Intelligent power distribution method and system based on power distribution cabinet

Abstract

The invention relates to an intelligent power distribution method and system based on a power distribution cabinet, and relates to the field of intelligent power technologies, and the method comprises the steps: obtaining the region prediction demand of each region; determining the overall prediction demand according to the prediction demand of each region, and determining the core prediction demand and the residual power supply amount according to the region prediction demand of the core region when the overall prediction demand is greater than the upper limit energy supply amount; randomly selecting a non-core area combination to form a simulation power supply combination, and determining a common prediction demand according to the simulation power supply combination; defining the simulation power supply combination of which the common prediction demand is not greater than the residual available power supply quantity as a feasible power supply combination, and defining a selectable pause area according to the feasible power supply combination; and determining an actual power supply combination in the feasible power supply combinations, defining the selectable pause area corresponding to the actual power supply combination as an actual pause area, and controlling the power distribution cabinet to perform power supply pause operation on the actual pause area. The power distribution cabinet has the function of improving the overall electric energy distribution effect when the power distribution cabinet is used.

Description

Technical Field

[0001] This application relates to the field of smart power technology, and in particular to a smart power distribution method and system based on a distribution cabinet. Background Technology

[0002] Distribution cabinets are key equipment in power systems used for power distribution, control, measurement, and protection, and are widely used in various power consumption scenarios such as buildings, factories, and data centers. With the development of intelligent technology, modern distribution cabinets have gradually integrated data acquisition and communication modules, enabling real-time monitoring of electrical parameters such as circuit current, voltage, and power, laying the foundation for more refined energy management.

[0003] In the field of smart power distribution, demand management is a core function, aiming to control users' peak electricity demand to avoid high electricity bills due to exceeding contracted capacity. Current technologies commonly predict short-term electricity demand. When the predicted demand exceeds a set threshold, the system activates load control strategies, suspending or delaying some interruptible or shiftable non-critical loads. Typically, the system pre-sets a fixed priority strategy; for example, it classifies loads such as office lighting and air conditioning as low priority. When demand exceeds the limit, it prioritizes cutting power to these non-critical areas to ensure the continuous and stable operation of core loads such as production areas and data center server rooms.

[0004] In the aforementioned related technologies, the priority strategy is pre-set and basically does not change. Once the strategy is set, in subsequent operation, as long as the demand control condition is triggered, the system will mechanically and indiscriminately perform power-off operations according to the fixed order. That is, the highest priority area will definitely be powered off. However, under normal circumstances, there is no obvious order between non-core areas. That is, using the current priority strategy may lead to a poor power experience for users or appliances in some areas, which means that the power distribution effect of the entire system is poor and there is still room for improvement. Summary of the Invention

[0005] In order to improve the overall power distribution effect when using a power distribution cabinet, this application provides an intelligent power distribution method and system based on a power distribution cabinet.

[0006] Firstly, this application provides an intelligent power distribution method based on a distribution cabinet, employing the following technical solution: A smart power distribution method based on a distribution cabinet includes: Obtain the regional forecast demand for each region; The overall predicted demand is determined by summing the predicted demand of each region, and an adjustment signal is output when the overall predicted demand exceeds the preset upper limit of energy supply. After adjusting the signal output, the core predicted demand is determined by summing the regional predicted demand of the core area, and the remaining available power is determined by calculating the difference between the upper limit of power supply and the core predicted demand. Randomly select non-core areas from all non-core areas to form a simulated power supply combination, and determine the general forecast demand based on the simulated power supply combination; A simulated power supply combination in which the normal predicted demand is no greater than the remaining available power is defined as a feasible power supply combination, and a non-core area that is not in a feasible power supply combination is defined as an optional pause area. The actual power supply combination is determined from the feasible power supply combinations, and the optional suspension area corresponding to the actual power supply combination is defined as the actual suspension area. The control distribution cabinet performs power supply suspension operation on the actual suspension area.

[0007] Optionally, the steps for determining the actual power supply combination from the feasible power supply combinations include: Within a single feasible power supply combination, a priority coefficient is set based on the corresponding optional pause area; The number of demand pauses is determined by counting based on the selectable pause areas, and the quantity priority coefficient corresponding to the number of demand pauses is determined based on the preset quantity matching relationship; Construct a unit interval with a preset unit duration on a preset timeline, with the current time point as the endpoint, and obtain the actual power consumption of each selectable pause area within the unit interval; The average electricity consumption is determined by averaging all actual electricity consumption, and the electricity stability coefficient is determined by averaging the average electricity consumption and all actual electricity consumption. The stability priority coefficient corresponding to the power stability coefficient is determined based on the preset stability matching relationship. The combination selection coefficient is determined by calculating the set priority coefficient, quantity priority coefficient, and stability priority coefficient, and the feasible power supply combination corresponding to the largest combination selection coefficient is determined as the actual power supply combination.

