Multi-parameter fusion power distribution network local load hotspot elimination method and system

Abstract

The invention discloses a multi-parameter fusion power distribution network local load hotspot elimination method and system, and relates to the technical field of power distribution networks, and the method comprises the following steps: collecting the real-time state data of each node in a power distribution network in real time, and determining whether a node association line is a target load hotspot line according to the real-time state data; when the node association line is a target load hotspot line, acquiring a load excess value and a line impedance parameter of the target load line, and acquiring a vehicle scheduling characteristic parameter of the electric vehicle within a coverage range of the target load hotspot line; wherein the vehicle scheduling characteristic parameters comprise a charging demand emergency degree, a discharging capability grade and a user charging and discharging willingness coefficient; and inputting the load excess value, the line impedance parameter and the vehicle scheduling characteristic parameter into the scheduling response correlation model to obtain a basic charging and discharging scheduling strategy. The method has the effect of improving the resource utilization efficiency and the flexible regulation and control capability of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically, to a method and system for eliminating local load hotspots in power distribution networks using multi-parameter fusion. Background Technology

[0002] During the operation of power distribution networks, with the continuous growth of electricity load and the large-scale integration of new loads such as electric vehicles, localized areas are prone to experiencing concentrated load overloads and hotspot phenomena. Traditional power distribution network load regulation methods often rely on scheduling strategies based on single load data, neglecting key state parameters such as conductor temperature, voltage fluctuations, and harmonic distortion. This leads to inaccurate hotspot identification, causing potential hotspots to go undetected and resulting in line overheating, equipment damage, or even power outages. Furthermore, traditional methods lack effective utilization of flexibly dispatchable resources like electric vehicles when addressing hotspots, failing to fully consider scheduling characteristics such as the urgency of electric vehicle charging needs, discharge capacity levels, and user charging / discharging willingness. This results in insufficient targeting and effectiveness of scheduling strategies. Simultaneously, traditional methods do not incorporate historical data on charging / discharging deviation rates and line impedance variation errors to calibrate scheduling strategies, leading to discrepancies between the regulation effect and actual demand, making it difficult to completely eliminate load hotspots. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for eliminating local load hotspots in distribution networks using multi-parameter fusion.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A multi-parameter fusion method for eliminating local load hotspots in distribution networks, comprising the following steps: Real-time status data of each node in the distribution network is collected in real time. Based on the real-time status data, it is determined whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, the load excess value and line impedance parameters of the target load line are collected. Vehicle dispatch characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line are also collected. The load excess value, line impedance parameters, and vehicle dispatching characteristic parameters are input into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy. By combining historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes, the basic charging and discharging scheduling strategy is processed to obtain the first charging and discharging control command and the demand analysis command. Based on the demand analysis command, the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles are processed to obtain the second charging and discharging control command.

[0005] Preferably, the real-time status data includes load data, conductor temperature data, line voltage fluctuation data, and line current harmonic data.

[0006] Preferably, determining whether a node-associated line is a target load hotspot line based on real-time status data specifically includes the following steps: The load change amplitude of each node's associated line in the distribution network is calculated based on the load data. The temperature change rate of each node's associated line in the distribution network is calculated based on the conductor temperature data. The voltage fluctuation amplitude of each node's associated line in the distribution network is calculated based on the line voltage fluctuation data. The harmonic distortion rate of each node's associated line in the distribution network is calculated based on the line current harmonic data. If the load change amplitude of the connected line at a node in the distribution network exceeds the load change amplitude threshold, the temperature change rate exceeds the temperature change rate threshold, the voltage fluctuation amplitude exceeds the voltage fluctuation amplitude threshold, and the harmonic distortion rate exceeds the harmonic distortion rate threshold, then the connected line at that node is determined to be a target load hotspot line; otherwise, the connected line at that node is determined to be a non-target load hotspot line.

[0007] Preferably, the load excess value, line impedance parameters, and vehicle dispatching characteristic parameters are input into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy, specifically including the following steps: Acquire load hotspot events and electric vehicle charging and discharging scheduling data of the distribution network during historical periods; The load hotspot events include the occurrence time of the load hotspot, the duration of the load hotspot, the load excess value and the corresponding line impedance parameters. The electric vehicle charging and discharging scheduling data includes the number of vehicles scheduled, the charging and discharging power allocation, the actual adjustment effect and the scheduling deviation value. A scheduling response correlation model is constructed based on load hotspot events and electric vehicle charging and discharging scheduling data. The basic charging and discharging scheduling strategy is obtained by inputting the vehicle scheduling characteristic parameters, load excess value and line impedance parameters of the scheduling scenario one into the scheduling response association model.

