Configuration scale evaluation method, device and equipment for stock power distribution network station area charging piles

Abstract

The invention discloses a configuration scale evaluation method, device and equipment for stock power distribution network station area charging piles, and relates to the technical field of charging pile planning and construction. The method comprises the following steps: acquiring load data of a stock power distribution network area and resident travel data of a target area; predicting a charging load demand of the electric vehicle based on the resident travel data; establishing a charging pile site selection optimization model by taking the minimization of the payback period as a target and combining the reconstruction and construction cost of the power distribution network; calculating the operation income of the charging pile based on the charging load demand, solving the optimization model according to the operation income, and obtaining the optimal layout of the charging pile; and calculating the average maximum operation risk of the power distribution network under the optimal layout, and determining the number of acceptable charging piles in the power distribution network area according to a risk limit value. The method is used for solving the problems that in stock power distribution network planning in the prior art, load prediction accuracy is low, economic evaluation is distorted due to neglecting of transformation cost, and power grid acceptance capability evaluation lacks a quantitative risk basis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile planning and construction, and more particularly to a configuration scale evaluation method, device and equipment for charging piles in a stock power distribution network area. BACKGROUND

[0002] The number of electric vehicles (EVs) continues to rise, and the demand for charging infrastructure construction is increasingly urgent. For a large number of stock power distribution network areas in cities, how to scientifically and reasonably plan the construction scale and layout position of charging piles under the limited capacity of the power grid has become a key problem in the field of power distribution network planning. The stock power distribution network is different from the newly built power grid, its network structure is fixed, the capacity of the transformer and the line is limited, and the charging load of electric vehicles has high randomness and unevenness in time and space distribution. Large-scale unordered access is likely to cause voltage out-of-limit, transformer overload and other safety problems, and also faces economic challenges such as difficulty in expansion and reconstruction, uncertainty of investment return period, etc.

[0003] The existing charging pile planning and evaluation technology mainly includes two types: evaluation based on power grid carrying capacity and planning based on traffic demand. The former usually calculates the remaining capacity according to the rated capacity of the transformer minus the peak value of the conventional load, which is used as a hard constraint for the number of charging piles; the latter more often uses traffic flow data to locate the site with the minimum service radius or the highest charging pile utilization rate, and in the economic evaluation, usually only considers the purchase cost, installation cost and operation and maintenance cost of the charging pile equipment, and determines the construction scheme by minimizing the construction cost or maximizing the service income. For safety evaluation, the existing technology mostly uses deterministic power flow calculation, and whether the node voltage and branch current exceed the allowed range is used as a single judgment standard.

[0004] However, the above existing technology has significant deficiencies when applied to stock power distribution network areas: first, in terms of load prediction, existing methods often assume that user behavior follows a simple normal distribution, lack of detailed characterization of the coupling relationship between different travel purposes (residence, office, business) and parking time, charging behavior, and are difficult to accurately reflect the different load "tide" characteristics of weekdays and weekends, office areas and residential areas, resulting in a large deviation between the predicted results and the actual situation. Secondly, in terms of economic site selection, existing models generally ignore the "invisible reconstruction cost" of the stock power grid, such as line reconstruction and transformer capacity increase, which must be carried out due to the access of new loads, resulting in a site selection scheme that appears to have low equipment investment, but actually causes high power grid reconstruction costs, and the investment recovery period is seriously distorted. Finally, in terms of receiving capacity evaluation, the traditional "hard limit" judgment method cannot quantify the severity and probability risk of over-limit, resulting in evaluation results that are either too conservative, leading to waste of remaining capacity, or too risky, ignoring the potential harm of serious over-limit, making it difficult to find the best balance point between safety risk and receiving scale. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a method for evaluating the configuration scale of charging piles in a stock power distribution network area, which analyzes the charging load demand of electric vehicles based on resident travel data, establishes a minimum payback period site selection model considering the transformation cost of the power distribution network, and introduces an index type severity function risk assessment capacity determination mechanism to solve the problems of low load prediction accuracy, distorted economic evaluation due to ignoring transformation cost, and lack of quantitative risk basis for grid accommodation capacity evaluation in the stock power distribution network planning of the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The method for evaluating the configuration scale of charging piles in a stock power distribution network area comprises the following steps: obtaining load data of a stock power distribution network area and resident travel data of a target area; based on the resident travel data, a mapping relationship of travel purpose, travel time and driving characteristics is established, and the charging load demand of electric vehicles is predicted based on the mapping relationship; a charging pile site selection optimization model is established by taking the minimum payback period as the objective function and combining the transformation and construction cost of the power distribution network line and transformer; the operating income of the charging pile is calculated based on the charging load demand, and the optimal layout of the charging pile is obtained by solving the optimization model; based on the optimal layout, the charging load demand is superimposed on the load data for power flow calculation, the average maximum operating risk of the power distribution network is calculated, and the acceptable number of charging piles in the power distribution network area is determined according to the risk limit value.

