Control auxiliary decision-making method and system for dispatching load of intelligent distribution network

Abstract

The invention provides a control auxiliary decision-making method and system for dispatching load of an intelligent distribution network, and relates to the field of electric power system dispatching.The method comprises the steps that charging areas and units are divided according to power access points, a topological structure is constructed, and the charging state is monitored in real time; dynamically acquiring a power curve of a unit in charging, vehicle electric quantity and maximum power supply power of a power supply point, and calculating charging time by combining with a battery capacity parameter; based on the residual electric quantity gap, the real-time power proportion and the power change trend, constructing a dynamic priority scoring model and performing normalization processing; a hierarchical power distribution strategy is adopted, the total power is distributed according to the area priority and the working occupation ratio of the charging units, and then the initial power is distributed in the area according to the unit score; and carrying out constraint correction on the over-limit power, circularly distributing the redundant power to a non-over-limit unit according to the priority, and finally issuing a corrected power instruction in real time through an MQTT protocol. Safe operation of a power grid is guaranteed, and the charging efficiency and the resource utilization rate are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system dispatching, in particular to a control auxiliary decision method and system for intelligent distribution network dispatching load. BACKGROUND

[0002] With the large-scale access of electric vehicles, the distribution network dispatching is facing severe challenges; especially for the special charging station scenes such as public transport hubs and logistics parks, the existing charging pile dispatching method generally adopts flat device list management, which cannot reflect the hierarchical relationship between the power access point and the charging pile; it is difficult to quickly locate the associated charging pile under the high-load power supply point during power grid dispatching, and the response delay is significant; the load demand characteristics are ignored, the power distribution is disconnected with the physical network structure, and the power demand of the charging unit with high occupation ratio is much larger than that of the charging unit with low occupation ratio; in addition, the charging pile cluster power distribution method mostly adopts static threshold or simple polling strategy, which cannot dynamically identify the charging urgency difference of different vehicles: such as mixing the demand of low battery vehicles and vehicles close to full battery, which leads to the delay of high-priority vehicle charging during power grid peak load, causing user complaints; and there is no real-time correction mechanism for device-level power constraints, and high-density areas have the risk of local overload due to insufficient power distribution; power over-limit units need manual intervention, which affects the safety of the system.

[0003] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0004] The purpose of the present application is to provide a control auxiliary decision method and system for intelligent distribution network dispatching load to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A control auxiliary decision method for intelligent distribution network dispatching load, the specific steps comprising: S1: According to the power access point of the charging pile, the charging piles in the same power access point are divided into networks, and a charging pile network topology structure containing areas and units is constructed, each unit corresponds to a charging pile, each area corresponds to a charging station, and the areas and units are marked, and the working state of the unit is determined according to whether it is charging; S2: According to the determination result, the charging unit is screened out, the maximum power supply power of the power access point and the power curve of the charging unit this time, and the real-time power and the maximum power of the corresponding charging vehicle are obtained, and the real-time power, the maximum power and the charging time of the charging unit this time are obtained by identifying the power curve; S3: According to the current charging power, the remaining battery capacity, the maximum battery capacity and the charged time of each charging unit obtained by real-time identification, the current charging urgency of each charging unit is evaluated in combination with the power curve change trend, and a corresponding priority score for measuring the current demand degree of power resources is assigned; S4: According to the region and unit mark, the total power of the power access point is regionally allocated by using a multi-objective allocation function according to the priority score aggregation and charging unit working occupation ratio of each charging unit in each region, and then the unit-level power allocation is performed based on the priority score of each unit in the region, and the target allocation power initial value of each unit is output. S5: The allocation power initial value of each charging unit is corrected, if it exceeds the maximum power threshold allowed by the charging unit, it is constrained to the maximum power value, and the excess part is redistributed to other charging units according to the relative priority, and finally the corrected target output power value of each charging unit is obtained, and the corrected target power value is sent as a charging control instruction to each corresponding unit to regulate its output power.

[0006] Further, the power access point coordinate information of all charging piles is obtained from the charging pile management system in real time, each access point corresponds to an independent power supply, all charging piles under the same power access point are divided into a region, representing a charging station, and each region is assigned a unique region ID: ; Each charging pile is a unit and is assigned a unique unit ID: ; The topology is stored using a graph data structure, the region is the parent node and the unit is the child node, and a topology table is output, including fields: region ID, unit ID; The working state of each unit is polled every five seconds; If the current current value of the unit is greater than 0, the state is marked as "charging"; Otherwise, it is marked as "idle"; The topology table with the updated working state is output.