[0008] Preferably, the basic charging and discharging scheduling strategy is processed by combining historical data on the electric vehicle charging and discharging deviation rate and the scheduling error value induced by changes in line impedance to obtain the first charging and discharging control command and the command to be analyzed. Specifically, this includes the following steps: The basic charging and discharging scheduling strategy includes the charging and discharging power allocation status and duration of vehicles in the priority scheduling queue. The charge / discharge difference value is obtained by extracting the difference between the actual charge / discharge amount and the planned charge / discharge amount of electric vehicles from historical data. The charge / discharge deviation rate is obtained by calculating the ratio between the charge / discharge difference and the planned charge / discharge amount. The impedance change of the target load hotspot line is acquired in real time, and the charging and discharging deviation status value of the target load hotspot line is obtained based on the impedance change and the charging and discharging deviation rate. The optimized scheduling strategy is obtained by calibrating the basic charge and discharge scheduling strategy based on the charge and discharge deviation values. The first charge / discharge control command and the analysis command are obtained by adjusting the load value according to the optimized scheduling strategy.

[0009] Preferably, the first charge / discharge control command and the analysis command are obtained by regulating the load value according to the optimized scheduling strategy, specifically including the following steps: If the load value after optimization and scheduling is less than the preset node safety carrying threshold, then the first charging and discharging control command is output. If the load value after optimization and scheduling is greater than or equal to the preset node safety carrying capacity threshold, then an analysis instruction will be output.

[0010] Preferably, the second charging and discharging control command is obtained by processing the load margin of the associated lines in the target load hotspot line and the dynamic access of electric vehicles according to the analysis command, specifically including the following steps: After receiving the analysis command, determine the power supply range and topology of the target load hotspot line, and filter out the associated lines that have power interaction with the target load hotspot line based on the power supply range and topology. Collect rated load data, actual operating load data, and line impedance data of the associated lines; The load condition difference is obtained by calculating the difference between the rated load data and the actual operating load data in the associated lines; The load margin of the associated lines is obtained by correcting the load condition difference based on the line impedance data. The dynamic access volume of electric vehicles within the coverage area of ​​the associated line is obtained based on the connection stability signal between the electric vehicle and the associated line. The load margin of the associated lines is assigned to the corresponding judgment interval, and the dynamic access volume of electric vehicles is assigned to the corresponding judgment interval. The margin status characteristics of each associated line are generated based on the matching relationship between the interval to which the load margin belongs and the interval to which the dynamic access volume of electric vehicles belongs. The second charge / discharge control command is output after analyzing the margin state characteristics.

[0011] Preferably, the second charge / discharge control command is output by analyzing the margin state characteristics, specifically including the following steps: The supplementary regulation power is obtained based on the margin state characteristics and charge / discharge deviation values. A second optimized scheduling strategy is formulated based on the supplementary adjustment power and the vehicle scheduling characteristic parameters of electric vehicles in the associated lines; wherein, the second optimized scheduling strategy includes the charging and discharging power allocation of vehicles in the alternative scheduling queue and the load allocation scheme of the associated lines; The second charge-discharge control command is obtained by correcting the supplementary adjustment power of the second optimized scheduling strategy based on the load regulation error correction coefficient, impedance change, and charge-discharge deviation value.

[0012] A multi-parameter fusion-based distribution network local load hotspot elimination system, characterized in that it includes: The data acquisition module collects real-time status data of each node in the distribution network. Based on the real-time status data, it determines whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, it collects the load excess value and line impedance parameters of the target load line, and collects the vehicle dispatching characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line. Processing module: Inputs the load excess value, line impedance parameters and vehicle dispatching characteristic parameters into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy; Output module: Combines historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes to process the basic charging and discharging scheduling strategy to obtain the first charging and discharging control command and the demand analysis command; Based on the demand analysis command, processes the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles to obtain the second charging and discharging control.

