Cell distribution transformer overload dynamic early warning method, system and device based on charging and discharging risk time sequence identification and medium

Abstract

A cell distribution transformer overload dynamic early warning method based on charge and discharge risk time sequence identification relates to the technical field of electric power prediction, and specifically comprises the following steps: obtaining multi-time sequence scale basic data and V2G parameters; constructing a dynamic bearing capacity boundary model of charge and discharge separation; constructing a risk early warning model based on a V2G cluster behavior and a dynamic bearing capacity boundary model simulated by Monte Carlo; an evaluation result of the risk early warning model is separated into a charging load and a discharging load, and distribution transformer load rates in the charging scene and the discharging scene are calculated respectively; carrying out risk judgment on the distribution transformer load rates of the charging scene and the discharging scene, and calculating the dynamic safety margin of the cell distribution transformer; outputting a multi-stage risk assessment result and an early warning result according to the risk judgment result and the dynamic safety margin condition; the method is used for solving the problem that a traditional distribution transformer overload evaluation result is large in deviation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power prediction, in particular to a cell distribution transformer overload dynamic early warning method, system, device and medium based on charging and discharging risk time sequence identification. BACKGROUND

[0002] With the rapid popularization of electric vehicles (EV) and the gradual popularization and application of vehicle-to-grid (V2G) technology, electric vehicles are no longer just a power load in the transportation field, but also a distributed energy storage resource with bidirectional charging and discharging adjustment capability.

[0003] And the number of electric vehicle users is also increasing day by day, and under the guidance of time-of-use electricity price and other incentive policies, electric vehicle users tend to charge in low electricity price periods and discharge in high electricity price periods to obtain economic benefits, and the charging and discharging area of electric vehicle users is generally concentrated in their residential communities. However, this charging and discharging behavior based on electricity price response is easy to form a highly synchronized cluster effect in a specific period, causing the distribution transformer in the residential community to face a serious overload risk in a short period of time, and even causing reverse power to be sent back, threatening the safe and stable operation of the distribution network.

[0004] Currently, the research on distribution transformer overload problems mainly focuses on static load prediction or simple superposition analysis based on average behavior, lacking a detailed description of the randomness, time sequence and coupling mechanism of V2G behavior and electricity price incentives. The existing early warning methods often fail to effectively distinguish between the two different nature of the charging and discharging impact process, and also fail to consider the evolution of user behavior patterns at different stages of V2G penetration rate, resulting in risk assessment results deviating from reality, making it difficult to provide accurate basis for power grid planning and operation scheduling.

[0005] The Chinese patent with publication number CN120473982A discloses an optimization scheduling method and system for electric vehicles participating in carbon emission reduction of distribution network based on V2G technology, which calculates the reduced external grid power purchase amount of V2G electric vehicles during the scheduling process, evaluates the carbon emission reduction benefit, and uses the result to optimize future scheduling strategy, realizes the system low-carbon goal, reduces the external power purchase demand, promotes carbon emission reduction, promotes the construction of low-carbon power grid, improves the accuracy of carbon emission calculation result, provides scientific basis for optimizing future scheduling strategy, and the double-layer model used can more comprehensively consider system load balancing and user cost minimization two goals, realizing double optimization; However, its optimization target is mainly carbon emission reduction and economy, lacking in-depth analysis of safety problems such as distribution transformer overload and reverse power sending back, and relying on ADMM for centralized-distributed coordination, making it difficult to accurately simulate the synchronous charging and discharging risk at peak period.

[0006] Therefore, a method for risk assessment of distribution transformer overload is proposed, and the assessment result is accurate. SUMMARY

[0007] The application aims to provide a cell distribution transformer overload dynamic early warning method, system, device and medium based on charging and discharging risk timing recognition, which is used to solve the problem of large deviation of traditional distribution transformer overload evaluation results.

[0008] The application is realized by the following technical solutions: A cell distribution transformer overload dynamic early warning method based on charging and discharging risk timing recognition, specifically comprising: Obtaining multi-time scale basic data and V2G parameters; Building a dynamic bearing capacity boundary model for charging and discharging separation; Based on the V2G cluster behavior and dynamic bearing capacity boundary model of Monte Carlo simulation, a risk early warning model is built; Separate the evaluation results of the risk early warning model into charging load and discharging load, and calculate the distribution transformer load rate under the charging and discharging scenarios respectively; Risk determination is made on the distribution transformer load rate of the charging and discharging scenarios, and the dynamic safety margin of the cell distribution transformer is calculated; According to the risk determination result and the dynamic safety margin, output the multi-stage risk assessment result and the early warning result.