[0007] Further, from the topology table, all units in the charging state are extracted to form a charging unit list, and the following data is read in real time: the maximum power supply of the power access point, the current charging power curve of each charging unit, the real-time power and the maximum power of the corresponding charging vehicle, and the moving average filter is applied to the power curve of each charging unit to remove noise and obtain the charging power curve after eliminating high-frequency noise , then the linear regression model is used to identify the key parameters, including: real-time power of the charging unit, peak power of the charging unit, average charging power, real-time power of the charged vehicle, battery capacity of the vehicle, and estimated charging time.

[0008] 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: ; 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.

[0009] Furthermore, 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.

[0010] 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: ; ; 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 The number of units in the region, For The region charging unit working occupation ratio; use a multi-objective allocation function to allocate total power to each region, and the allocation formula is as follows: Wherein, The power allocated to The region, The maximum power supply of the power supply, The total number of all regions; based on the unit priority allocation in each region, the allocation formula is as follows: ; Wherein, The initial allocation power of the unit; output the charging unit list, and add The field.

[0011] Further, the output of S4 is corrected, each charging unit is traversed, and whether The maximum power threshold of the unit is exceeded If , If , And record the excess power : .

[0012] Further, the total excess power is calculated, and the total excess power calculation formula is as follows: ; Wherein, The excess power of the charging unit , The total excess power, the total excess power is redistributed to other units that do not exceed the limit according to the relative priority level; ; ; Wherein, The additional power increment allocated to the unit That does not exceed the limit, The set of all units that do not exceed the limit, generate charging control instructions, The final allocation power, the instructions are transmitted to the charging unit controller in real time through the MQTT protocol, and the power adjustment is executed.

[0013] The application further provides a control auxiliary decision system for intelligent distribution network scheduling load, which is used for the control auxiliary decision method for intelligent distribution network scheduling load, comprising: a topology construction module, configured to divide the charging piles in one power access point according to the power access points, to construct a charging pile network topology containing areas and units, each unit corresponding to one charging pile and each area corresponding to one charging station, and to mark the areas and units and determine the working state of the units according to whether they are charging; a data acquisition module, configured to filter out the charging units according to the determination result, to acquire the maximum power supply of the power access point and the power curve of the current charging of the charging units and the real-time power and the maximum power of the corresponding charging vehicles, to identify the power curve to acquire the real-time power, the maximum power and the charging time of the current charging of the charging units; a priority scoring module, configured to evaluate the current charging urgency of each charging unit according to the current charging power, the remaining battery power, the maximum power and the charged time of each charging unit obtained by real-time identification, and to give a priority score for measuring the current demand degree of power resources of each charging unit according to the change trend of the power curve; a power allocation module, configured to preferentially allocate power in the unit of area according to the priority scores of the charging units in each area and the working occupation ratio of the charging units, to perform regional-level power allocation on the total power of the power access point by using a multi-objective allocation function, to perform unit-level power allocation based on the priority scores of the units in the area, and to output the target initial power of each unit; a power correction and regulation module, configured to correct the initial power of each charging unit, to constrain it to the maximum power value if it exceeds the maximum power threshold of the charging unit, to reallocate the excess part to other charging units according to the relative priority, and to finally obtain the corrected target output power value of each charging unit, to send the corrected target power value to each corresponding unit as a charging control instruction, and to regulate the output power of each corresponding unit.

