An intelligent distribution network dispatching load control auxiliary decision method and system

Abstract

This invention provides a control-aided decision-making method and system for intelligent distribution network load dispatching, relating to the field of power system dispatching. This invention divides charging areas and units according to power access points, constructs a topology structure, and monitors charging status in real time. It dynamically collects the power curves of units during charging, vehicle battery level, and maximum power output from power sources, and calculates charging time based on battery capacity parameters. Based on remaining power gaps, real-time power ratios, and power change trends, it constructs and normalizes a dynamic priority scoring model. A hierarchical power allocation strategy is adopted: first, total power is allocated according to area priority and charging unit occupancy ratio; then, initial power is allocated within the area according to unit scores. Over-limit power is constrained and corrected; excess power is cyclically allocated to non-over-limit units according to priority. Finally, the corrected power command is issued in real time via the MQTT protocol. This invention ensures safe grid operation and improves charging efficiency and resource utilization.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching, specifically to a control-aid decision-making method and system for intelligent distribution network dispatching loads. Background Technology

[0002] With the large-scale integration of electric vehicles, power distribution network dispatching faces severe challenges. This is especially true for dedicated charging station scenarios such as bus hubs and logistics parks, where load control of charging pile clusters with uniform rated power is crucial. Existing charging pile dispatching methods generally employ flat device list management, failing to reflect the hierarchical relationship between power access points and charging piles. This results in difficulty in quickly locating associated charging piles under high-load power sources during grid dispatching, leading to significant response delays. Load demand characteristics are ignored, and power allocation is disconnected from the physical network structure, with high-occupancy charging units having significantly higher power demands than low-occupancy units. Furthermore, power allocation methods for charging pile clusters often employ static thresholds or simple polling strategies, failing to dynamically identify the varying charging urgency of different vehicles. For example, the demand from low-battery vehicles is mixed with that of vehicles nearing full charge, causing charging delays for high-priority vehicles during peak grid load periods, leading to user complaints. Moreover, the lack of a real-time correction mechanism for device-level power constraints means that insufficient power allocation in high-density areas can create localized overload risks. Power over-limit units require manual intervention, with delayed responses impacting system safety.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a control-aid decision-making method and system for intelligent distribution network scheduling load, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A control-aided decision-making method for intelligent distribution network load scheduling, comprising the following steps:

[0007] S1: Based on the power access point of the charging pile, divide the charging piles within the same power access point into a network, construct a charging pile network topology structure containing regions and units, each unit corresponds to one charging pile, each region corresponds to one charging station, and mark the regions and units, and determine the working status of the unit based on whether it is charging.

[0008] S2: Based on the judgment results, select the charging unit, obtain the maximum power supply of the power access point and the power curve of the charging unit during this charging, as well as the real-time power and maximum power of the corresponding charging vehicle, and identify the power curve to obtain the real-time power, maximum power and charging time of the charging unit during this charging.

[0009] S3: Based on the real-time identification of the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit, combined with the power curve change trend, the current charging urgency of each charging unit is evaluated, and a corresponding priority score is assigned to measure its current demand for power resources.

[0010] S4: Based on the region and unit labels, prioritize the region as the unit. According to the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit, use a multi-objective allocation function to allocate the total power of the power access point at the region level. Then, based on the priority score of each unit in the region, allocate the power at the unit level and output the initial value of the target allocated power for each unit.

[0011] S5: Perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, constrain it to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power.

[0012] Furthermore, the power access point coordinates of all charging piles are obtained in real time from the charging pile management system. Each access point corresponds to an independent power source. All charging piles under the same power access point are divided into a region, representing a charging station, and a unique region ID is assigned to each region. Each charging station is treated as a unit and assigned a unique unit ID. Use a graph data structure to store the topology, with regions as parent nodes and cells as child nodes. Output a topology table containing the fields: Region ID and Cell ID. Poll the working status of each cell every five seconds. If the current value of the cell is greater than 0, mark the status as "charging"; otherwise, mark it as "idle". Output the updated topology table with the working status.