[0009] Further, the multi-time scale basic data includes the rated capacity of the target distribution transformer , the typical daily basic load curve and the time-of-use price curve ; The V2G parameters include: penetration rate , single vehicle power , cluster size .

[0010] Further, the dynamic bearing capacity boundary model for charging and discharging separation is built, and the specific formula includes: Charging overload risk check:

[0011] Discharge reverse power risk check:

[0012] In the formula, Increased forward load, Forward power safety factor, Reverse power safety factor, Increased reverse load.

[0013] Further, the construction risk early warning model, the specific steps are: Set the number of simulations And initialize The state of each vehicle for each simulation; For each time point , iterate through each vehicle ; For each vehicle At time Generate a random number ; Compare the random number With the charge and discharge behavior probability density function According to the comparison result to determine the power state of the vehicle; For each simulation , calculate The power of the vehicle at time , that is:

[0014] In the formula, Indicates The charging and discharging power of the charging pile at time ; After all time points and all simulation times are completed, the statistical distribution of the vehicle load is obtained, and the expected value is taken as the final predicted load curve.

[0015] Further, the calculation formula of the final predicted load curve is: .

[0016] Further, the formula for calculating the load rate of the distribution transformer under the charging and discharging scenarios is:

[0017] .

[0018] Further, the formula for calculating the dynamic safety margin of the distribution transformer of the cell is: .

[0019] A cell distribution transformer overload dynamic early warning system based on charging and discharging risk time sequence identification, comprising: A data acquisition unit for acquiring multi-time scale basic data and V2G parameters; A boundary model construction unit for constructing a dynamic bearing capacity boundary model for charging and discharging separation; An early warning model construction unit for constructing a risk early warning model based on the V2G cluster behavior and the dynamic bearing capacity boundary model of the Monte Carlo simulation; A component calculation unit is configured to separate the evaluation result of the risk early warning model into charging load and discharging load, and calculate the distribution transformer load rate in the charging and discharging scenarios respectively; A risk determination and dynamic safety margin calculation unit is configured to determine the risk of the distribution transformer load rate in the charging and discharging scenarios, and calculate the dynamic safety margin of the distribution transformer in the cell. An analysis output unit is configured to output the multi-stage risk evaluation result and early warning result according to the risk determination result and the dynamic safety margin.

[0020] An electronic device comprises: A processor, a memory, and a communication interface. The memory is configured to store executable instructions of the processor. The processor is configured to execute the above-mentioned cell distribution transformer overload dynamic early warning method based on charging and discharging risk time sequence identification by executing the executable instructions.

[0021] A readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the above-mentioned cell distribution transformer overload dynamic early warning method based on charging and discharging risk time sequence identification.

[0022] The technical scheme of the present application has at least the following advantages and beneficial effects: The present application discloses a cell distribution transformer overload dynamic early warning method, system, device and medium based on charging and discharging risk time sequence identification, which constructs a dynamic bearing capacity boundary model and a V2G cluster behavior based on Monte Carlo simulation, and further constructs a risk early warning model, which not only simulates the randomness of user behavior, but more importantly, can identify the time sequence characteristics of different risks, thereby realizing early and accurate early warning of the primary risk and gaining valuable time for intervention measures.

[0023] By separating the evaluation, the problem of using "net load" calculation to mask the real overload risk is avoided, making the risk evaluation result more accurate and reliable, and accurately revealing the real source of risk.