[0014] Compared with the prior art, the present application has the beneficial effects that: based on the division of areas and units by power access points, a tree topology is constructed and the state is mapped in real time, so that the power distribution is strictly aligned with the physical structure of the power grid, and the area load identification efficiency is improved; in addition, based on the remaining power gap, real-time power proportion and power change trend, a dynamic priority scoring model is constructed and normalized, and the vehicle charging urgency is dynamically differentiated, and the low power vehicle gets higher power distribution right, and the overall charging completion time is shortened; a hierarchical power distribution strategy is adopted, the total power is distributed according to the area priority and the charging unit working occupation ratio, and then the initial power is distributed according to the unit score within the area; the regional aggregation priority and the power unit working occupation ratio double factor distribution function are introduced, the power quota of high demand dense area is improved, and local overload is avoided; in addition, the over-limit power is corrected, the excess power is distributed to the non-over-limit unit according to the priority, through the over-limit power correction and the priority-based cyclic redistribution mechanism, the unit power is ensured not to exceed the limit, the total power of the power supply point is ensured not to exceed the capacity, the failure rate is reduced, and the whole process is automatically executed without manual intervention; finally, the corrected power instruction is issued in real time through the MQTT protocol; the present application ensures the safe operation of the power grid, improves the charging efficiency and resource utilization. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall method flow of the present application; Figure 2 It is a SOC data graph of each charging unit in the embodiment; Figure 3 It is a Score data graph of each charging unit in the embodiment; Figure 4 It is a distribution power data graph of each charging unit before and after the constraint correction in the embodiment; Figure 5 It is a schematic block diagram of the overall system module of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with specific embodiments.

[0017] It should be noted that the technical terms or scientific terms used in the present application should be understood as the general meaning understood by those skilled in the art unless otherwise defined. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Embodiments: Please refer to Figures 1-4 The present application provides a technical solution: An intelligent distribution network dispatching load control auxiliary decision-making method, the specific steps comprising: S1: According to the power access point of the charging pile, the charging piles in the same power access point are divided into networks, and the charging pile network topology structure containing areas and units is constructed, each unit corresponds to a charging pile, each area corresponds to a charging station, and the areas and units are marked, and the working state of the unit is determined according to whether it is charging or not; In this embodiment: the power access point coordinate information of all charging piles is obtained in real time from the charging pile management system, each access point corresponds to an independent power supply, all charging piles under the same power access point are divided into an area, representing a charging station, and each area is assigned a unique area ID: ; Each charging pile is a unit and is assigned a unique unit ID: ; The topology is stored using a graph data structure, the area is the parent node and the unit is the child node, and a topology table is output, containing fields: area ID, unit ID; The working state of each unit is polled every five seconds; If the current current value of the unit is greater than 0, the state is marked as "charging"; Otherwise, it is marked as "idle"; The topology table with the working state after updating is output. In this embodiment, the total power of the power supply is 300KW, the number of charging units is 12, which belong to 3 areas, of which 10 are in the working state of charging, as shown in the following table:

[0019] S2: According to the determination result, the charging unit is screened out, the maximum power supply power of the power supply access point and the power curve of the charging unit in this charging are obtained in real time, and the real-time power, maximum power and charging time of the charging unit in this charging are obtained by identifying the power curve; In this embodiment, all units in the charging state are extracted from the topology table to form a charging unit list. The following data is read in real time: the maximum power supply power of the power supply access point, the power curve of each charging unit in this charging, the real-time power and maximum power of the corresponding charging vehicle. Moving average filtering is applied to the power curve of each charging unit to denoise, and the formula is as follows: ; wherein, is the charging power curve after eliminating high-frequency noise. Moving average filtering can effectively smooth the power curve, reduce the influence of instantaneous fluctuations, and make the data more stable and reliable. This is particularly important for real-time systems, as charging power may be disturbed by various factors such as power grid fluctuations, charging pile state changes, etc. Linear regression model can quickly calculate real-time power and maximum power, suitable for dynamic change monitoring in real-time environment. This method not only provides accurate values of real-time power , but also reflects the highest power in the charging process, providing necessary basis for subsequent decision-making is the time offset, is the original power curve of the charging unit, and then the linear regression model is used to identify the key parameters: ; ; ; ; wherein, is the real-time power, is the current time, is the peak power of the charging unit in this charging process, is the real-time power of the charged vehicle of the charging unit, is the maximum power of the charged vehicle of the charging unit ; is the battery capacity of the charged vehicle of the charging unit; is the average power of the charging unit in this charging, is the charging efficiency coefficient, and in this embodiment , the estimation of charging time is based on real-time and , combined with the average power and charging efficiency , provides a more accurate remaining charging time estimation, traditional technologies may rely on static power models or historical data-based estimates, which may not be accurate in the face of dynamic changes. The combination of moving average and linear regression reflects the trend of power demand in real time, improving the accuracy and real-time performance of data collection.