[0013] An electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-parameter fusion method for eliminating local load hotspots in a distribution network.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention identifies target load hotspot lines by collecting real-time status data from various nodes in the distribution network. It can identify lines with overload risks in the distribution network, enabling early warning and location of load hotspots. This avoids serious power grid accidents such as line overheating and voltage collapse caused by undetected load hotspots, ensuring the safe and stable operation of the distribution network. The invention collects load excess values, line impedance parameters, and vehicle dispatching characteristic parameters of electric vehicles, inputting them into a dispatch response correlation model to obtain a basic charging and discharging dispatching strategy. This multi-parameter fusion method fully utilizes power grid operation data and the dispatchable resource information of electric vehicles, enabling the basic dispatching strategy to accurately match the actual condition of the load hotspots and the dispatching potential of electric vehicles. The basic charging and discharging dispatching strategy is calibrated by combining historical data on electric vehicle charging and discharging deviation rates and dispatching error values ​​induced by line impedance changes to obtain an optimized dispatching strategy. This improves the accuracy and adaptability of the dispatching strategy, achieving effective initial control of load hotspots. Based on the analysis command, the load margin of associated lines in the target load hotspot lines and the dynamic access volume of electric vehicles are judged to obtain margin status characteristics, and a second charging and discharging control command is output. It can completely eliminate local load hotspots in the distribution network, improving the resource utilization efficiency and flexible control capability of the distribution network. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the steps of the multi-parameter fusion method for eliminating local load hotspots in a distribution network proposed in this invention. Figure 2 This is a schematic diagram of the module of the multi-parameter fusion distribution network local load hotspot elimination system proposed in this invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0016] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] The embodiments further illustrate the multi-parameter fusion-based method and system for eliminating local load hotspots in distribution networks proposed in this invention.

[0022] A multi-parameter fusion method for eliminating local load hotspots in distribution networks, comprising the following steps: Real-time status data of each node in the distribution network is collected in real time. Based on the real-time status data, it is determined whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, the load excess value and line impedance parameters of the target load line are collected. Vehicle dispatch characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line are also collected. The load excess value, line impedance parameters, and vehicle dispatching characteristic parameters are input into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy. By combining historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes, the basic charging and discharging scheduling strategy is processed to obtain the first charging and discharging control command and the demand analysis command. Based on the demand analysis command, the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles are processed to obtain the second charging and discharging control command.

[0023] Real-time status data includes load data, conductor temperature data, line voltage fluctuation data, and line current harmonic data.

[0024] Determining whether a node's associated line is a target load hotspot line based on real-time status data includes the following steps: The load change amplitude of each node's associated line in the distribution network is calculated based on the load data. The temperature change rate of each node's associated line in the distribution network is calculated based on the conductor temperature data. The voltage fluctuation amplitude of each node's associated line in the distribution network is calculated based on the line voltage fluctuation data. The harmonic distortion rate of each node's associated line in the distribution network is calculated based on the line current harmonic data. If the load change amplitude of the connected line at a node in the distribution network exceeds the load change amplitude threshold, the temperature change rate exceeds the temperature change rate threshold, the voltage fluctuation amplitude exceeds the voltage fluctuation amplitude threshold, and the harmonic distortion rate exceeds the harmonic distortion rate threshold, then the connected line at that node is determined to be a target load hotspot line; otherwise, the connected line at that node is determined to be a non-target load hotspot line.

[0025] First, load data, conductor temperature data, line voltage fluctuation data, and line current harmonic data of each node in the distribution network are collected in real time. Based on these data, the load change amplitude, temperature change rate, voltage fluctuation amplitude, and harmonic distortion rate of the lines associated with each node are calculated respectively.

[0026] Calculate the load change amplitude of each node's associated lines in the distribution network per unit time based on load data, for example, within a time interval. The initial load of the line associated with a certain node is The end load is Then through the formula The calculated load change amplitude reflects the degree of drastic change in line load per unit time. If it exceeds the preset load change amplitude threshold, it indicates that the line load change is abnormal.

[0027] Calculate the rate of temperature change of the connected lines at each node in the distribution network based on conductor temperature data, assuming that the time... The initial temperature of the internal wiring conductors is The final temperature is From the formula The calculated rate of temperature change reflects how fast the line temperature changes. When this rate exceeds the temperature change rate threshold, it means that the line temperature is rising too fast due to factors such as load, and there is a risk of overheating.

[0028] Calculate the voltage fluctuation amplitude of the connected lines at each node in the distribution network based on line voltage fluctuation data. For example, the rated voltage of a certain line is... The maximum value of its voltage fluctuation is The minimum voltage fluctuation is Through formula The voltage fluctuation amplitude is calculated, and this parameter reflects the stability of the line voltage. If it exceeds the voltage fluctuation amplitude threshold, it indicates that the line voltage stability is poor.