[0024] The safety margin can clearly indicate the "remaining safety distance" of the system from the danger boundary, providing a direct and scientific decision basis for the operation control and planning reconstruction of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A flowchart of a cell distribution transformer overload dynamic early warning method of the present application; Figure 2 A structural diagram of a cell distribution transformer overload dynamic early warning system of the present application; Figure 3 A schematic diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0026] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0027] Embodiment 1 As Figure 1 shown, a cell distribution transformer overload dynamic early warning method based on charge and discharge risk timing identification, specifically comprising: obtaining multi-time scale basic data and V2G parameters; The multi-time scale basic data describes the inherent characteristics of the power grid system itself and the external market environment, which is the basis and background of risk assessment, and specifically includes: the rated capacity of the target distribution transformer , which is a fixed and unchangeable physical parameter set by the transformer manufacturer, indicating the maximum apparent power that the transformer can withstand under long-term continuous operation, which represents the maximum service capability limit of the power supply equipment; and its function is: used to measure all overload risks, wherein the calculation formula of the load rate is: , which can directly reflect the pressure level of the transformer; In addition, the rated capacity of the target distribution transformer is the basis for calculating the safety threshold in the boundary model, without which all load data lose the judgment standard; Typical daily basic load curve , which is the curve of the daily electricity consumption of residents in the community over time without V2G load, which presents time sequence characteristics, and usually has an early peak, a late peak and a night low; Time-of-use price curve , which is an economic lever set by the power grid company to encourage users to adjust their electricity consumption behavior, and its core principle is price demand elasticity—by setting low and high valley prices, users are guided to use electricity in the low valley and sell electricity to the grid in the high valley; The V2G parameters describe the technical characteristics and scale state of the V2G cluster itself, which are the object and variable of risk assessment, and specifically include: penetration rate , which refers to the proportion of households in the community that own V2G electric vehicles, reflecting the popularity of V2G technology, which is a macro statistical parameter; Single vehicle power , which refers to the rated power of a single electric vehicle when charging and discharging, which is a technical parameter determined by the specifications of the on-board charger and charging pile; Cluster size The total number of electric vehicles participating in V2G in the cell is the most direct scale parameter.

[0028] A dynamic load-carrying capacity boundary model is constructed for charging and discharging separation. The construction idea of the dynamic load-carrying capacity boundary model is to change from the traditional concept of "static and single" to the modern evaluation system of "dynamic, bidirectional and risk separation". The model is based on multi-time scale basic data and V2G parameters. The specific formula of the model includes: Charging overload risk verification:

[0029] Discharge reverse power risk verification:

[0030] In the formula, Increased forward load, Forward power safety factor, Reverse power safety factor, Increased reverse load; That is, the boundary of dynamic load-carrying capacity is no longer a fixed value, but a curve that changes with time. This is because the basic load It changes dynamically with time The boundary is specifically: This means that: In periods of high basic load, the system's ability to withstand additional V2G load, i.e. the margin, is small. In periods of low basic load, the system's ability to withstand additional V2G load, i.e. the margin, is large. In addition, for charging overload risk verification, electric vehicles act as loads, directly drawing power from the grid. When this power is added to the basic load of the cell, it will increase the transformer current, causing the winding to overheat and the insulation to age. Therefore, the significance of charging overload risk verification is that the sum of "basic load" and "V2G charging load" cannot exceed the upper limit of the transformer's safe operation, A safety factor less than 1, leaving the necessary safety margin for the transformer to prevent it from being in a long-term full-load limit state; For discharge reverse power risk, electric vehicles act as power sources, feeding power back to the grid. When the feedback power exceeds the local load at that time, the power begins to flow in the opposite direction. This can cause the voltage to rise above the upper limit, damaging user appliances or causing false positives in protection devices, triggering false actions. Therefore, the significance of discharge reverse power risk is that the difference between "basic load" and "V2G discharge load" cannot be lower than a reverse power threshold.

[0031] In addition, considering the aging of the transformer, the sudden situation and the need for certain backup capacity, it is not allowed to run at 100% load for a long time, so It can be set to 0.8 or 0.9; while the power distribution system is designed for one-way power flow, and is weaker in tolerating reverse power, therefore, the allowed reverse power threshold is usually lower than the forward overload threshold, that is It can be set to -0.2 or -0.3.

