second sub-region is higher than the energy consumption density of other sub-regions by a first preset multiple, taking the second sub-region as the first sub-region.

[0119] Furthermore, in some embodiments of the present application, the preprocessing of the three-phase current time series data of each first device in the first sub-area includes: screening a number of abnormal data points from the three-phase current time series data; sliding a sliding window of a preset size on the three-phase current time series data to obtain data in the sliding window before and after each of the abnormal data points; and performing weighted interpolation compensation on the data at the abnormal data point based on the data in the sliding window before and after the abnormal data point.

[0120] Furthermore, in some embodiments of the present application, the method of screening out a second device with three-phase unbalance from the first device based on the preprocessed three-phase current time series data includes: calculating the average current of each phase corresponding to the first device based on the preprocessed three-phase current time series data; and using the first device that meets the preset conditions as the second device; wherein the preset conditions include: the average current of one phase is greater than the sum of the average currents of the other phases by a second preset multiple.

[0121] Furthermore, in some embodiments of the present application, the phase to which each second device needs to be connected and the sub-area in which it needs to be located are used as decision variables, and the three-phase current balance of each second device and the load balancing of the area to be planned are used as optimization objectives to construct a multi-objective optimization mathematical model, including: constructing a first optimization objective based on the cost and three-phase current data required when the second device is connected to the phase to be connected; constructing a second optimization objective based on the load difference between each sub-area when the second device is in the sub-area in which it needs to be located; and taking the weighted sum of the first optimization objective and the second optimization objective as the optimization objective to construct the multi-objective optimization mathematical model.

[0122] Furthermore, in some embodiments of the present application, constructing a first optimization objective based on the cost required when the second device is connected to the phase to be connected and the three-phase current data includes:

[0123] Min((max(I D,k )-min(I D,k )) / mean(I D,k )) 2 +x k,p ·C k,p

[0124] Among them, Min represents the minimization optimization target; x k,p Indicates whether device k is assigned to phase p. The three-phase current after assignment is I D,k ;max(I D,k )、min(I D,k ) and mean(I D,k ) represent the maximum current, minimum current and average current of the three-phase transformer during the period when the device k is assigned to the phase p; C k,p is the cost of moving device k to phase p. If there is no charging station at phase p in a sub-area, then C k,p To infinity.

[0125] Furthermore, in some embodiments of the present application, constructing a second optimization objective based on the load difference between the sub-areas when the second device is in the sub-area where it is required to be located includes:

[0126]

[0127]

[0128] Among them, Min represents the minimization optimization target; R represents the area to be planned; L r represents the total load of sub-region r; mean(L) represents the average load of the sub-region; xk,p Indicates whether device k is assigned to phase p; I k,p Represents the current value when device k is connected to phase p.

[0129] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present application, which can implement any of the above-mentioned method embodiments of the present application to provide a three-phase balance and regional load balancing method for the substation.

[0130] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided herein, the connection relationship between the modules indicates that there is a communication connection between them, which may be implemented as one or more communication buses or signal lines. Those skilled in the art may understand and implement the present invention without inventive effort.

[0131] Based on the above-mentioned embodiments of the substation three-phase balancing and regional load balancing method, another embodiment of the present application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the substation three-phase balancing and regional load balancing method of any embodiment of the present application is implemented.

[0132] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more module elements may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0133] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0134] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0135] Based on the above-mentioned method embodiments, another embodiment of the present application provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a substation three-phase balance and regional load balancing method described in any one of the above-mentioned method embodiments of the present application.

[0136] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium, etc.

Claims

1. A method for three-phase balance and regional load balancing in a substation, characterized in that: include: Counting the total energy consumption and the total number of users of each sub-area in the area to be planned, and selecting a first sub-area with an unbalanced load from each sub-area based on the total energy consumption and the total number of users; Acquiring and preprocessing three-phase current time series data of each first device in the first sub-area; the first device is a device whose energy consumption is greater than a preset energy consumption; screening out a second device with three-phase imbalance from the first device according to the preprocessed three-phase current time series data; A multi-objective optimization mathematical model is constructed using the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balance of the area to be planned as optimization objectives; Solve the multi-objective optimization mathematical model, and reallocate the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

2. A method for three-phase balancing and regional load balancing in a transformer area according to claim 1, characterized in that: The selecting, from the sub-areas according to the total energy consumption and the total number of users, a first sub-area with unbalanced loads, includes: The ratio of the total energy consumption to the total number of users is used as the energy consumption density of the corresponding sub-area; The sub-region with the highest energy consumption density is used as the second sub-region, and when the energy consumption density of the second sub-region is higher than the energy consumption density of other sub-regions by a first preset multiple, the second sub-region is used as the first sub-region.