[0029] The harmonic distortion rate is calculated based on the line current harmonic data, assuming the effective value of the fundamental current is... The effective value of each harmonic current is (n=2,3,...), then by formula The calculated harmonic distortion rate reflects the degree of harmonic pollution in the line current. When it exceeds the harmonic distortion rate threshold, it indicates that the line has serious harmonic problems due to load and other reasons.

[0030] A line connected to a node is identified as a target load hotspot when it simultaneously meets the following criteria: load change amplitude exceeds a threshold, temperature change rate exceeds a threshold, voltage fluctuation amplitude exceeds a threshold, and harmonic distortion rate exceeds a threshold. If any one of these conditions is not met, the line is classified as a non-target load hotspot. For example, if a line's load change amplitude and temperature change rate both exceed their respective thresholds, but its voltage fluctuation amplitude does not exceed the voltage fluctuation amplitude threshold, then that line will not be identified as a target load hotspot. This multi-parameter fusion-based determination method can identify hotspots in the distribution network that truly pose a risk of overload, laying the foundation for subsequent targeted load control measures.

[0031] The basic charging and discharging scheduling strategy is obtained by inputting the load excess value, line impedance parameters, and vehicle scheduling characteristic parameters into the scheduling response correlation model. The specific steps include: Acquire load hotspot events and electric vehicle charging and discharging scheduling data of the distribution network during historical periods; Among them, load hotspot events include the occurrence time of load hotspots, the duration of load hotspots, the load excess value and the corresponding line impedance parameters, and electric vehicle charging and discharging scheduling data includes the number of dispatched vehicles, charging and discharging power allocation, actual adjustment effect and scheduling deviation value. A scheduling response correlation model is constructed based on load hotspot events and electric vehicle charging and discharging scheduling data. The basic charging and discharging scheduling strategy is obtained by inputting the vehicle scheduling characteristic parameters, load excess value and line impedance parameters of the scheduling scenario one into the scheduling response association model.

[0032] First, we acquire historical data on load hotspot events and electric vehicle charging / discharging dispatching for the distribution network. Load hotspot events include the time of occurrence, duration, excess load, and corresponding line impedance parameters. For example, a load hotspot might occur at 10:00 AM, last for 2 hours, have an excess load of 5MW, and correspond to a line impedance of 0.2Ω. Electric vehicle charging / discharging dispatching data covers the number of dispatched vehicles, charging / discharging power allocation, actual adjustment effect, and dispatching deviation. For instance, dispatching 100 electric vehicles with a 2kW discharge power allocation per vehicle might result in an actual load reduction of 180kW, with a dispatching deviation of 20kW.

[0033] Based on this historical data, a scheduling response correlation model is constructed. This model can learn the correlation patterns between load hotspot event parameters and electric vehicle charging and discharging scheduling data. The vehicle scheduling characteristic parameters, load excess value, and line impedance parameters of the scenario to be scheduled are input into the scheduling response correlation model. For example, in the scenario to be scheduled, the load excess value is 6MW, the line impedance is 0.25Ω, and the vehicle scheduling characteristic parameters include the urgency of charging demand, the discharge capacity level, and the user's charging and discharging willingness coefficient. The scheduling response correlation model outputs a basic charging and discharging scheduling strategy by calling upon historical correlation patterns, clarifying the number of electric vehicles that need to be scheduled, the charging and discharging power allocation and duration for each vehicle, thereby providing a scheduling basis for eliminating the current load hotspot.

[0034] By combining historical data on electric vehicle charging / discharging deviation rates and scheduling error values ​​induced by line impedance changes, the basic charging / discharging scheduling strategy is processed to obtain the first charging / discharging control command and the command to be analyzed. Specifically, this includes the following steps: The basic charging and discharging scheduling strategy includes the charging and discharging power allocation status and duration of vehicles in the priority scheduling queue; The charge / discharge difference value is obtained by extracting the difference between the actual charge / discharge amount and the planned charge / discharge amount of electric vehicles from historical data. The charge / discharge deviation rate is obtained by calculating the ratio between the charge / discharge difference and the planned charge / discharge amount. The impedance change of the target load hotspot line is acquired in real time, and the charging and discharging deviation status value of the target load hotspot line is obtained based on the impedance change and the charging and discharging deviation rate. The optimized scheduling strategy is obtained by calibrating the basic charge and discharge scheduling strategy based on the charge and discharge deviation values. The first charge / discharge control command and the analysis command are obtained by adjusting the load value according to the optimized scheduling strategy.