[0007] In a preferred embodiment, the average maximum operating risk uses an exponential form of the severity function to nonlinearly quantify and amplify the risks of voltage out-of-limit, current out-of-limit and transformer overload, and the sensitivity to the severity of out-of-limit is controlled by adjusting the amplification factor.

[0008] In a preferred embodiment, the average maximum operating risk uses an exponential form of the severity function to nonlinearly amplify the risks of voltage out-of-limit, current out-of-limit and transformer overload, and the sensitivity to the severity of out-of-limit is controlled by adjusting the amplification factor.

[0009] In a preferred embodiment, the mapping relationship of travel purpose, travel time and driving characteristics specifically comprises: setting different parking duration probability distributions for different travel purposes; using a three-peak mixed Gaussian distribution to fit the daily first travel time of electric vehicles; using a lognormal distribution to fit the driving time and driving distance of electric vehicles; based on the fitted time and distance distribution, the parking duration of different travel purposes is combined to generate the continuous travel and charging behavior chain of electric vehicles.

[0010] In a preferred embodiment, establishing the mapping relationship between travel purpose, travel time, and driving characteristics, and using this to predict the charging load demand of electric vehicles, specifically includes: fitting the first daily travel time of electric vehicles using a three-peaked mixed Gaussian distribution; fitting the driving time and driving distance of electric vehicles using a log-normal distribution; and predicting the charging load demand of electric vehicles based on the fitted time and distance distributions, combined with the parking duration for different travel purposes.

[0011] In a preferred embodiment, the investment payback period is the year in which the cumulative net profit of the charging pile over its entire life cycle is zero; in the calculation of the cumulative net profit, the cost items specifically include the charging pile construction cost, the estimated line reconstruction investment cost, the public transformer renovation cost, parking space rental and operation and maintenance costs.

[0012] In a preferred embodiment, the charging pile site selection optimization model is solved iteratively using a bisection method, including: calculating the cumulative net profit at the end of the charging pile's service life; if it is less than zero, it is determined that the cost cannot be recovered; setting an initial upper and lower bound for the time interval; iteratively calculating the cumulative net profit of the middle year; continuously narrowing the time interval according to the positive or negative sign of the net profit; until finding the year in which the cumulative net profit converges to zero; the combination of charging pile location and type corresponding to that year is the optimal solution.

[0013] In a preferred embodiment, the average maximum operating risk is a weighted average of voltage over-limit risk, branch current over-limit risk, and distribution transformer overload risk.

[0014] In a preferred embodiment, the exponential severity function is expressed as follows:

[0015] In the formula, Severity of loss; This is the amplification factor used to adjust the sensitivity to the severity of the loss; The normalized loss amount; for the overload risk of distribution transformers, its loss amount Defined as: when the transformer load factor Less than or equal to the overload limit value hour, =0; when Greater than hour, .

[0016] In a preferred embodiment, determining the acceptable number of charging piles in a distribution network area includes: conducting several Monte Carlo samplings of the travel behavior of electric vehicle users; and calculating the maximum daily operational risk of the distribution network for each sampling. ; Calculate the average maximum operating risk Where M is the number of samplings; adjust the number of charging piles connected to the power distribution network, when the average maximum operating risk Reaching the preset risk limit value of the distribution network At that time, the corresponding number of charging piles is the number of acceptable charging piles in that existing distribution network area.