[0013] Furthermore, from the topology table, all units in the charging state are extracted to form a charging unit list. The following data are read in real time: the maximum power supply of the power access point, the charging power curve of each charging unit, the real-time and maximum power of the corresponding charging vehicle, and a moving average filter is applied to the power curve of each charging unit to remove high-frequency noise, thus obtaining the charging power curve after eliminating high-frequency noise. Then, a linear regression model is used to identify... Key parameters include: real-time power of the charging unit, peak power of the charging unit, average charging power, real-time battery level of the vehicle being charged, battery capacity of the vehicle, and estimated charging time.

[0014] Furthermore, based on the data obtained from S2, the urgency of each charging unit is assessed and assigned a priority score. The first derivative is used to calculate the changing trend of the power curve of each charging unit. A weighted scoring method is adopted to construct a priority scoring function, as shown in the following formula:

[0015]

[0016] in, For charging unit Priority score for The real-time battery level of the vehicle being charged by the charging unit For charging unit The maximum battery capacity of the vehicle being charged For charging unit Real-time power output during this charging process. For charging unit Peak power during this charging process, This represents the power trend value of the charging unit. This represents the system's maximum trend value. Let be the weight coefficient, and satisfy... Store the calculated values ​​in the list of charging units.

[0017] Furthermore, normalization The formula for scores from 0 to 100 is as follows:

[0018]

[0019] in, For charging unit Normalized score The minimum score among all charging units. To find the maximum Score among all charging units, add a Score field for each charging unit, store the calculated value in the field, and output the updated value containing the maximum Score. List of charging units.

[0020] Furthermore, in S4, region aggregation is first performed. For each region, the formula for calculating the total region priority and charging unit occupancy ratio is as follows:

[0021]

[0022]

[0023] in, for Regional overall priority for area Priority score of charging units, express The total number of charging units in the area for The number of units currently in operation in the region. for Number of units in the region for The occupancy ratio of the regional charging units; using a multi-objective allocation function, the total power is allocated to each region, and the allocation formula is as follows:

[0024]

[0025] in, To be assigned The power of the region, This is the maximum power supply capacity. The total number of all regions. The index is used in the summation formula; it is assigned based on cell priority within each region, using the following formula:

[0026]

[0027] in, Allocate initial power to the units; output a list of charging units and add new ones. fields, where This is the index in the summation formula.

[0028] Furthermore, constraint correction is applied to the output of S4, and each charging unit is checked. Does it exceed the unit's maximum power threshold? ,like ,but ;like ,but And record excess power : ;in For final power allocation.

[0029] Furthermore, the total excess power is calculated using the following formula:

[0030]

[0031] in, For charging unit Excess power For total excess power, Given the total number of charging units, the total excess power is redistributed to other units that have not exceeded the limit according to their relative priority.

[0032]

[0033]

[0034] in, To allocate to units that have not exceeded limits Additional power increment, The set of all non-overlimited units, Generate charging control commands for the total number of units that have not exceeded the limit. To ultimately allocate power, instructions are sent in real time to the controllers of each charging unit via the MQTT protocol to perform power adjustments.

[0035] The present invention also provides a control-aid decision-making system for intelligent distribution network dispatch load, wherein the control-aid decision-making system for intelligent distribution network dispatch load is used in the above-mentioned control-aid decision-making method for intelligent distribution network dispatch load, and includes:

[0036] The topology construction module is used to divide the charging piles within the same power access point into a network based on the power access point of the charging pile, and construct a charging pile network topology containing regions and units. Each unit corresponds to one charging pile, and each region corresponds to one charging station. The region and unit are marked, and the working status of the unit is determined based on whether it is charging.