[0032] Based on the V2G cluster behavior and dynamic carrying capacity boundary model based on Monte Carlo simulation, a risk early warning model is constructed; this step is mainly to quantify the risk by simulating uncertainty, and compare the quantified risk with the explicit safety boundary, so as to realize the paradigm shift from "post analysis" to "pre warning", and the realization is to aggregate the realistic cluster load curve through Monte Carlo simulation of the random decision-making process of a large number of V2G units under the price incentive; The evaluation results of the risk early warning model are separated into charging load and discharging load, and the distribution transformer load rate in the charging and discharging scenarios is calculated respectively, and the specific formula is:

[0033] ; This is because the charging risk and discharging risk of V2G cluster behavior to distribution transformer are completely different in physical nature, mechanism and consequences, and mixing them together will seriously mislead risk assessment and subsequent control decisions; The distribution transformer load rate in the charging scenario and the discharging scenario is judged for risk, and the dynamic safety margin of the community distribution transformer is calculated, and the calculation formula is:

[0034] The purpose is to transform complex data into clear and operable management instructions; According to the risk judgment result and the dynamic safety margin, the multi-stage risk assessment result and the early warning result are output; Among them, when outputting the multi-stage risk assessment result and outputting the early warning result, the load rate of the separated charging and discharging scenarios can clearly distinguish whether the primary risk is charging overload or discharging reverse sending, and what the root cause of the risk is, so as to ensure that the subsequent control measures have strong pertinence; if the charging risk is determined to be the main risk, the solution will focus on orderly charging and inhibition of synchronism; if it is discharging risk, reverse power needs to be managed; And the specific process is: first, determine the peak value of the distribution transformer load rate in the charging scenario and the discharging scenario; Then, according to the pre-set risk level, the peak value of the distribution transformer load rate is determined for graded risk assessment; Next, we will analyze the load rate curve to identify the specific time period in which the risk occurred; Finally, combining the risk assessment results and the time period of the risk occurrence, an early warning is issued based on the safety margin. That is, as the V2G penetration rate increases, the safety margin will decrease. Therefore, when the safety margin is less than the threshold, an early warning reminder of the corresponding risk level will be issued.

[0035] Example 2 As one embodiment, the specific steps for constructing the risk warning model are as follows: Set the number of simulations Assuming 10,000 simulations, and initializing for each simulation... The condition of the vehicle; For each time point traverse each car ; For each vehicle At any moment Generate a random number The aim is to reproduce and capture the inherent, unpredictable randomness of V2G user decisions in the real world through computer simulations. random numbers With charge and discharge behavior probability density function The comparison is performed, and the power status of the vehicle is determined based on the comparison results; Among them, under the charging decision, if The vehicle status is charging, and the power is... ; Under the discharge decision, if Then the vehicle is in a discharge state with a power of ; Under the idle decision: if the charging decision and discharging decision are not satisfied, the vehicle state is idle and the power is 0; Therefore, the vehicle's power state includes , There are three types: 0 and 0.

[0036] In addition, the probability of charging and discharging behavior and It is the electricity price incentive function and permeability correction function The product of these factors, after coefficient calibration, is obtained. The electricity price incentive function reflects the nonlinear response characteristics of users to time-of-use pricing and can be defined using a logic function or a piecewise function. The penetration rate correction function reflects the differences in user adoption behavior at different stages of V2G technology adoption and can be defined using a model based on innovation diffusion theory. The specific parameters of these functions can be fitted and calibrated using historical data, questionnaires, or expert experience. The charging and discharging behavior probability... and After generation, normalization processing is needed to ensure that the sum is always no more than 1. The normalization processing can be realized by scaling or probability distribution based on the Softmax function. The remaining probability after normalization is automatically assigned to the idle state, that is .

[0037] For each simulation , the power of the vehicle at time is calculated, that is:

[0038] In the formula, is the charging / discharging power of the charging pile at time , that is ; After all time points and all simulation times are completed, the statistical distribution of the vehicle load is obtained, and the expected value thereof is taken as the final predicted load curve, and the calculation formula is:

[0039] The statistical distribution describes the uncertainty of the V2G load at the time, and the expected value of the distribution is the most core and central position feature of the distribution, which is used to represent the most likely load level; the core purpose of this step is to filter out random fluctuations, identify and extract the stable and systematic regular behavior hidden behind a large number of random simulations, and thus generate a unique and reliable baseline prediction curve.

[0040] Embodiment 3 As an embodiment, a community distribution transformer overload dynamic early warning system based on charging and discharging risk timing identification, as shown in Figure 2 , comprises: A data acquisition unit for acquiring multi-time scale basic data and V2G parameters; A boundary model construction unit for constructing a dynamic bearing capacity boundary model for charging and discharging separation; A warning model construction unit for constructing a risk warning model based on the V2G cluster behavior of Monte Carlo simulation and the dynamic bearing capacity boundary model; A component calculation unit for separating the evaluation results of the risk warning model into charging load and discharging load, and calculating the distribution transformer load rate under the charging and discharging scenarios respectively; A risk judgment and dynamic safety margin calculation unit for risk judgment on the distribution transformer load rate in the charging and discharging scenarios, and calculation of the dynamic safety margin of the community distribution transformer; The analysis output unit is used to output multi-stage risk assessment results and early warning results based on the risk assessment results and dynamic safety margin.