[0017] This invention provides a device for assessing the configuration scale of charging piles in existing power distribution network areas, comprising: The system comprises the following modules: a data acquisition module for acquiring load data from existing distribution network areas and resident travel data from target areas; a load forecasting module for establishing a mapping relationship between travel purpose, travel time, and driving characteristics based on resident travel data, and forecasting the charging load demand of electric vehicles; a site selection optimization module for establishing a charging pile site selection optimization model with the objective function of minimizing the investment payback period, combined with the renovation and construction costs of distribution network lines and transformers, calculating the operating revenue of charging piles based on the charging load demand, and solving the optimization model to obtain the optimal layout of charging piles; and a risk assessment and capacity determination module for superimposing the charging load demand onto the load data based on the optimal layout, calculating the average maximum operating risk of the distribution network, and determining the acceptable number of charging piles in the distribution network area based on the risk limit value. The average maximum operating risk is nonlinearly amplified using an exponential severity function to assess the risks of voltage over-limit, current over-limit, and transformer overload, and the sensitivity to the severity of over-limit is controlled by adjusting the amplification factor.

[0018] A device for assessing the configuration scale of charging piles in existing power distribution network areas includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement the various steps of the method for assessing the configuration scale of charging piles in existing power distribution network areas.

[0019] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of a method for assessing the configuration scale of charging piles in existing power distribution network areas.

[0020] The technical effects and advantages of the method for evaluating the configuration scale of charging piles in existing distribution network areas in this invention are as follows: This invention analyzes the charging load demand of electric vehicles based on resident travel survey data, accurately predicting the distribution characteristics of charging load in different times and spaces, effectively improving the accuracy of load forecasting. By establishing an optimization model with the goal of minimizing the investment payback period and combining the renovation and construction costs of distribution network lines and transformers, the expansion costs of the existing power grid are explicitly incorporated into the site selection decision, avoiding the distortion of investment assessment caused by only considering construction costs, and achieving an economic balance between commercial benefits and power grid renovation costs. At the same time, by introducing an exponential severity function to nonlinearly quantify and amplify the risks of voltage overruns, current overruns, and transformer overloads, it can sensitively distinguish between minor and severe overrun risks, thereby scientifically determining the acceptable number of charging piles in the distribution network area while ensuring the safe operation of the power grid, maximizing the acceptance potential of the existing distribution network. Attached Figure Description

[0021] Figure 1 A schematic diagram of the process for evaluating the configuration scale of charging piles in existing distribution network areas, provided in an embodiment of the present invention; Figure 2 A simplified power distribution network structure diagram provided for embodiments of the present invention; Figure 3 This is a schematic diagram of node voltage distribution provided in an embodiment of the present invention; Figure 4 A schematic diagram of charging demand on Mondays and Sundays provided for embodiments of the present invention; Figure 5 The node voltage distribution including EV charging load is provided for embodiments of the present invention; Figure 6 A block diagram of the configuration scale assessment device for existing power distribution network charging piles provided in an embodiment of the present invention; Figure 7 A structural block diagram of an exemplary electronic device provided for implementing embodiments of the present disclosure; Figure 8 This is a schematic diagram of an exemplary storage medium that can be used to implement embodiments of the present disclosure, as provided in the embodiments of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1, Figure 1 This invention presents a method for assessing the configuration scale of charging piles in existing distribution network areas, comprising the following steps: S1: Obtain load data of existing distribution network areas and resident travel data of the target area.

[0024] In this embodiment, the specific steps of S1 are as follows: S101, Construct a physical model of the existing power distribution network.

[0025] Obtain the distribution network topology of the area to be evaluated, such as Figure 2 As shown, this embodiment selects an actual power distribution network area, which includes six 110 / 10 kV substations with a total of 34 outgoing lines. Among them, node 1 is the equivalent node of the upper-level system, node 2 is the 110 kV bus, and nodes 3, 14, 20, 25, 28 and 40 are 10 kV substation bus nodes.