[0008] Optionally, after the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Construct a historical interval with a preset historical duration on the timeline, using the current time point as the endpoint, and obtain the number of historical pauses under each selectable pause area; When the available pause area is the actual pause area in the historical interval, the pause time point is determined, and the most recent pause duration is determined based on the pause time point closest to the current time point and the current time point; The individual unit frequency coefficient is determined by calculating the number of historical pauses and the duration of the most recent pause, and the frequency priority coefficient is determined by calculating all individual unit frequency coefficients. The combined selection coefficient is updated based on the frequency priority coefficient and the combined selection coefficient.

[0009] Optionally, after the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Determine whether there exist at least two feasible power supply combinations with the same and largest selection coefficients; If there are no at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient shall be determined as the actual power supply combination. If there are at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient is defined as the alternative power supply combination. On the timeline, construct a subsequent interval with a preset subsequent duration and the current time point as the leading point. Determine the detection point in the subsequent interval to obtain the regional predicted demand, and define the regional predicted demand as the subsequent predicted demand. The detection interval is determined based on the detection point and the current time point, and the reasonable deviation coefficient corresponding to the detection interval is determined based on the preset deviation matching relationship. The adjusted forecast demand is determined by calculation based on the subsequent forecast demand and the reasonable deviation coefficient. Under the alternative power supply combination, the subsequent overall demand is determined by summing the adjusted predicted demand. The alternative power supply combination where the subsequent overall demand at each detection point is less than the corresponding remaining available power is determined as the actual power supply combination.

[0010] Optionally, it also includes a step for constructing the deviation matching relationship, which includes: Define the current actual electricity consumption in each region as the baseline electricity consumption, and define similar time points within the historical interval based on the baseline electricity consumption. Construct subsequent intervals at similar time points, and determine the subsequent deviation coefficient under the detection interval based on the actual electricity consumption of each detection point and the predicted demand for the subsequent sequence within each subsequent interval; Construct a similar deviation range based on the subsequent deviation coefficient and the preset similarity coefficient, and count the subsequent deviation coefficients within the similar deviation range to determine the number of ranges in the set. The deviation concentration range is defined as the range of similar deviations corresponding to the largest number of range concentrations, and the reasonable deviation coefficient under the detection interval is determined by calculating the subsequent deviation coefficient within the deviation concentration range.

[0011] Optionally, the step of calculating and determining the reasonable deviation coefficient for the detection interval based on the subsequent deviation coefficient within the deviation set range includes: The subsequent deviation coefficients within the deviation set range are defined as internal deviation coefficients, and the remaining subsequent deviation coefficients are defined as external deviation coefficients. A simulated deviation coefficient is randomly generated within the deviation set range, and the internal separation coefficient is determined based on the simulated deviation coefficient and the internal deviation coefficient, and the external separation coefficient is determined based on the simulated deviation coefficient and the external deviation coefficient. The representative effective coefficient is determined by calculating the internal separation coefficient, the preset internal main weight, the external separation coefficient, and the preset external secondary weight, and the simulation deviation coefficient corresponding to the largest representative effective coefficient is determined as the reasonable deviation coefficient.

[0012] Optionally, after the overall demand is determined, the intelligent power distribution method based on the distribution cabinet also includes: Determine if there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power. If there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power, then the actual power supply combination is determined from the corresponding alternative power supply combinations. If there is no alternative power supply combination where the overall demand in the subsequent sequence is less than the corresponding remaining available power at each detection point, then the detection point where the overall demand in the subsequent sequence is not less than the corresponding remaining available power is defined as the deviation point. The deviation interval duration is determined based on the deviation point and the current time point, and the alternative power supply combination corresponding to the largest deviation interval duration is determined as the actual power supply combination.

[0013] Secondly, this application provides an intelligent power distribution system based on a distribution cabinet, employing the following technical solution: An intelligent power distribution system based on a distribution cabinet includes: The acquisition module is used to obtain the regional forecast demand for each region. The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. The processing module calculates the total predicted demand based on the predicted demand of each region, and outputs an adjustment signal when the judgment module determines that the total predicted demand is greater than the preset upper limit of the power supply. After adjusting the signal output, the processing module calculates the core predicted demand by summing the regional predicted demand of the core area, and calculates the difference between the upper limit of power supply and the core predicted demand to determine the remaining available power. The processing module randomly selects non-core areas from all non-core areas to form a simulated power supply combination, and determines the general forecast demand based on the simulated power supply combination. The processing module defines simulated power supply combinations that are not greater than the remaining available power as feasible power supply combinations, and defines non-core areas that are not within feasible power supply combinations as optional pause areas. The processing module determines the actual power supply combination from the feasible power supply combinations, defines the optional pause area corresponding to the actual power supply combination as the actual pause area, and controls the power distribution cabinet to perform a power supply pause operation on the actual pause area.