[0037] The data acquisition module is used to filter out charging units based on the judgment results, and to obtain the maximum power supply of the power access point, the power curve of the charging unit during this charging, and the real-time and maximum power of the corresponding charging vehicle in real time. The power curve is used to identify and obtain the real-time power, maximum power and charging time of the charging unit during this charging.

[0038] The priority scoring module is used to evaluate the current charging urgency of each charging unit based on the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit identified in real time, combined with the power curve change trend, and assign a corresponding priority score to measure its current demand for power resources.

[0039] The power allocation module is used to allocate the total power of the power access point at the region level based on the region and unit labels, prioritizing the region as the unit. It then uses a multi-objective allocation function to allocate the total power at the region level based on the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit. Finally, it allocates the power at the unit level based on the priority score of each unit in the region and outputs the initial value of the target allocated power for each unit.

[0040] The power correction and regulation module is used to perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, it is constrained to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power.

[0041] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on the power access point, regions and units are divided, a tree-like topology is constructed and its status is mapped in real time, ensuring strict alignment of power allocation with the physical structure of the power grid and improving the efficiency of regional load identification. Furthermore, based on the remaining power gap, real-time power ratio, and power change trend, a dynamic priority scoring model is constructed and normalized to dynamically distinguish the charging urgency of vehicles, granting higher power allocation rights to vehicles with low power, thus shortening the overall charging completion time. A hierarchical power allocation strategy is adopted, first allocating total power according to regional priority and charging unit occupancy ratio, and then allocating initial power within the region according to unit scoring. A dual-factor allocation function of regional aggregation priority and power unit occupancy ratio is introduced to increase the power quota in high-demand dense areas, avoiding local overload. In addition, excess power is constrained and corrected, with surplus power cyclically allocated to non-exceeding units according to priority. Through excess power constraint correction and priority-based cyclic redistribution mechanism, it is ensured that unit power does not exceed limits and the total power of the power supply point does not exceed capacity, reducing the failure rate, and the entire process is automated without manual intervention. Finally, the corrected power command is issued in real time via the MQTT protocol. This invention ensures the safe operation of the power grid and improves charging efficiency and resource utilization. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0043] Figure 2 Here are the SOC data diagrams for each charging unit in the embodiment;

[0044] Figure 3 Here is a graph showing the score data for each charging unit in the embodiment;

[0045] Figure 4 This is a diagram showing the power distribution data of each charging unit before and after constraint correction in the embodiment.

[0046] Figure 5 This is a schematic block diagram of the overall system modules of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] Please see Figure 1-4 The present invention provides a technical solution:

[0051] A control-aided decision-making method for intelligent distribution network load scheduling, comprising the following steps:

[0052] S1: Based on the power access point of the charging pile, divide the charging piles within the same power access point into a network, construct a charging pile network topology structure containing regions and units, each unit corresponds to one charging pile, each region corresponds to one charging station, and mark the regions and units, and determine the working status of the unit based on whether it is charging.

[0053] In this embodiment: the coordinates of the power access points of all charging piles are obtained in real time from the charging pile management system. Each access point corresponds to an independent power source. All charging piles under the same power access point are divided into a region, representing a charging station, and a unique region ID is assigned to each region. Each charging station is treated as a unit and assigned a unique unit ID. The topology is stored using a graph data structure, with regions as parent nodes and cells as child nodes. A topology table is output, containing the fields: Region ID and Cell ID. The operating status of each cell is polled every five seconds. If the current value of a cell is greater than 0, its status is marked as "Charging"; otherwise, it is marked as "Idle". The updated topology table with the operating status is output. In this embodiment, the total power supply is 300KW, and there are 12 charging cells distributed across 3 regions. Ten of these cells are currently charging and operating, as shown in the table below:

[0054]

[0055] S2: Based on the judgment results, select the charging unit, obtain the maximum power supply of the power access point and the power curve of the charging unit during this charging, as well as the real-time power and maximum power of the corresponding charging vehicle, and identify the power curve to obtain the real-time power, maximum power and charging time of the charging unit during this charging.