[0026] Meanwhile, the equipment parameters of each substation were obtained, as shown in Table 1. The transformer models and capacity configurations of each node were entered, and the physical capacity boundaries of the power grid were determined.

[0027] Table 1

[0028] S102, Obtain the regular load data of the existing distribution network.

[0029] The standard load data of each node when it is not connected to electric vehicle charging load is obtained, including active power and reactive power. In this embodiment, the standard load of key nodes (such as nodes 11, 12, and 13) and their respective lines is collected and analyzed in detail. As shown in Table 2, taking the line where node 11 is located as an example, the obtained standard load data is in complex form, indicating that the standard load level of this line is already relatively high.

[0030] Table 2

[0031] Furthermore, data on the composition of conventional loads were obtained to refine load characteristics. As shown in Table 3, the loads were subdivided into Class A, Class B, and Class C loads. For example, Class B loads accounted for 3.98% of node 11, and Class B loads accounted for 24.7% of the overall line load. These data reflect the current load composition of the existing power grid and are a key benchmark for determining whether new charging piles can be added.

[0032] Table 3

[0033] S103 uses the acquired topology and conventional load data to perform initial power flow calculations, verify the accuracy of the data, and assess the current voltage level of the power grid.

[0034] like Figure 3As shown, the calculation results show that when there is only conventional load, the node voltage distribution is between 0.95 and 1.05 per unit (minimum 0.956), proving that the original distribution network is operating within a safe range and has a certain residual load capacity for connecting electric vehicles.

[0035] S104, Obtain resident travel data for the target area.

[0036] This embodiment uses the national household travel survey dataset as the data source to extract residents' travel characteristics. As shown in Table 4, the differences in travel frequency between weekdays and weekends for six types of destinations, including "going home," "shopping," and "going to work," are statistically analyzed. For example, the proportion of trips to work on weekdays is 16.30%, while it is only 3.98% on weekends.

[0037] Table 4

[0038] S2 establishes a mapping relationship between travel purpose, travel time and driving characteristics based on residents' travel data, and uses this to predict the charging load demand of electric vehicles.

[0039] In this embodiment, step S2 includes: S201, Establish a travel time model based on a three-peaked mixed Gaussian distribution: For the first trip of electric vehicles each day, this embodiment uses a trimodal Gaussian mixture distribution for fine-tuning. As shown in Table 5, the fitting parameters show that the weekday travel peak exhibits a typical trimodal characteristic. The table shows traffic flow during the morning rush hour and midday travel, respectively. This distribution more accurately reflects real traffic flow than a simple normal distribution. Represents the weight of each peak. The mean of all peaks. denoted as the standard deviation of each peak.

[0040] Table 5

[0041] S202, Establish a probabilistic model of driving characteristics and parking duration: A log-normal distribution was used to fit the driving distance and driving time, and a functional relationship between the two was constructed. As shown in Table 6, the power function relationship between driving distance and time was established through parameters a and b, which was used to estimate the remaining battery power (SOC) after the vehicle reaches its destination. and These are the mean and standard deviation of the travel distance, respectively.

[0042] Table 6

[0043] Meanwhile, differentiated parking duration models were established for different travel purposes obtained from S1. As shown in Table 7, parking behavior varies significantly across different destinations. For example, the average parking duration for "work" destination (weekdays) is significantly higher. The parking time is much longer than that at "shopping" destinations (weekdays). Based on this, this embodiment determines whether the vehicle is suitable for slow charging: slow charging is recommended for long-term stops (such as going to work or returning home), and fast charging is recommended for short-term stops.

[0044] Table 7

[0045] S203, combining vehicle model parameters to predict the spatiotemporal distribution of charging load: Set the battery parameters for the simulated vehicle. As shown in Table 8, select representative vehicle parameters such as the Tesla Model 3 (63% market share) as input.

[0046] Table 8

[0047] Based on the above model, a spatiotemporal simulation is performed, outputting the predicted charging load demand. For example... Figure 4 As shown, the left figure represents weekdays and the right figure represents weekends. The prediction results reveal significant spatiotemporal distribution characteristics: the charging demand in office areas is much higher on weekdays than on weekends, and is concentrated during the day; while the load in residential areas is slightly higher on weekends than on weekdays.