[0035] The basic charging and discharging scheduling strategy includes the charging and discharging power allocation status and duration of vehicles in the priority scheduling queue. For example, in a certain basic strategy, 50 electric vehicles are prioritized for scheduling, with each vehicle allocated a discharge power of 3kW and a scheduling duration of 2 hours.

[0036] The charge / discharge difference is calculated by extracting the difference between the actual and planned charge / discharge amounts of electric vehicles from historical data. For example, if a batch of electric vehicles has a planned charge / discharge amount of 1000 kWh and an actual charge / discharge amount of 900 kWh, the charge / discharge difference is 100 kWh. Then, the ratio of the charge / discharge difference to the planned charge / discharge amount is calculated to obtain the charge / discharge deviation rate: Charge / discharge deviation rate = (charge / discharge difference / planned charge / discharge amount) × 100%. In this case, the charge / discharge deviation rate is (100 / 1000) × 100% = 10%.

[0037] The impedance change of the target load hotspot line is acquired in real time. For example, if the impedance of a line was originally 0.3Ω, it becomes 0.35Ω after real-time monitoring, with an impedance change of 0.05Ω. Based on the impedance change and the charging / discharging deviation rate, the charging / discharging deviation status value of the target load hotspot line is obtained. This value comprehensively reflects the degree of dispatch deviation caused by the combined effect of electric vehicle charging / discharging deviation and line impedance change.

[0038] The basic charging and discharging scheduling strategy is calibrated based on the charging and discharging deviation values ​​to obtain an optimized scheduling strategy. For example, for the aforementioned 10% charging and discharging deviation rate and 0.05Ω impedance change, the original allocation of 3kW discharge power per vehicle for 50 electric vehicles and the duration of the allocation can be adjusted by increasing the number of vehicles or adjusting the power allocation, thereby obtaining a more accurate optimized scheduling strategy to better eliminate hot spots in the distribution network load.

[0039] The second charging and discharging control command is obtained by processing the load margin of the associated lines in the target load hotspot line and the dynamic access of electric vehicles according to the analysis command. The specific steps include: If the load value after optimization and scheduling is less than the preset node safety carrying threshold, then the first charging and discharging control command is output. If the load value after optimization and scheduling is greater than or equal to the preset node safety carrying capacity threshold, then an analysis instruction will be output.

[0040] The load at a distribution network node is adjusted according to an optimized scheduling strategy to obtain the adjusted load value. This load value is then compared with a preset safe load capacity threshold for the node. For example, if the preset safe load capacity threshold for a distribution network node is 10MW, and the load value after optimization is 8MW, since 8MW is less than 10MW, a first charge / discharge control command is output. This command directly guides the charging and discharging operation of electric vehicles to maintain the current load within a safe range.

[0041] If the preset safe carrying capacity threshold of another node is 12MW, and the load value after optimization and scheduling is 12MW, or the load value after regulation is 13MW (i.e., greater than or equal to the preset safe carrying capacity threshold), then an analysis command is output. This command will trigger further analysis of the load margin of the lines associated with the target load hotspot and the dynamic access of electric vehicles, thereby formulating a more comprehensive control strategy to ensure the safe and stable operation of the distribution network.

[0042] Based on the analysis instructions, the load margin of associated lines in the target load hotspot lines and the dynamic access volume of electric vehicles are judged to obtain the margin status characteristics, which specifically includes the following steps: After receiving the analysis command, determine the power supply range and topology of the target load hotspot line, and filter out the associated lines that have power interaction with the target load hotspot line based on the power supply range and topology. Collect rated load data, actual operating load data, and line impedance data of the associated lines; The load condition difference is obtained by calculating the difference between the rated load data and the actual operating load data in the associated lines; The load margin of the associated lines is obtained by correcting the load condition difference based on the line impedance data. The dynamic access volume of electric vehicles within the coverage area of ​​the associated line is obtained based on the connection stability signal between the electric vehicle and the associated line. The load margin of the associated lines is assigned to the corresponding judgment interval, and the dynamic access volume of electric vehicles is assigned to the corresponding judgment interval. The margin status characteristics of each associated line are generated based on the matching relationship between the interval to which the load margin belongs and the interval to which the dynamic access volume of electric vehicles belongs. The second charge / discharge control command is output after analyzing the margin state characteristics.