[0014] In summary, this application includes at least one of the following beneficial technical effects: When it is predicted that the power demand of all areas cannot be met, the non-core areas that need to be shut down can be analyzed to select the most suitable non-core areas for power outages, thereby reducing the likelihood of frequent power outages in a single area and improving the overall power distribution effect when the distribution cabinet is in use. When analyzing non-core areas, comprehensive consideration of data from various aspects is taken into account to improve the accuracy of data analysis, thereby improving the overall effectiveness of the distribution cabinet. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent power distribution method based on a distribution cabinet.

[0016] Figure 2 This is a module flowchart of an intelligent power distribution method based on a distribution cabinet. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an intelligent power distribution method based on a power distribution cabinet, referring to... Figure 1 The method flow of the intelligent power distribution method based on the distribution cabinet includes the following steps: Step S100: Obtain the regional forecast demand for each region.

[0020] Regional demand forecast refers to the power required by each area to be supplied by the distribution cabinet in the next time period, as predicted by the forecasting algorithm. The forecasting algorithm is a conventional algorithm for demand forecasting in this field, and will not be described in detail in this application.

[0021] Step S101: Sum the predicted demand of each region to determine the overall predicted demand, and output an adjustment signal when the overall predicted demand is greater than the preset upper limit of the power supply.

[0022] The overall predicted demand is the total amount of electricity required in the next period, which is determined by adding the predicted demand of all areas. The upper limit of the power supply is the maximum power that the current distribution cabinet can provide, which is also the value of the signed contract. When the overall predicted demand is greater than the upper limit of the power supply, it indicates that there will be a power shortage in the next period. Therefore, in order to maintain the normal use of the core area, it is necessary to conduct a power outage analysis on the non-core area. Therefore, an adjustment signal is output to mark this situation for subsequent analysis.

[0023] Step S102: After adjusting the signal output, summate the predicted demand of the core area to determine the core predicted demand, and calculate the difference between the upper limit of power supply and the core predicted demand to determine the remaining available power.

[0024] The core forecast demand is the amount of electricity required by all core areas, obtained by adding up the regional forecast demands of all core areas pre-defined by the user. The remaining available electricity can be supplied to non-core areas, determined by subtracting the core forecast demand from the upper limit of available electricity.

[0025] Step S103: Randomly select non-core areas from all non-core areas to form a simulated power supply combination, and determine the general forecast demand based on the simulated power supply combination.

[0026] The simulated power supply combination is a combination of non-core areas selected and supplied with power. The general forecast demand is the power value required by the selected non-core areas, which is obtained by adding the regional forecast demand of each non-core area in the simulated power supply combination.

[0027] Step S104: Define the simulated power supply combination where the normal predicted demand is no greater than the remaining available power as a feasible power supply combination, and define the non-core area that is not in the feasible power supply combination as an optional pause area.

[0028] When the normal predicted demand is not greater than the remaining available power, it means that the non-core areas within the current simulated power supply combination can be powered normally. Therefore, optional power supply combinations are defined to distinguish different simulated power supply combinations for subsequent analysis. At this time, optional pause areas are defined to identify the non-core areas that need to be paused under the feasible power supply combination, so as to facilitate the power distribution cabinet to allocate power.

[0029] Step S105: Determine the actual power supply combination from the feasible power supply combinations, define the optional pause area corresponding to the actual power supply combination as the actual pause area, and control the power distribution cabinet to perform a power supply pause operation on the actual pause area.

[0030] The actual power supply combination is a unique value among all feasible power supply combinations. It can be randomly selected or selected through the method of steps S200-S205. At this time, the actual pause area is defined to mark the area that needs to be paused in the next time period. This allows for better control of the power distribution cabinet to perform power pause operations on the actual pause area. This method can avoid the occurrence of frequent power pauses in a single area, alleviate the situation of poor user or appliance experience in a single area, and thus improve the overall distribution effect of the power distribution cabinet.

[0031] The steps for determining the actual power supply combination from the feasible power supply combinations include: Step S200: Within a single feasible power supply combination, obtain the set priority coefficient according to the corresponding optional pause area.