[0048] The beneficial effects of this embodiment are as follows: By accurately acquiring and benchmarking the conventional load data of the existing power grid in step S1, and combining this with refined load forecasting based on a three-peak Gaussian distribution and travel purpose classification in step S2, this method achieves a precise match between the "current state of the power grid" and "new demand." This ensures that subsequent site selection is not based on an idealized blank power grid, but on a real existing power grid that already carries a certain load, thus guaranteeing the feasibility of the assessment results.

[0049] S3 establishes an optimization model for charging pile site selection, taking the minimization of the investment payback period as the objective function and combining the renovation and construction costs of distribution network lines and transformers.

[0050] It should be noted that existing charging pile site selection technologies typically only consider the equipment purchase cost, installation cost, and land lease cost of the charging pile itself. This traditional approach may be feasible in newly built power grids or areas with ample capacity, but it has serious drawbacks in the "existing distribution network" scenario addressed in this application: the transformer and line capacity of the existing power grid is usually close to saturation, as shown in Table 2 in step S1 of this embodiment, where some nodes already have high conventional loads. If charging piles are forcibly connected solely based on geographical convenience, it often triggers a mandatory expansion demand for the distribution network, leading to the need to replace large-capacity transformers or re-lay cables. In traditional models, this huge "hidden renovation cost" is ignored, resulting in a severely distorted investment return period, and even situations where there is "a profit on paper, but a huge loss due to network renovation costs." To solve the above problems, this embodiment explicitly introduces the line reconstruction investment cost and the public transformer renovation cost into the cost model. This improvement transforms the physical "hard constraint" of power grid capacity into an economic "soft constraint." The significant technical benefit is that, during the optimization process, the algorithm automatically senses the remaining grid capacity value of each node. For nodes with heavy conventional loads and expensive retrofitting costs for new charging stations, the algorithm automatically lowers the construction priority of these nodes due to the surge in retrofitting costs and the resulting longer payback period. Conversely, the algorithm guides the deployment of charging stations to nodes with ample remaining capacity. This mechanism achieves an automatic balance between "commercial site selection benefits" and "grid security costs" without human intervention, ensuring that the assessed configuration scale is truly economically feasible at the engineering implementation level.

[0051] In this embodiment, step S3 includes the following steps: S301, based on the charging load demand of electric vehicles, constructs a cumulative revenue model for the entire life cycle of charging piles: Assume the service life of the charging pile is In the year, based on the spatiotemporal load distribution generated in S2, the [number]th [year] was determined. Estimated annual charging volume And set the ratio of actual charging to estimated charging as Calculate the charging pile accordingly. In the Annual operating revenue, i.e., cumulative revenue The formula for calculating the accumulated income is as follows: , In the formula, For the current year being calculated, .

[0052] S302, Construct a cumulative total cost model that includes investment in upgrading existing distribution networks: The total accumulated cost includes not only the direct investment in new charging piles but also the costs of power distribution network upgrades incurred due to the connection of charging piles. Specifically, the cost items include charging pile construction costs, estimated line reconstruction investment costs, public transformer upgrade costs, parking space rental fees, and operation and maintenance costs. Since charging piles are divided into AC charging piles (or fast charging piles) and DC charging piles (or slow charging piles), this embodiment sets a threshold based on the proportion of AC charging piles selected by the user. When the proportion is greater than Select AC charging pile in case of emergency, otherwise select DC charging pile. Assume... and These represent the number of DC and AC charging stations built, respectively. and These represent the construction cost of a single pile, respectively. The estimated investment cost for line reconstruction, This refers to the line modification coefficient; This represents the total number of common transformer types. and The first Price and quantity of this type of transformer; Rental fee per parking space; and These are the operating costs for DC and AC charging piles, respectively. For years of service, This is the annual average correlation coefficient. Then, the charging pile... In the Accumulated costs over the years The calculation formula is as follows: , S303, based on the aforementioned operating revenue calculated based on charging load demand. and cumulative total cost Define the charging pile in the first Cumulative net profit of the year To accumulate income With accumulated costs The difference, that is The investment payback period is the period during which the cumulative net profit of the charging pile throughout its entire life cycle is realized. The optimization objective established in this embodiment is to find the smallest year that converges to zero or a number greater than zero for the first time. At the same time, it meets the utilization rate of charging piles. The constraints include the average hourly charging capacity (AV) at the pre-selected locations and the service capacity of a single charging pile. The specific expression of the optimization model is as follows: , In the formula, and These represent the charging power of AC and DC charging piles, respectively. This is the amount of electricity required under steady-state conditions. Select the ratio for the user. For charging piles Average hourly charging amount For charging piles Rated power supply capacity, The maximum allowable load factor for the road. This is the lower limit of the capacity for transformer selection.