[0056] In this embodiment, all units in the charging state are extracted from the topology table to form a charging unit list. The following data are read in real time: the maximum power supply of the power access point, the charging power curve of each charging unit, the real-time power level and maximum power level of the corresponding charging vehicle, and a moving average filter is applied to the power curve of each charging unit for noise reduction, as shown in the following formula:

[0057]

[0058] in, To eliminate high-frequency noise from the charging power curve, moving average filtering effectively smooths the curve, reducing the impact of instantaneous fluctuations and making the data more stable and reliable. This is particularly important for real-time systems, as charging power can be affected by various factors, such as grid fluctuations and changes in charging station status. Linear regression models can quickly calculate real-time and maximum power, making them suitable for monitoring dynamic changes in real-time environments. This method not only provides real-time power... The accurate value also reflects the highest power during the charging process. This provided the necessary basis for subsequent decision-making. This is the time offset. for The original power curve of the charging unit was then used to identify key parameters using a linear regression model:

[0059]

[0060]

[0061]

[0062]

[0063] in, For real-time power, For the current moment, For charging unit Peak power during this charging process, for The real-time battery level of the vehicle being charged by the charging unit. For charging unit The maximum battery capacity of the vehicle being charged; for The battery capacity of the vehicle being charged by the charging unit; For charging unit The average power of this charge, In this embodiment, the charging efficiency coefficient is... The charging time estimate is based on real-time and Combined with average power and charging efficiency This provides a more accurate estimate of remaining charging time. Traditional technologies may rely on static power models or estimations based on historical data, which may not be accurate enough when faced with dynamic changes. The combination of moving average and linear regression reflects the changing trend of power demand in real time, improving the accuracy and real-time performance of data collection.

[0064] S3: Based on the real-time identification of the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit, combined with the power curve change trend, the current charging urgency of each charging unit is evaluated, and a corresponding priority score is assigned to measure its current demand for power resources.

[0065] In this embodiment, based on the data obtained in S2, the urgency of each charging unit is evaluated and assigned a priority score. The first derivative is used to calculate the changing trend of the power curve of each charging unit, as shown in the following formula:

[0066]

[0067] in, The power trend value of each charging unit is used for trend analysis. By calculating the first derivative of the power curve, trend analysis can monitor the power change trend of each charging unit in real time. This dynamic monitoring capability helps the system to promptly identify increases or decreases in power demand, enabling the charging pile network to respond quickly to changes. Through trend analysis, the system can identify impending high-load conditions or potential faults in advance. For example, if... When the value increases sharply, the system can automatically adjust resource allocation to avoid overload and equipment damage; a weighted scoring method is used to construct a priority scoring function, as shown in the following formula:

[0068]

[0069] in, For charging unit Priority score, The maximum trend value of the system is denoted as , where . State of Charge (SOC) is a key indicator reflecting the current state of battery power; the lower the charge level, the greater the urgency to charge; therefore, SOC is given a relatively high weight of 0.6; real-time power Relative values ​​reflect the current power output capability of a charging unit; considering real-time power helps ensure the effective utilization of charging resources when assessing urgency; although power is an important factor, its impact is slightly lower than that of state of charge, therefore it is given a moderate weight of 0.3; the trend factor provides insight into power change trends, and while it helps identify upcoming demand changes, its relative influence is generally low; therefore, its weight is set to 0.1 to reflect its role as an auxiliary indicator; the priority scoring function comprehensively considers multiple key factors—SOC, real-time power, and trend factor—through a weighted scoring method, making the demand assessment of each charging unit more comprehensive; this comprehensive assessment helps accurately identify the most urgent charging needs; through real-time updated scoring, the system can dynamically adjust the priority of charging units, effectively respond to instantaneous load changes, and ensure that important needs are met in a timely manner.