[0032] The priority coefficient is set by the staff to determine the priority coefficient of the optional pause area as the actual pause area. It is determined by the staff or users based on the specific power consumption of each non-core area. For example, equipment such as air conditioners can be turned off at any time, while equipment such as computers will be damaged if they are turned off at any time. Therefore, the power supply to the area where the air conditioner is located will be set to a higher priority coefficient than that of the area where the computer is located.

[0033] Step S201: Count according to the selectable pause area to determine the number of demand pauses, and determine the quantity priority coefficient corresponding to the number of demand pauses according to the preset quantity matching relationship.

[0034] The demand suspension quantity refers to the number of selectable suspension areas corresponding to the determined feasible power supply combination, that is, the number of areas that need to be temporarily suspended. The quantity priority coefficient is a coefficient value that reflects the impact of the demand suspension quantity on the selection of feasible power supply combinations. Under different demand suspension quantities, the corresponding quantity priority coefficient is also different. The quantity matching relationship between the two is determined by the staff in advance through multiple tests. It is necessary to ensure that the larger the demand suspension quantity, the smaller the corresponding quantity priority coefficient.

[0035] Step S202: Construct a unit interval with a preset unit duration on the preset time axis with the current time point as the end point, and obtain the actual power consumption of each selectable pause area within the unit interval.

[0036] The time axis is a coordinate axis formed by combining various time points. This coordinate axis points from the time points that have already passed to the time points that have not yet been reached. The time points that have already passed are on the left side of the coordinate axis, and the left side of the coordinate axis is defined as the front side of the time axis. The unit duration is a fixed duration set by the staff. This duration can reflect the power consumption of each area in a short period of time. The unit interval is constructed to facilitate the acquisition of data within the unit duration. The actual power consumption is the actual power consumption obtained from the data collection points (i.e., detection points) of each distribution cabinet in the selectable pause area within the unit interval.

[0037] Step S203: Calculate the average electricity consumption based on the average of all actual electricity consumption to determine the average electricity consumption, and calculate the electricity stability coefficient based on the average electricity consumption and all actual electricity consumption.

[0038] The average electricity consumption is the average of all actual electricity consumption at each detection point in a single selectable pause area within a unit interval. The electricity stability coefficient is a value that reflects the stability of electricity consumption in the current area. The larger the value, the more stable the electricity consumption. It can be determined by calculating the difference between the average electricity consumption and the actual electricity consumption, summing the absolute values ​​and taking the reciprocal.

[0039] Step S204: Determine the stability priority coefficient corresponding to the power stability coefficient according to the preset stability matching relationship.

[0040] The stability priority coefficient is a parameter value that reflects the impact of power stability on the selection of actual power supply combination. The larger the power stability coefficient, the more stable the power situation, which means that the impact of suspending other uses will be greater. Therefore, the smaller the corresponding stability priority coefficient, the stability matching relationship between the two is determined in advance by the staff.

[0041] Step S205: Calculate and determine the combination selection coefficient based on the set priority coefficient, quantity priority coefficient and stability priority coefficient, and determine the actual power supply combination from the feasible power supply combination corresponding to the largest combination selection coefficient.

[0042] The combination selection coefficient is a feasibility factor value for selecting feasible power supply combinations as actual power supply combinations. It is determined by the sum of all set priority coefficients divided by the product of the quantity priority coefficient and the stability priority coefficient. The larger the value, the more reasonable it is to use the corresponding optional pause area in the selected combination as the actual pause area. Therefore, the corresponding feasible power supply combination can be determined as the actual power supply combination.

[0043] After the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Step S300: Construct a historical interval with a preset historical duration on the timeline, using the current time point as the endpoint, and obtain the number of historical pauses under each selectable pause area.

[0044] The historical duration is the time period set by staff for acquiring historical electricity consumption data for each area. Historical intervals are constructed to facilitate the acquisition and analysis of data within the historical duration. The number of historical pauses is the number of times power supply was suspended in the selectable pause areas within the historical interval.

[0045] Step S301: Determine the pause time point in the historical interval when the selectable pause area is the actual pause area, and determine the most recent pause duration based on the pause time point closest to the current time point and the current time point.

[0046] The pause time point is the time point when the power supply interruption operation occurs in the currently determined selectable pause area in the historical interval. The most recent pause duration is the interval between the most recent pause time point and the current time point, which is also the interval between the current selectable pause area and the last power supply interruption operation.

[0047] Step S302: Calculate the individual unit frequency coefficient based on the historical number of pauses and the most recent pause duration, and calculate the frequency priority coefficient based on all individual unit frequency coefficients.