[0041] Example 4 As one example, such as Figure 3 An electronic device shown includes: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the above-mentioned dynamic early warning method for overload of distribution transformers in a residential area based on charging and discharging risk timing identification by executing the executable instructions.

[0042] A readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the above-mentioned dynamic early warning method for overload of distribution transformers in residential areas based on charging and discharging risk timing identification.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamic early warning of overload in community distribution transformers based on charging and discharging risk timing identification, characterized in that, Specifically, it includes: Acquire multi-time-scale basic data and V2G parameters; Construct a dynamic load-bearing capacity boundary model for charge-discharge separation; A risk warning model is constructed based on the V2G cluster behavior and dynamic carrying capacity boundary model of Monte Carlo simulation; The assessment results of the risk warning model are separated into charging load and discharging load, and the transformer load rate under charging and discharging scenarios is calculated separately. Risk assessment of transformer load rate in charging and discharging scenarios, and calculation of dynamic safety margin of transformer in the community; Based on the risk assessment results and dynamic safety margin, multi-stage risk assessment results and early warning results are output.

2. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification as described in claim 1, characterized in that: The multi-time-scale basic data includes: the rated capacity of the target distribution transformer. Typical daily base load curve and time-of-use electricity price curve ; The V2G parameters include: permeability. Single vehicle power Cluster size .

3. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification as described in claim 2, characterized in that: The specific formulas for constructing the dynamic bearing capacity boundary model with charge-discharge separation include: Charging overload risk verification: Reverse power discharge risk verification: In the formula, Increased positive load, For positive power safety factor, For reverse power safety factor, Increased reverse load.

4. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification as described in claim 1, characterized in that: The specific steps for constructing the risk early warning model are as follows: Set the number of simulations and initialize for each simulation The condition of the vehicle; For each time point traverse each car ; For each vehicle At any moment Generate a random number ; random numbers With charge and discharge behavior probability density function The comparison is performed, and the power status of the vehicle is determined based on the comparison results; For each simulation ,calculate Vehicles at all times The power, that is: In the formula, refer to Instant charging station The charging / discharging power; After completing all time points and all simulations, the statistical distribution of vehicle load is obtained, and its expected value is taken as the final predicted load curve.

5. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification as described in claim 4, characterized in that: The formula for calculating the final predicted load curve is: 。 6. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification as described in claim 5, characterized in that: The specific formula for calculating the transformer load rate under charging and discharging scenarios is as follows: 。 7. The method for dynamic early warning of overload of distribution transformer in a residential area based on charging and discharging risk timing identification according to claim 6, characterized in that: The formula for calculating the dynamic safety margin of a distribution transformer in a residential area is: 。 8. A dynamic early warning system for overload of distribution transformers in a residential area based on charging and discharging risk timing identification, characterized in that, include: The data acquisition unit is used to acquire multi-time-scale basic data and V2G parameters; Boundary model building unit, used to construct dynamic bearing capacity boundary model for charge-discharge separation; The early warning model building unit is used to construct a risk early warning model based on the V2G cluster behavior and dynamic carrying capacity boundary model based on Monte Carlo simulation; The component calculation unit is used to separate the evaluation results of the risk warning model into charging load and discharging load, and calculate the transformer load rate under charging and discharging scenarios respectively. The risk assessment and dynamic safety margin calculation unit is used to assess the risk of the transformer load rate in charging and discharging scenarios and calculate the dynamic safety margin of the transformer in the community. The analysis output unit is used to output multi-stage risk assessment results and early warning results based on the risk assessment results and dynamic safety margin.

9. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the dynamic early warning method for overload of distribution transformers in a residential area based on charging and discharging risk timing identification as described in any one of claims 1-7 by executing the executable instructions.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic early warning method for overload of distribution transformers in residential areas based on charging and discharging risk timing identification as described in any one of claims 1-7.

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