[0070] Normalization The formula for scores from 0 to 100 is as follows:

[0071]

[0072] in, For charging unit Normalized score The minimum score among all charging units. To find the maximum Score among all charging units, add a Score field to each charging unit, store the calculated value, and output the updated value containing the maximum Score. The system includes a list of charging units; scores are normalized to a range of 0-100, making it easier for users and management systems to intuitively understand and compare the urgency ratings of different charging units; values ​​are within a fixed range, making the evaluation results clearer; the normalization process ensures that all scores are performed under the same standard, eliminating evaluation bias caused by different magnitudes or units; it helps maintain the consistency of the system's evaluation and ensures the reliability of comparisons under different times and conditions.

[0073] S4: Based on the region and unit labels, prioritize the region as the unit. According to the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit, use a multi-objective allocation function to allocate the total power of the power access point at the region level. Then, based on the priority score of each unit in the region, allocate the power at the unit level and output the initial value of the target allocated power for each unit.

[0074] In this embodiment, S4 first performs region aggregation. For each region, the formula for calculating the total region priority and charging unit occupancy ratio is as follows:

[0075]

[0076]

[0077] in, for Regional overall priority express In the region Priority score of charging unit, express The total number of charging units in the area. for The number of units currently in operation in the region. for Number of units in the region for Area occupancy ratio; using a multi-objective allocation function, the total power is allocated to each area, and the allocation formula is as follows:

[0078]

[0079] in, To be assigned The power of the region, For all regions and The sum of products The total number of all regions, This is the maximum power supply capacity. The index used in the summation formula is specifically the index of the region; by using the overall region priority, regions with higher demand can be quickly identified. As a working occupancy ratio, it serves as a buffer and correction for the working charging units within a region. This is especially helpful for load control decision-making in distribution network scheduling for charging pile clusters with uniform rated power, such as dedicated charging stations in bus hubs and logistics parks. A higher proportion of working charging units indicates a greater charging demand in the current region, requiring more power to be allocated. It directly reflects the real-time load and characterizes the current equipment utilization rate in the region, more closely matching the real-time power demand. Because the more working units there are, the greater the total demand and the greater the potential charging demand.

[0080] This approach helps decision-makers prioritize the construction of charging infrastructure and power supply needs in these regions. By calculating the workload ratio, it can understand the demand for charging facilities within the region, thus providing guidance for future expansion and deployment. Compared to traditional single-point assessment methods, this approach provides a more representative and holistic data foundation, making the decision-making process more scientific and rational. Existing technologies rarely consider the characteristics of charging workload ratios, while this method can effectively improve the efficiency of charging unit management, enabling more precise resource allocation and avoiding resource waste. By continuously monitoring the overall regional priority and the workload ratio of charging units, it can promptly identify changes in regional demand, enhance the system's responsiveness to emergencies, and improve the overall operational flexibility.

[0081] In this embodiment, allocation is based on unit priority within each region, and the allocation formula is as follows:

[0082]

[0083] in, Assign power to the unit initially. for The sum of the scores of all units within the region, where The index used in the summation formula is specifically the index of the charging unit within the region; the output list of charging units is added. The system employs a field-based approach; by allocating power proportionally based on unit priority, it ensures that the power received by each charging unit is proportional to its score. This method maintains fairness, ensuring that high-priority charging units receive more power, thus better meeting user needs. Introducing a density factor into the regional allocation effectively avoids overload caused by uneven power distribution among charging units in high-density areas, thereby guaranteeing system safety and stability. This helps improve the overall reliability of charging facilities and reduce the risk of failure. Traditional methods may use uniform or fixed-ratio allocation, while this method dynamically calculates the initial power allocation for each unit, which is closer to actual needs and improves resource utilization efficiency.

[0084] S5: Perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, constrain it to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power.