[0020] S3: According to the current charging power, battery remaining capacity, maximum capacity and charged time of each charging unit identified in real time, combined with the trend of power curve change, the current charging urgency of each charging unit is evaluated, and the corresponding priority score for measuring its current demand for power resources is given; In this embodiment, based on the data obtained by S2, the urgency of each charging unit is evaluated and a priority score is given, the trend of each charging unit power curve is calculated using the first derivative, the formula is as follows: ; Where, is the charging unit power trend value, trend analysis through the calculation of the first derivative of the power curve can monitor the trend of each charging unit power in real time; this dynamic monitoring capability helps the system to identify the increase or decrease of power demand in time, so that the charging pile network can quickly respond to changes; through trend analysis, the system can identify the upcoming high load state or potential failure in advance. For example, if 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, the formula is as follows: ; Where, is the priority score of the charging unit , and is the maximum trend value of the system, where , SOC is a key indicator reflecting the current capacity state of the battery; the lower the capacity, the higher the urgency of charging; therefore, a higher weight of 0.6 is given to SOC; 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. 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 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.

[0021] 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. 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: ; ; 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: ; in, To be assigned The power of the region, For all regions and The sum of products The total number of all regions, This indicates the maximum power supply capacity; by using the overall regional priority, areas with high 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. 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. In this embodiment, allocation is based on unit priority within each region, and the allocation formula is as follows: ; in, Assign power to the unit initially. for The sum of all unit scores in the region; output the charging unit list, add new The field; through the proportional allocation of unit priority, ensure that each charging unit obtains power proportional to its score. This method can maintain fairness and ensure that high-priority charging units obtain more power, thereby better meeting user demand; the introduction of a density factor in regional allocation can effectively avoid the overload of charging units in high-density areas due to uneven power distribution, thereby ensuring the safety and stability of the system. This helps to improve the overall reliability of charging facilities and reduce the risk of failure; traditional methods may use uniform distribution or fixed proportional distribution, while this method dynamically calculates the initial power allocation of each unit, better meets actual demand, and improves resource utilization efficiency.

[0022] S5: Constraint correction of the initial power allocation 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 is redistributed to other charging units according to the relative priority, and the final corrected target output power value of each charging unit is obtained. The corrected target power value is used as the charging control instruction to each corresponding unit to regulate its output power; In this embodiment: the output of S4 is corrected, each charging unit is traversed, and whether it exceeds the maximum power threshold of the unit is checked If , If , , and record the excess power : .

[0023] In this embodiment, the total excess power is calculated, and the total excess power calculation formula is as follows: ; Wherein, is the excess power of the charging unit , and is the total excess power, which is redistributed to other non-exceeding units according to the relative priority level; ; ; Wherein, is the additional power increment allocated to the non-exceeding unit , and is the set of all non-exceeding units, and the charging control instruction is generated ​To finally allocate power, the instructions are issued in real time to each charging unit controller through the MQTT protocol to execute power adjustment; by correcting the power of each charging unit, it is ensured that the allocated power does not exceed the maximum power threshold of each unit, thereby effectively avoiding overload, equipment damage or safety hazards, and ensuring the safe and stable operation of the system; by redistributing the excess power, system resources can be more effectively utilized, and unused power can be allocated to other charging units that are not over the limit, promoting efficient use of resources and improving the overall operating efficiency of the system; by using the relative priority to redistribute excess power, it is ensured that high-priority units are reasonably considered when obtaining power, and this mechanism improves user satisfaction and a sense of fairness; compared with the traditional static allocation method, this step can dynamically adjust the power allocation according to the actual situation, thereby more accurately meeting user demand and avoiding waste of resources; by monitoring and correcting power allocation in real time, the system can quickly adapt to changes in charging demand, improve its response to unexpected situations, and make overall charging management more flexible. In this embodiment, the data of 12 charging piles in three selected regions from 0 o'clock to 5 minutes, 15 minutes and 30 minutes are used to show the effect of the method; Tables 1 to 4 are as follows: Table 1: Experimental data set