[0043] After receiving the analysis command, determine the power supply range and topology connection relationship of the target load hotspot line. For example, the power supply range of a target load hotspot line covers three surrounding communities and is connected to three branch lines in terms of topology. Based on this, filter out the associated lines that have power interaction with the target load hotspot line, assuming they are line A, line B and line C.

[0044] Data on the rated load, actual operating load, and line impedance of these related lines were collected. Taking line A as an example, its rated load is 20MW, its actual operating load is 15MW, and its line impedance is 0.15Ω.

[0045] Then, the difference between the rated load data and the actual operating load data in the associated line is calculated to obtain the load condition difference. For line A, the load condition difference is 20MW-15MW=5MW. The load margin of the associated line is obtained by correcting the load condition difference based on the line impedance data. The line impedance will affect the power transmission loss, thus affecting the actual load margin that can be carried. Load margin = load condition difference × (1-line impedance × actual operating load / rated voltage²). If the rated voltage is 10kV, then the load margin of line A is 5MW×(1-0.15×15 / (10²))≈5MW×(1-0.00225)≈4.98875MW.

[0046] The dynamic access volume of electric vehicles within the coverage area of ​​the associated line is obtained based on the connection stability signal between the electric vehicle and the associated line. For example, if there are 50 electric vehicles in the coverage area of ​​line A that are in a stable connection state and participate in charging and discharging scheduling, then the dynamic access volume of electric vehicles is 50.

[0047] The load margin of associated lines is assigned to corresponding judgment intervals. For example, the load margin of line A is approximately 4.98875MW, which is assigned to the "4-6MW" interval; the dynamic access volume of 50 electric vehicles is assigned to the "40-60 vehicles" interval. Finally, based on the matching relationship between the interval to which the load margin belongs and the interval to which the dynamic access volume of electric vehicles belongs, the margin status characteristics of each associated line are generated. For example, the load margin interval of line A has a high matching degree with the dynamic access volume interval of electric vehicles, generating a margin status characteristic that shows sufficient load margin and suitable electric vehicle access volume, with significant throttling potential. This provides detailed status basis for the subsequent output of the second charging and discharging control command.

[0048] The analysis of the margin state characteristics and the output of the second charge / discharge control command specifically include the following steps: The supplementary regulation power is obtained based on the margin state characteristics and charge / discharge deviation values. A second optimized scheduling strategy is formulated based on the supplementary adjustment power and the vehicle scheduling characteristic parameters of electric vehicles in the associated lines; wherein, the second optimized scheduling strategy includes the charging and discharging power allocation of vehicles in the alternative scheduling queue and the load allocation scheme of the associated lines; The second charge-discharge control command is obtained by correcting the supplementary adjustment power of the second optimized scheduling strategy based on the load regulation error correction coefficient, impedance change, and charge-discharge deviation value.

[0049] The supplementary regulation power is obtained based on the load margin characteristics and the charge / discharge deviation value. For example, the load margin characteristics of a certain associated line show that it has an 8MW regulation space and a charge / discharge deviation value of 5%. The supplementary regulation power = load margin × (1 - charge / discharge deviation value) = 8MW × (1 - 5%) = 7.6MW.

[0050] A second optimized scheduling strategy is formulated based on the supplementary regulation power and the vehicle scheduling characteristic parameters of electric vehicles within the associated lines. These vehicle scheduling characteristic parameters include the urgency of charging demand, discharge capacity level, and user charging / discharging willingness coefficient. For example, if there are 100 electric vehicles within the associated lines, and 50 of them have high discharge capacity levels and high user charging / discharging willingness coefficients, then the second optimized scheduling strategy includes the allocation of charging / discharging power for these vehicles in the alternative scheduling queue, such as 20kW discharge per vehicle. Simultaneously, a load allocation scheme for the associated lines is formulated, allocating 7.6MW of supplementary regulation power to the load control of that associated line.

[0051] The supplementary regulation power of the second optimized scheduling strategy is corrected based on the load regulation error correction coefficient, impedance change, and charge / discharge deviation value to obtain the second charge / discharge control command. Assuming the load regulation error correction coefficient is 0.9, the impedance change is 0.02Ω, and the charge / discharge deviation value is 5%, the corrected supplementary regulation power = original supplementary regulation power × load regulation error correction coefficient × (1 - impedance change × charge / discharge deviation value). The corrected supplementary regulation power is 7.6MW × 0.9 × (1 - 0.02 × 5%) ≈ 7.6 × 0.9 × 0.99 ≈ 6.7584MW. This generates the second charge / discharge control command containing the corrected power allocation, guiding the charging and discharging operation of electric vehicles and achieving precise elimination of hotspots in the distribution network.