[0048] The individual frequency coefficient is a coefficient value reflecting the frequency of a single selectable pause region for power supply temporary operation, derived from... Calculated, where For individual frequency coefficients, For the number of historical pauses, For the most recent pause duration, The weighting coefficient for the number of historical pauses. The weighting coefficient for the most recent pause duration is calculated by... as well as The setting enables synchronization of two data sets; the frequency priority coefficient is the average of all individual frequency coefficients.

[0049] Step S303: Calculate and update the combined selection coefficient based on the frequency priority coefficient and the combined selection coefficient.

[0050] By subtracting the frequency priority coefficient from the combination selection coefficient, the power outage frequency data of each region can be taken into account, thereby updating the combination selection coefficient and improving the accuracy of actual power supply combination selection.

[0051] After the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Step S400: Determine whether there are at least two feasible power supply combinations with the same and largest combination selection coefficient.

[0052] The purpose of this determination is to ascertain whether there are multiple acceptable power supply combinations that meet the requirements, so as to identify the unique and actual power supply combination.

[0053] Step S4001: If there are no at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient shall be determined as the actual power supply combination.

[0054] When there are no at least two feasible power supply combinations with the same and largest selection coefficients, it means that there is only one eligible power supply combination, which can then be defined as the actual power supply combination.

[0055] Step S4002: If there are at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient is defined as the alternative power supply combination.

[0056] When there are at least two feasible power supply combinations with the same and largest selection coefficient, it indicates that there are multiple feasible power supply combinations that meet the requirements. These are defined as alternative power supply combinations to distinguish between different feasible power supply combinations and facilitate subsequent analysis.

[0057] Step S401: Construct a subsequent interval with a preset subsequent duration on the time axis, using the current time point as the leading point, and determine the detection point in the subsequent interval to obtain the regional predicted demand, and define the regional predicted demand as the subsequent predicted demand.

[0058] The subsequent duration is the duration of at least three detection periods set by the staff, meaning that at least three demand predictions need to be made within this subsequent duration. The subsequent interval is constructed to facilitate the acquisition and analysis of data within the subsequent duration. The subsequent predicted demand is defined to distinguish the regional predicted demand at each detection point at the current time point, which is convenient for subsequent analysis.

[0059] Step S402: Determine the detection interval based on the detection point and the current time point, and determine the reasonable deviation coefficient corresponding to the detection interval based on the preset deviation matching relationship.

[0060] The detection interval is the time interval between the detection point and the current time point. Due to the different intervals, the error caused by the prediction based on the data at the current time point will also have different deviations. The reasonable deviation coefficient is the maximum deviation that will occur under theoretical conditions. The deviation matching relationship between the two is determined in advance by the staff. It can be determined by multiple experiments or by the method in steps S500-S503.

[0061] Step S403: Calculate and determine the adjusted forecast demand based on the subsequent forecast demand and the reasonable deviation coefficient.

[0062] By multiplying the subsequent predicted demand by the reasonable deviation coefficient and then adding the subsequent predicted demand, we can obtain the maximum demand that the detection point would have under theoretical conditions, which is to adjust the predicted demand.

[0063] Step S404: Under the alternative power supply combination, the adjusted predicted demand is summed to determine the subsequent overall demand, and the alternative power supply combination where the subsequent overall demand at each detection point is less than the corresponding remaining available power is determined as the actual power supply combination.

[0064] The subsequent overall demand is the total power demand that will appear at each detection point under the alternative power supply combination. When the subsequent overall demand at each detection point is less than the corresponding remaining available power, it means that the power supply requirements can be met under the current alternative power supply combination. Therefore, it is unlikely that there will be a situation where it is necessary to switch to different areas to suspend power supply. Therefore, the corresponding alternative power supply combination can be determined as the actual power supply combination. When there are multiple alternative power supply combinations of this type, they can be randomly selected. If there is no alternative power supply combination of this type, the method of steps S700-S701 is used for further analysis and determination.

[0065] It also includes the step of constructing the deviation matching relationship, which includes: Step S500: Define the actual electricity consumption of each region as the baseline electricity consumption, and define similar time points within the historical interval based on the baseline electricity consumption.

[0066] By defining a baseline electricity consumption and similar time points, different data can be identified and distinguished to facilitate subsequent analysis. The similar time point is the time point in the same area where the actual electricity consumption is consistent with the current baseline electricity consumption.

[0067] Step S501: Construct subsequent intervals at similar time points, and determine the subsequent deviation coefficient under the detection interval based on the actual power consumption at each detection point and the predicted demand for the subsequent interval.