3. The method for three-phase balancing and regional load balancing in a transformer area according to claim 1, characterized in that: The pre-processing of the three-phase current time series data of each first device in the first sub-area includes: screening a number of abnormal data points from the three-phase current time series data; Sliding a sliding window of a preset size on the three-phase current time series data to obtain data within the sliding window before and after each abnormal data point; And based on the data in the sliding window before and after the abnormal data point, weighted interpolation compensation is performed on the data at the abnormal data point.

4. The method for three-phase balancing and regional load balancing in a transformer area according to claim 1, characterized in that: The method of screening out the second device with three-phase imbalance from the first device according to the preprocessed three-phase current time series data includes: Calculating the average current of each phase corresponding to the first device according to the preprocessed three-phase current time series data; The first device that meets the preset conditions is used as the second device; wherein the preset conditions include: the average current of one phase is greater than the sum of the average currents of other phases by a second preset multiple.

5. The method for three-phase balancing and regional load balancing in a transformer area according to claim 1, characterized in that: The multi-objective optimization mathematical model is constructed by taking the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balance of the area to be planned as optimization objectives, including: constructing a first optimization objective according to the cost required when the second device is connected to the phase to be connected and the three-phase current data; constructing a second optimization objective according to the load difference between the sub-areas when the second device is in the sub-area where it is required to be located; The weighted sum of the first optimization objective and the second optimization objective is used as the optimization objective to construct the multi-objective optimization mathematical model.

6. A method for three-phase balancing and regional load balancing in a transformer area according to claim 5, characterized in that: The constructing a first optimization objective according to the cost required when the second device is connected to the phase to be connected and the three-phase current data includes: Min((max(I D,k )-min(I D,k )) / mean(I D,k )) 2 +x k,p ·C k,p Among them, Min represents the minimization optimization target; x k,p Indicates whether device k is assigned to phase p. The three-phase current after assignment is I D,k ;max(I D,k )、min(I D,k ) and mean(I D,k ) represent the maximum current, minimum current and average current of the three-phase transformer during the period when the device k is assigned to the phase p; C k,p is the cost of moving device k to phase p. If there is no charging station at phase p in a sub-area, then C k,p To infinity.

7. The method for three-phase balancing and regional load balancing in a transformer area according to claim 5, characterized in that: The constructing a second optimization objective according to the load difference between the sub-areas when the second device is in the sub-area where the second device is required to be located includes: Among them, Min represents the minimization optimization target; R represents the area to be planned; L r represents the total load of sub-region r; mean(L) represents the average load of the sub-region; x k,p Indicates whether device k is assigned to phase p; I k,p Represents the current value when device k is connected to phase p.

8. A three-phase balancing and regional load balancing device for a transformer area, characterized in that: include: a first sub-area screening module, a data processing module, a second device screening module, a model building module, and a model solving module; The first sub-region screening module is configured to count the total energy consumption and the total number of users of each sub-region in the area to be planned, and screen the first sub-region with unbalanced load from each sub-region based on the total energy consumption and the total number of users; The data processing module is used to obtain and pre-process the three-phase current time series data of each first device in the first sub-area; the first device is a device whose energy consumption is greater than a preset energy consumption; The second device screening module is configured to screen out second devices with three-phase imbalance from the first devices based on the preprocessed three-phase current time series data; The model building module is configured to construct a multi-objective optimization mathematical model using the phase to which each second device needs to be connected and the sub-area in which it needs to be located as decision variables, and taking the three-phase current balance of each second device and the load balancing of the area to be planned as optimization objectives; The model solving module is used to solve the multi-objective optimization mathematical model and reallocate the connection phase and the sub-area to which each second device belongs according to the solution result of the multi-objective optimization mathematical model.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method implements a substation three-phase balancing and regional load balancing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a substation three-phase balancing and regional load balancing method as described in any one of claims 1 to 7.

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