[0052] A multi-parameter fusion-based distribution network local load hotspot elimination system, characterized in that it includes: The data acquisition module collects real-time status data of each node in the distribution network. Based on the real-time status data, it determines whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, it collects the load excess value and line impedance parameters of the target load line, and collects the vehicle dispatching characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line. Processing module: Inputs the load excess value, line impedance parameters and vehicle dispatching characteristic parameters into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy; Output module: Combines historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes to process the basic charging and discharging scheduling strategy to obtain the first charging and discharging control command and the demand analysis command; Based on the demand analysis command, processes the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles to obtain the second charging and discharging control command.

[0053] Electronic equipment, including memory, processor and computer program stored in memory and run on processor, wherein a method for eliminating local load hotspots in distribution networks by multi-parameter fusion is implemented when the processor executes the program.

[0054] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logic instructions in the memory 630 to execute a multi-parameter fusion method for eliminating local load hotspots in the distribution network.

[0055] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0056] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute a multi-parameter fusion method for eliminating local load hotspots in a distribution network.

[0057] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for eliminating local load hotspots in a distribution network by multi-parameter fusion.

[0058] The device embodiments described above are merely illustrative. 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; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0059] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for eliminating local load hotspots in a distribution network using multi-parameter fusion, characterized in that, The method includes the following steps: Real-time status data of each node in the distribution network is collected in real time. Based on the real-time status data, it is determined whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, the load excess value and line impedance parameters of the target load line are collected. Vehicle dispatch characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line are also collected. The load excess value, line impedance parameters, and vehicle dispatching characteristic parameters are input into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy. By combining historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes, the basic charging and discharging scheduling strategy is processed to obtain the first charging and discharging control command and the demand analysis command. Based on the demand analysis command, the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles are processed to obtain the second charging and discharging control command.

2. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion as described in claim 1, characterized in that, The real-time status data includes load data, conductor temperature data, line voltage fluctuation data, and line current harmonic data.

3. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion as described in claim 2, characterized in that, Determining whether a node's associated line is a target load hotspot line based on real-time status data includes the following steps: The load change amplitude of each node's associated line in the distribution network is calculated based on the load data. The temperature change rate of each node's associated line in the distribution network is calculated based on the conductor temperature data. The voltage fluctuation amplitude of each node's associated line in the distribution network is calculated based on the line voltage fluctuation data. The harmonic distortion rate of each node's associated line in the distribution network is calculated based on the line current harmonic data. If the load change amplitude of the connected line at a node in the distribution network exceeds the load change amplitude threshold, the temperature change rate exceeds the temperature change rate threshold, the voltage fluctuation amplitude exceeds the voltage fluctuation amplitude threshold, and the harmonic distortion rate exceeds the harmonic distortion rate threshold, then the connected line at that node is determined to be a target load hotspot line; otherwise, the connected line at that node is determined to be a non-target load hotspot line.

4. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion as described in claim 1, characterized in that, The basic charging and discharging scheduling strategy is obtained by inputting the load excess value, line impedance parameters, and vehicle scheduling characteristic parameters into the scheduling response correlation model, which includes the following steps: The vehicle dispatching characteristic parameters include the urgency of charging demand, the level of discharge capacity, and the user's willingness to charge and discharge. Acquire load hotspot events and electric vehicle charging and discharging scheduling data of the distribution network during historical periods; The load hotspot events include the occurrence time of the load hotspot, the duration of the load hotspot, the load excess value and the corresponding line impedance parameters. The electric vehicle charging and discharging scheduling data includes the number of vehicles scheduled, the charging and discharging power allocation, the actual adjustment effect and the scheduling deviation value. A scheduling response correlation model is constructed based on load hotspot events and electric vehicle charging and discharging scheduling data. The basic charging and discharging scheduling strategy is obtained by inputting the vehicle scheduling characteristic parameters, load excess value and line impedance parameters of the scheduling scenario one into the scheduling response association model.

5. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion according to claim 4, characterized in that, By combining historical data on electric vehicle charging / discharging deviation rates and scheduling error values ​​induced by line impedance changes, the basic charging / discharging scheduling strategy is processed to obtain the first charging / discharging control command and the command to be analyzed. Specifically, this includes the following steps: The basic charging and discharging scheduling strategy includes the charging and discharging power allocation status and duration of vehicles in the priority scheduling queue. The charge / discharge difference value is obtained by extracting the difference between the actual charge / discharge amount and the planned charge / discharge amount of electric vehicles from historical data. The charge / discharge deviation rate is obtained by calculating the ratio between the charge / discharge difference and the planned charge / discharge amount. The impedance change of the target load hotspot line is acquired in real time, and the charging and discharging deviation status value of the target load hotspot line is obtained based on the impedance change and the charging and discharging deviation rate. The optimized scheduling strategy is obtained by calibrating the basic charge and discharge scheduling strategy based on the charge and discharge deviation values. The first charge / discharge control command and the analysis command are obtained by adjusting the load value according to the optimized scheduling strategy.

6. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion as described in claim 5, characterized in that, The first charge / discharge control command and the analysis command are obtained by adjusting the load value according to the optimized scheduling strategy. The specific steps include: If the load value after optimization and scheduling is less than the preset node safety carrying threshold, then the first charging and discharging control command is output. If the load value after optimization and scheduling is greater than or equal to the preset node safety carrying capacity threshold, then an analysis instruction will be output.

7. The method for eliminating local load hotspots in a distribution network using multi-parameter fusion as described in claim 1, characterized in that, The second charging and discharging control command is obtained by processing the load margin of the associated lines in the target load hotspot line and the dynamic access of electric vehicles based on the analysis command. The specific steps include: After receiving the analysis command, determine the power supply range and topology of the target load hotspot line, and filter out the associated lines that have power interaction with the target load hotspot line based on the power supply range and topology. Collect rated load data, actual operating load data, and line impedance data of the associated lines; The load condition difference is obtained by calculating the difference between the rated load data and the actual operating load data in the associated lines; The load margin of the associated lines is obtained by correcting the load condition difference based on the line impedance data. The dynamic access volume of electric vehicles within the coverage area of ​​the associated line is obtained based on the connection stability signal between the electric vehicle and the associated line. The load margin of the associated lines is assigned to the corresponding judgment interval, and the dynamic access volume of electric vehicles is assigned to the corresponding judgment interval. The margin status characteristics of each associated line are generated based on the matching relationship between the interval to which the load margin belongs and the interval to which the dynamic access volume of electric vehicles belongs. The second charge / discharge control command is output after analyzing the margin state characteristics.

8. The method for eliminating local load hotspots in a distribution network by multi-parameter fusion according to claim 7, characterized in that, The analysis of the margin state characteristics and the output of the second charge / discharge control command specifically include the following steps: The supplementary regulation power is obtained based on the margin state characteristics and charge / discharge deviation values. A second optimized scheduling strategy is formulated based on the supplementary adjustment power and the vehicle scheduling characteristic parameters of electric vehicles in the associated lines; wherein, the second optimized scheduling strategy includes the charging and discharging power allocation of vehicles in the alternative scheduling queue and the load allocation scheme of the associated lines; The second charge-discharge control command is obtained by correcting the supplementary adjustment power of the second optimized scheduling strategy based on the load adjustment error correction coefficient, impedance change, and charge-discharge deviation value.

9. A multi-parameter fusion-based distribution network local load hotspot elimination system, applied to the multi-parameter fusion-based distribution network local load hotspot elimination method according to any one of claims 1 to 8, characterized in that, include: The acquisition module collects real-time status data of each node in the distribution network. Based on the real-time status data, it determines whether the node-related line is a target load hotspot line. When the node-related line is a target load hotspot line, it collects the load excess value and line impedance parameters of the target load line, and collects the vehicle dispatching characteristic parameters of electric vehicles within the coverage area of ​​the target load hotspot line. Processing module: Inputs the load excess value, line impedance parameters and vehicle dispatching characteristic parameters into the dispatching response correlation model to obtain the basic charging and discharging dispatching strategy; Output module: Combines historical data on electric vehicle charging and discharging deviation rates and scheduling error values ​​induced by line impedance changes to process the basic charging and discharging scheduling strategy to obtain the first charging and discharging control command and the demand analysis command; Based on the demand analysis command, processes the load margin of associated lines in the target load hotspot lines and the dynamic access of electric vehicles to obtain the second charging and discharging control command.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-parameter fusion method for eliminating local load hotspots in a distribution network as described in any one of claims 1 to 8.

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