[0068] The subsequent deviation coefficient is the deviation between the actual electricity consumption and the corresponding subsequent predicted demand; this value is an absolute value.

[0069] Step S502: Construct a similar deviation range based on the subsequent deviation coefficient and the preset similarity coefficient, and count the subsequent deviation coefficients within the similar deviation range to determine the number of ranges in the set.

[0070] The similarity coefficient is the maximum difference allowed when two subsequent deviation coefficients are considered to be relatively close, as set by the staff. By adding and subtracting the similarity coefficient from the subsequent deviation coefficients, a similar deviation range can be constructed. The number of ranges is the number of subsequent deviation coefficients that are within the similar deviation range, which is also the number of other subsequent deviation coefficients that are relatively close to the subsequent deviation coefficients that construct the current similar deviation range.

[0071] Step S503: Define the range of similar deviations corresponding to the largest range concentration as the deviation concentration range, and calculate the reasonable deviation coefficient under the detection interval based on the subsequent deviation coefficient within the deviation concentration range.

[0072] Define the deviation concentration range to distinguish the deviation range where the most frequently occurring subsequent deviation coefficients are located. This means that the data in this range are deviation values ​​that would occur under normal circumstances. At this time, the subsequent deviation coefficients in this range are used to calculate and determine the reasonable deviation coefficient under the detection interval. The reasonable deviation coefficient can be determined by calculating the average of all subsequent deviation coefficients, or it can be determined by steps S600-S602.

[0073] The steps for calculating the reasonable deviation coefficient for the detection interval based on the subsequent deviation coefficient within the deviation set range include: Step S600: Define the subsequent deviation coefficients within the deviation set range as internal deviation coefficients, and define the remaining subsequent deviation coefficients as external deviation coefficients.

[0074] By defining internal and external deviation coefficients, different subsequent deviation coefficients can be distinguished, which facilitates subsequent analysis.

[0075] Step S601: Randomly generate a simulated deviation coefficient within the deviation set range, and determine the internal separation coefficient based on the simulated deviation coefficient and the internal deviation coefficient, and determine the external separation coefficient based on the simulated deviation coefficient and the external deviation coefficient.

[0076] By randomly selecting the simulation deviation coefficients, the data can be analyzed. The internal separation coefficient is the difference between the simulation deviation coefficient and the internal deviation coefficient, and the external separation coefficient is the difference between the simulation deviation coefficient and the external deviation coefficient. Both of these differences are absolute values.

[0077] Step S602: Calculate the representative effective coefficient based on the internal separation coefficient, the preset internal main weight, the external separation coefficient, and the preset external secondary weight, and determine the simulation deviation coefficient corresponding to the largest representative effective coefficient as the reasonable deviation coefficient.

[0078] The internal primary weight reflects the influence of the internal separation coefficient on the reasonable deviation coefficient, while the external secondary weight reflects the influence of the external separation coefficient on the reasonable deviation coefficient. The representative effective coefficient is obtained by multiplying all internal separation coefficients by the internal primary weight and adding all external separation coefficients by the external secondary weight. This value reflects the reasonable and feasible value of the simulation deviation coefficient as a reasonable deviation coefficient. The larger the value, the more reasonable it is. Therefore, the simulation deviation coefficient corresponding to the largest representative effective coefficient is determined as the reasonable deviation coefficient.

[0079] After the overall demand is determined, the intelligent power distribution method based on the distribution cabinet also includes: Step S700: Determine whether there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power.

[0080] The purpose of the judgment is to determine whether there are alternative power supply combinations that meet the requirements.

[0081] Step S7001: If there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power, then the actual power supply combination is determined from the corresponding alternative power supply combinations.

[0082] When there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power, it indicates that there are alternative power supply combinations that meet the requirements. In this case, it can be determined as the actual power supply combination.

[0083] Step S7002: If there is no alternative power supply combination where the overall demand of the subsequent sequence is less than the corresponding remaining available power at each detection point, then the detection point where the overall demand of the subsequent sequence is not less than the corresponding remaining available power is defined as the deviation point.

[0084] When there is no alternative power supply combination where the overall demand at each detection point is less than the corresponding remaining available power, it indicates that there is no alternative power supply combination that meets the requirements, and therefore further analysis is needed. By defining deviation points, we can distinguish the detection points that cannot meet the demand requirements, which will facilitate subsequent analysis.

[0085] Step S701: Determine the deviation interval duration based on the deviation point and the current time point, and determine the alternative power supply combination corresponding to the largest deviation interval duration as the actual power supply combination.