[0024] Table 2: t=5 minutes experimental data set

[0025] Table 3: t=15 minutes experimental data set

[0026] Table 4: t=30 minutes experimental data set

[0027] Table 5: Power utilization rate comparison

[0028] Referring to Figure 2 , Figure 3 , Figure 4The execution efficiency of the method in the charging process of the charging pile is clearly shown, the area and unit are divided based on the power access point, the tree topology is constructed and the state is mapped in real time, the power distribution is strictly aligned with the physical structure of the power grid, and the area load identification efficiency is improved; 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, and the vehicle charging urgency is dynamically distinguished, the low power vehicle gets higher power allocation right, and the overall charging completion time is shortened; a hierarchical power allocation strategy is adopted, the total power is allocated according to the area priority and charging unit working occupation ratio, and then the initial power is allocated according to the unit score; the regional aggregation priority and power unit working occupation ratio double factor distribution function is introduced, the power quota of the high demand dense area is improved, and local overload is avoided; in addition, the over-limit power is corrected, the excess power is recycled to the non-over-limit unit according to the priority, and through the over-limit power correction and the recycling redistribution mechanism based on the priority, the unit power is not over-limit, the total power of the power supply point is not over-capacity, and the failure rate is reduced. At the same time, in the project of kilometer utilization rate using the average allocation method without using the method of the application, the average utilization rate is 97.6% vs 88.5% of the traditional scheme; the waste power is reduced by 62.8 kWh within 1800 seconds.

[0029] Please refer to Figure 5 The application further provides a control auxiliary decision system for smart distribution network scheduling load, which is used for the control auxiliary decision method for smart distribution network scheduling load, and comprises: A topology structure construction module is configured to divide the charging piles in the same power access point according to the power access point of the charging pile, construct a charging pile network topology structure comprising areas and units, each unit corresponding to a charging pile and each area corresponding to a charging station, and mark the areas and units and determine the working state of the units according to whether they are charging; A data acquisition module is configured to filter out the charging units according to the determination result, acquire the maximum power supply power of the power access point and the power curve of the current charging of the charging unit and the real-time power and maximum power of the corresponding charging vehicle, and identify the power curve to acquire the real-time power, maximum power and charging time of the current charging of the charging unit; A priority scoring module is configured to evaluate the current charging urgency of each charging unit according to the current charging power, battery remaining power, maximum power and charged time of each charging unit identified in real time, and combine the power curve change trend to give a priority score for measuring the demand degree of the power resource of each charging unit; The power distribution module is configured to, according to the region and the unit mark, preferentially distribute power in the unit of region, according to the priority score aggregation and the working occupation ratio of each charging unit in each region, perform regional-level power distribution on the total power of the power access point by using a multi-objective distribution function, and perform unit-level power distribution based on the priority score of each unit in the region to output the target distribution power initial value of each unit. The power correction and regulation module is configured to correct the distribution power initial value of each charging unit, and if the distribution power initial value exceeds the maximum power threshold allowed by the charging unit, the distribution power initial value is corrected to the maximum power value, and the excess part is redistributed to other charging units according to the relative priority, and finally the corrected target output power value of each charging unit is obtained. The corrected target power value is used as a charging control instruction and is sent to each corresponding unit to regulate the output power.

[0030] The above formulas are dimensionless values calculated, the formulas are obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0031] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially. Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0032] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0033] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present 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, assess the current charging urgency of each charging unit and assign a corresponding priority score 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.

2. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 1, characterized in that: 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.

3. The intelligent distribution network load control auxiliary decision-making method according to claim 2, characterized in that: 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. Apply a moving average filter to the power curve of each charging unit to remove high-frequency noise, 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.

4. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 3, characterized in that: 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.

5. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 4, 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.

6. The intelligent distribution network scheduling load control auxiliary decision-making method according to claim 5, 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 for all regions; within each region, allocation is based on unit priority, using the following formula: ； in, Allocate initial power to the units; output a list of charging units and add new ones. Field.

7. The intelligent distribution network load control auxiliary decision-making method according to claim 6, 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 : .

8. The intelligent distribution network load control auxiliary decision-making method according to claim 7, characterized in that: The total excess power is calculated using the following formula: ； in, For charging unit Excess power The total excess power is redistributed to other non-over-limit units according to their relative priority levels; ； ； in, To allocate to units that have not exceeded limits Additional power increment, Generate charging control commands for the set of all cells that have not exceeded the limits. 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.

9. 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 1-8, 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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