[0085] In this embodiment: constraint correction is performed on the output of S4, and each charging unit is traversed to check... Does it exceed the unit's maximum power threshold? ,like ,but ;like ,but And record excess power : ;in For final power allocation.

[0086] In this embodiment, the total excess power is calculated, and the formula for calculating the total excess power is as follows:

[0087]

[0088] in, For charging unit Excess power For total excess power, Given the total number of charging units, the total excess power is redistributed to other units that have not exceeded the limit according to their relative priority.

[0089]

[0090]

[0091] in, To allocate to units that have not exceeded limits Additional power increment, The set of all non-overlimited units, Generate charging control commands for the total number of units that have not exceeded the limit. To finalize power allocation, instructions are sent in real-time to each charging unit controller via the MQTT protocol to execute power adjustments. By constraining and correcting the power of each charging unit, it is ensured that the allocated power does not exceed the maximum power threshold of each unit, effectively avoiding overload, equipment damage, or safety hazards, and ensuring the safe and stable operation of the system. The redistribution of excess power allows for more efficient use of system resources, allocating unused power to other charging units that have not exceeded their limits, promoting efficient resource utilization and improving the overall operating efficiency of the system. The relative priority redistribution of excess power ensures that high-priority units receive reasonable consideration when obtaining power, improving user satisfaction and fairness. Compared to traditional static allocation methods, this step dynamically adjusts power allocation based on actual conditions, more accurately meeting user needs and avoiding resource waste. Real-time monitoring and correction of power allocation allows for rapid adaptation to changes in charging demand, improving the system's responsiveness to emergencies and making overall charging management more flexible. In this embodiment, data from 12 charging piles in three selected areas, collected at 5 minutes, 15 minutes, and 30 minutes from time 0, are used to demonstrate the effectiveness of the method of this invention, as shown in Tables 1 to 4 below.

[0092] Table 1: Experimental Dataset

[0093]

[0094] Table 2: Experimental Data Set at t=5 Minutes

[0095]

[0096] Table 3: Experimental Data Set at t=15 Minutes

[0097]

[0098] Table 4: Experimental Data Set at t=30 Minutes

[0099]

[0100] Table 5: Comparison of Power Utilization Rate

[0101]

[0102] Reference Figure 2 , Figure 3 , Figure 4The method clearly demonstrates its efficiency in the charging process of charging piles. By dividing the power access point into regions and units, constructing a tree topology and mapping the status in real time, the power allocation is strictly aligned with the physical structure of the power grid, improving the efficiency of regional load identification. In addition, based on the remaining power gap, real-time power ratio, and power change trend, a dynamic priority scoring model is constructed and normalized to dynamically distinguish the charging urgency of vehicles, giving vehicles with low power a higher power allocation right, thus shortening the overall charging completion time. A hierarchical power allocation strategy is adopted, first allocating the total power according to the regional priority and the working occupancy ratio of the charging unit, and then allocating the initial power within the region according to the unit score. A two-factor allocation function of regional aggregation priority and power unit working occupancy ratio is introduced to increase the power quota in high-demand dense areas and avoid local overload. In addition, the excess power is constrained and corrected, and the excess power is cyclically allocated to the non-exceeding units according to priority. Through the excess power constraint correction and priority-based cyclic redistribution mechanism, it is ensured that the unit power does not exceed the limit and the total power of the power supply point does not exceed the capacity, thus reducing the failure rate. Meanwhile, in the unused method of this invention, the kilometer utilization rate when using average allocation is shown in Table 5, with an average utilization rate of 97.6% vs. 88.5% for the traditional scheme; and a reduction of 62.8 kWh of wasted power within 1800 seconds.