[0086] The deviation interval duration is the time interval between the deviation point and the current time point. The maximum deviation interval duration indicates that the current alternative power supply combination can maintain the power outage area unchanged for the longest time, so it has the best effect. At this time, the corresponding alternative power supply combination can be determined as the actual power supply combination.

[0087] Reference Figure 2Based on the same inventive concept, embodiments of the present invention provide an intelligent power distribution system based on a power distribution cabinet, comprising: The acquisition module is used to obtain the regional forecast demand for each region. The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. The processing module calculates the total predicted demand based on the predicted demand of each region, and outputs an adjustment signal when the judgment module determines that the total predicted demand is greater than the preset upper limit of the power supply. After adjusting the signal output, the processing module calculates the core predicted demand by summing the regional predicted demand of the core area, and calculates the difference between the upper limit of power supply and the core predicted demand to determine the remaining available power. The processing module randomly selects non-core areas from all non-core areas to form a simulated power supply combination, and determines the general forecast demand based on the simulated power supply combination. The processing module defines simulated power supply combinations that are not greater than the remaining available power as feasible power supply combinations, and defines non-core areas that are not within feasible power supply combinations as optional pause areas. The processing module determines the actual power supply combination from the feasible power supply combinations, defines the optional pause area corresponding to the actual power supply combination as the actual pause area, and controls the power distribution cabinet to perform power supply pause operation on the actual pause area. The actual power supply combination determination module is used to determine the actual power supply combination from the feasible power supply combinations; The combined selection coefficient update module updates the corresponding combined selection coefficients based on the historical power outage situation in each non-core area. The feasible power supply combination screening module is used to select a unique actual power supply combination from multiple feasible power supply combinations that meet the requirements. The deviation matching relationship construction module constructs deviation matching relationships based on historical data; The reasonable deviation coefficient calculation module is used to calculate a more accurate reasonable deviation coefficient; The "No Alternative Analysis Module" analyzes and processes cases where no alternative power supply combinations exist.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart power distribution method based on a distribution cabinet, characterized in that, include: Obtain the regional forecast demand for each region; The overall predicted demand is determined by summing the predicted demand of each region, and an adjustment signal is output when the overall predicted demand exceeds the preset upper limit of energy supply. After adjusting the signal output, the core predicted demand is determined by summing the regional predicted demand of the core area, and the remaining available power is determined by calculating the difference between the upper limit of power supply and the core predicted demand. Randomly select non-core areas from all non-core areas to form a simulated power supply combination, and determine the general forecast demand based on the simulated power supply combination; A simulated power supply combination in which the normal predicted demand is no greater than the remaining available power is defined as a feasible power supply combination, and a non-core area that is not in a feasible power supply combination is defined as an optional pause area. The actual power supply combination is determined from the feasible power supply combinations, and the optional suspension area corresponding to the actual power supply combination is defined as the actual suspension area. The control distribution cabinet performs power supply suspension operation on the actual suspension area.

2. The intelligent power distribution method based on a distribution cabinet according to claim 1, characterized in that, The steps for determining the actual power supply combination from the feasible power supply combinations include: Within a single feasible power supply combination, a priority coefficient is set based on the corresponding optional pause area; The number of demand pauses is determined by counting based on the selectable pause areas, and the quantity priority coefficient corresponding to the number of demand pauses is determined based on the preset quantity matching relationship; Construct a unit interval with a preset unit duration on a preset timeline, with the current time point as the endpoint, and obtain the actual power consumption of each selectable pause area within the unit interval; The average electricity consumption is determined by averaging all actual electricity consumption, and the electricity stability coefficient is determined by averaging the average electricity consumption and all actual electricity consumption. The stability priority coefficient corresponding to the power stability coefficient is determined based on the preset stability matching relationship. The combination selection coefficient is determined by calculating the set priority coefficient, quantity priority coefficient, and stability priority coefficient, and the feasible power supply combination corresponding to the largest combination selection coefficient is determined as the actual power supply combination.

3. The intelligent power distribution method based on a distribution cabinet according to claim 2, characterized in that, After the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Construct a historical interval with a preset historical duration on the timeline, using the current time point as the endpoint, and obtain the number of historical pauses under each selectable pause area; When the available pause area is the actual pause area in the historical interval, the pause time point is determined, and the most recent pause duration is determined based on the pause time point closest to the current time point and the current time point; The individual unit frequency coefficient is determined by calculating the number of historical pauses and the duration of the most recent pause, and the frequency priority coefficient is determined by calculating all individual unit frequency coefficients. The combined selection coefficient is updated based on the frequency priority coefficient and the combined selection coefficient.

4. The intelligent power distribution method based on a distribution cabinet according to claim 3, characterized in that, After the combination selection coefficients are determined, the intelligent power distribution method based on the distribution cabinet also includes: Determine whether there exist at least two feasible power supply combinations with the same and largest selection coefficients; If there are no at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient shall be determined as the actual power supply combination. If there are at least two feasible power supply combinations with the same and largest combination selection coefficient, then the feasible power supply combination corresponding to the largest combination selection coefficient is defined as the alternative power supply combination. On the timeline, construct a subsequent interval with a preset subsequent duration and the current time point as the leading point. Determine the detection point in the subsequent interval to obtain the regional predicted demand, and define the regional predicted demand as the subsequent predicted demand. The detection interval is determined based on the detection point and the current time point, and the reasonable deviation coefficient corresponding to the detection interval is determined based on the preset deviation matching relationship. The adjusted forecast demand is determined by calculation based on the subsequent forecast demand and the reasonable deviation coefficient. Under the alternative power supply combination, the subsequent overall demand is determined by summing the adjusted predicted demand. The alternative power supply combination where the subsequent overall demand at each detection point is less than the corresponding remaining available power is determined as the actual power supply combination.

5. The intelligent power distribution method based on a distribution cabinet according to claim 4, characterized in that, It also includes the step of constructing the deviation matching relationship, which includes: Define the current actual electricity consumption in each region as the baseline electricity consumption, and define similar time points within the historical interval based on the baseline electricity consumption. Construct subsequent intervals at similar time points, and determine the subsequent deviation coefficient under the detection interval based on the actual electricity consumption of each detection point and the predicted demand for the subsequent sequence within each subsequent interval; Construct a similar deviation range based on the subsequent deviation coefficient and the preset similarity coefficient, and count the subsequent deviation coefficients within the similar deviation range to determine the number of ranges in the set. The deviation range corresponding to the largest number of ranges is defined as the deviation range, and the reasonable deviation coefficient under the detection interval is determined by calculating the subsequent deviation coefficient within the deviation range.

6. The intelligent power distribution method based on a distribution cabinet according to claim 5, characterized in that, The steps for calculating the reasonable deviation coefficient for the detection interval based on the subsequent deviation coefficient within the deviation set range include: The subsequent deviation coefficients within the deviation set range are defined as internal deviation coefficients, and the remaining subsequent deviation coefficients are defined as external deviation coefficients. A simulated deviation coefficient is randomly generated within the deviation set range, and the internal separation coefficient is determined based on the simulated deviation coefficient and the internal deviation coefficient, and the external separation coefficient is determined based on the simulated deviation coefficient and the external deviation coefficient. The representative effective coefficient is determined by calculating the internal separation coefficient, the preset internal main weight, the external separation coefficient, and the preset external secondary weight, and the simulation deviation coefficient corresponding to the largest representative effective coefficient is determined as the reasonable deviation coefficient.

7. The intelligent power distribution method based on a distribution cabinet according to claim 4, characterized in that, After the overall demand is determined, the intelligent power distribution method based on the distribution cabinet also includes: Determine if there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power. If there are alternative power supply combinations where the overall demand at each detection point is less than the corresponding remaining available power, then the actual power supply combination is determined from the corresponding alternative power supply combinations. If there is no alternative power supply combination where the overall demand in the subsequent sequence is less than the corresponding remaining available power at each detection point, then the detection point where the overall demand in the subsequent sequence is not less than the corresponding remaining available power is defined as the deviation point. The deviation interval duration is determined based on the deviation point and the current time point, and the alternative power supply combination corresponding to the largest deviation interval duration is determined as the actual power supply combination.

8. An intelligent power distribution system based on a distribution cabinet, characterized in that, include: The acquisition module is used to obtain the regional forecast demand for each region. The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. The processing module calculates the total predicted demand based on the predicted demand of each region, and outputs an adjustment signal when the judgment module determines that the total predicted demand is greater than the preset upper limit of the power supply. After adjusting the signal output, the processing module calculates the core predicted demand by summing the regional predicted demand of the core area, and calculates the difference between the upper limit of power supply and the core predicted demand to determine the remaining available power. The processing module randomly selects non-core areas from all non-core areas to form a simulated power supply combination, and determines the general forecast demand based on the simulated power supply combination. The processing module defines simulated power supply combinations that are not greater than the remaining available power as feasible power supply combinations, and defines non-core areas that are not within feasible power supply combinations as optional pause areas. The processing module determines the actual power supply combination from the feasible power supply combinations, defines the optional pause area corresponding to the actual power supply combination as the actual pause area, and controls the power distribution cabinet to perform a power supply pause operation on the actual pause area.

Images