[0103] Please see Figure 5 The present invention also provides a control-aid decision-making system for intelligent distribution network dispatch load, wherein the intelligent distribution network dispatch load control-aid decision-making system is used in the above-mentioned intelligent distribution network dispatch load control-aid decision-making method, comprising:

[0104] The topology construction module is used to divide the charging piles within the same power access point into a network based on the power access point of the charging pile, and construct a charging pile network topology containing regions and units. Each unit corresponds to one charging pile, and each region corresponds to one charging station. The region and unit are marked, and the working status of the unit is determined based on whether it is charging.

[0105] The data acquisition module is used to filter out charging units based on the judgment results, and to obtain the maximum power supply of the power access point, the power curve of the charging unit during this charging, and the real-time and maximum power of the corresponding charging vehicle in real time. The power curve is used to identify and obtain the real-time power, maximum power and charging time of the charging unit during this charging.

[0106] The priority scoring module is used to evaluate the current charging urgency of each charging unit based on the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit identified in real time, combined with the power curve change trend, and assign a corresponding priority score to measure its current demand for power resources.

[0107] The power allocation module is used to allocate the total power of the power access point at the region level based on the region and unit labels, prioritizing the region as the unit. It then uses a multi-objective allocation function to allocate the total power at the region level based on the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit. Finally, it allocates the power at the unit level based on the priority score of each unit in the region and outputs the initial value of the target allocated power for each unit.

[0108] The power correction and regulation module is used to perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, it is constrained to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power.

[0109] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0110] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A control-aided decision-making method for intelligent distribution network load scheduling, characterized in that, The specific steps include: S1: Based on the power access point of the charging pile, divide the charging piles within the same power access point into a network, construct a charging pile network topology structure containing regions and units, each unit corresponds to one charging pile, each region corresponds to one charging station, and mark the regions and units, and determine the working status of the unit based on whether it is charging. S2: Based on the judgment results, select the charging unit, obtain the maximum power supply of the power access point and the power curve of the charging unit during this charging, as well as the real-time power and maximum power of the corresponding charging vehicle, and identify the power curve to obtain the real-time power, maximum power and charging time of the charging unit during this charging. S3: Based on the real-time identification of the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit, combined with the power curve change trend, the current charging urgency of each charging unit is evaluated, and a corresponding priority score is assigned to measure its current demand for power resources. S4: Based on the region and unit labels, prioritize the region as the unit. According to the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit, use a multi-objective allocation function to allocate the total power of the power access point at the region level. Then, based on the priority score of each unit in the region, allocate the power at the unit level and output the initial value of the target allocated power for each unit. S5: Perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, constrain it to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power. The power access point coordinates of all charging piles are obtained in real time from the charging pile management system. Each access point corresponds to an independent power source. All charging piles under the same power access point are divided into a region, representing a charging station, and a unique region ID is assigned to each region. Each charging station is treated as a unit and assigned a unique unit ID. Use a graph data structure to store the topology, with regions as parent nodes and cells as child nodes. Output a topology table containing the fields: Region ID and Cell ID. Poll the working status of each cell every five seconds. If the current value of the cell is greater than 0, mark the status as "Charging"; otherwise, mark it as "Idle". Output the updated topology table with the working status. From the topology table, extract all units in the charging state to form a charging unit list. Read the following data in real time: the maximum power supply of the power access point, the charging power curve of each charging unit, the real-time and maximum battery level of the corresponding charging vehicle, and apply a moving average filter to the power curve of each charging unit to remove high-frequency noise, thus obtaining the charging power curve after eliminating high-frequency noise. Identify using a linear regression model Key parameters include: real-time power of the charging unit, peak power of the charging unit, average charging power, real-time battery level of the vehicle being charged, battery capacity of the vehicle, and estimated charging time. Based on the data obtained from S2, the urgency of each charging unit is assessed and assigned a priority score. The first derivative is used to calculate the changing trend of the power curve of each charging unit. A weighted scoring method is used to construct a priority scoring function, as shown in the following formula: in, For charging unit Priority score for The real-time battery level of the vehicle being charged by the charging unit. For charging unit The maximum battery capacity of the vehicle being charged For charging unit Real-time power output during this charging process. For charging unit Peak power during this charging process This represents the power trend value of the charging unit. This represents the system's maximum trend value. Let be the weight coefficient, and satisfy... Store the calculated values ​​in the list of charging units.

2. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 1, characterized in that: Normalization The formula for scores from 0 to 100 is as follows: in, For charging unit Normalized score The minimum score among all charging units. To find the maximum Score among all charging units, add a Score field for each charging unit, store the calculated value in the field, and output the updated value containing the maximum Score. List of charging units.

3. The intelligent distribution network load control auxiliary decision-making method according to claim 2, characterized in that: In S4, region aggregation is performed first. For each region, the formula for calculating the total region priority and charging unit occupancy ratio is as follows: in, for Regional overall priority for area Priority score of charging units, express The total number of charging units in the area for The number of units currently in operation in the region. for Number of units in the region for The occupancy ratio of the regional charging units; using a multi-objective allocation function, the total power is allocated to each region, and the allocation formula is as follows: in, To be assigned The power of the region, This is the maximum power supply capacity. The total number of all regions. The index is used in the summation formula; it is assigned based on cell priority within each region, using the following formula: in, Allocate initial power to the units; output a list of charging units and add new ones. Fields, This is the index in the summation formula.

4. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 3, characterized in that: Constraint correction is applied to the initial power allocation of the cells, and each charging cell is traversed and checked. Does it exceed the unit's maximum power threshold? ,like ,but ;like ,but And record excess power : ;in For final power allocation.

5. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 4, characterized in that: The total excess power is calculated using the following formula: in, For charging unit Excess power For total excess power, Given the total number of charging units, the total excess power is redistributed to other units that have not exceeded the limit according to relative priority; in, To allocate to units that have not exceeded limits Additional power increment, The set of all non-overlimited units, Generate charging control commands for the total number of units that have not exceeded the limit. To ultimately allocate power, instructions are sent in real time to the controllers of each charging unit via the MQTT protocol to perform power adjustments.

6. A control-aid decision-making system for intelligent distribution network load dispatching, characterized in that: The intelligent distribution network dispatch load control auxiliary decision-making system is used to execute the intelligent distribution network dispatch load control auxiliary decision-making method according to any one of claims 1-5, including: The topology construction module is used to divide the charging piles within the same power access point into a network based on the power access point of the charging pile, and construct a charging pile network topology containing regions and units. Each unit corresponds to one charging pile, and each region corresponds to one charging station. The region and unit are marked, and the working status of the unit is determined based on whether it is charging. The data acquisition module is used to filter out charging units based on the judgment results, and to obtain the maximum power supply of the power access point, the power curve of the charging unit during this charging, and the real-time and maximum power of the corresponding charging vehicle in real time. The power curve is used to identify and obtain the real-time power, maximum power and charging time of the charging unit during this charging. The priority scoring module is used to evaluate the current charging urgency of each charging unit based on the current charging power, remaining battery capacity, maximum capacity and charging time of each charging unit identified in real time, combined with the power curve change trend, and assign a corresponding priority score to measure its current demand for power resources. The power allocation module is used to allocate the total power of the power access point at the region level based on the region and unit labels, prioritizing the region as the unit. It then uses a multi-objective allocation function to allocate the total power at the region level based on the priority score aggregation of each charging unit in each region and the working occupancy ratio of the charging unit. Finally, it performs unit-level power allocation based on the priority score of each unit in the region and outputs the initial value of the target allocated power for each unit. The power correction and regulation module is used to perform constraint correction on the initial power allocation value of each charging unit. If it exceeds the maximum power threshold allowed by the charging unit, it is constrained to the maximum power value. The excess part is redistributed to other charging units according to relative priority. Finally, the corrected target output power value of each charging unit is obtained. The corrected target power value is sent to each corresponding unit as a charging control command to regulate its output power.

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