[0053] S304. Based on the operating revenue calculated from the charging load demand and the corresponding cost model, the optimization model is iteratively solved using a bisection method to determine the optimal layout of charging piles. Given the time monotonicity of the investment payback period calculation, the specific iterative solution steps are as follows: The first step is to conduct a feasibility assessment and calculate the service life of the charging pile at the end of its service life (i.e., the first...). Cumulative net profit (year) If the value is less than zero, it means that the investment cost cannot be recovered within the service life, and the plan is deemed unfeasible. The second step, if the plan is feasible, is to set an initial lower bound for the time interval. Initial upper bound ; The third step is to enter the iterative loop and calculate the intermediate year. and the cumulative net profit for that year. ; The fourth step is to narrow down the range based on the sign of the net profit. and ( (The value is the median of the previous time step), indicating that the payback period is in the first half, so the upper bound is updated. ;like and This indicates that the payback period is in the latter half, and the lower bound is updated. ; Fifth, repeat the above iterative process until the year in which the cumulative net profit converges to zero is found. The combination of charging pile locations and types corresponding to this convergence year is the optimal layout scheme under the condition of taking into account the economics of power grid transformation.

[0054] This embodiment internalizes the cost of upgrading existing distribution network lines and transformers into the investment payback period calculation and uses the bisection method for efficient solution. It successfully selects a set of optimal charging pile layout schemes that can meet the charging needs of electric vehicles, maximize the use of the remaining capacity of the power grid, and avoid expensive grid upgrades, thus achieving dual optimization of economic benefits and power grid security.

[0055] S4. Based on the optimal layout, the charging load demand is superimposed on the load data for power flow calculation, and the average maximum operating risk of the distribution network is calculated. The acceptable number of charging piles in the distribution network area is determined according to the risk limit value.

[0056] It should be noted that traditional distribution network capacity assessments typically employ a "deterministic criterion," meaning that if the voltage or current exceeds a limit at any given moment, the network is deemed unqualified, and the number of charging stations at that point represents the limit. This hard-constraint method cannot distinguish the fundamental difference in harm between "minor, short-term overruns" and "severe, sustained overruns," often leading to overly conservative assessment results and wasting the potential capacity of the existing power grid. The exponential function introduced in this embodiment has non-linear amplification characteristics. By adjusting the amplification factor, it can assign extremely high risk values ​​to severe overruns while maintaining a certain tolerance for minor overruns. This shift from "deterministic safety" to "probabilistic risk management" allows for maximizing the potential of existing distribution network areas to accommodate electric vehicles (NACP) while ensuring that no cascading failures occur in the power grid.

[0057] In this embodiment, step S4 specifically includes: S401 performs superposition of charging load and conventional load, and power flow calculation: Based on the optimal layout (location and type) of charging piles obtained in step S3, combined with the existing distribution network conventional load data acquired in step S1 and the electric vehicle charging load demand predicted in step S2, the two are superimposed in both time and space dimensions to form the total load of the distribution network. Continuous power flow calculations are then performed on the superimposed distribution network model using the Newton-Raphson method or the forward-backward substitution method to obtain the actual voltage of each node and the actual current data of each branch at different times. Figure 5 The diagram illustrates the voltage distribution at nodes after the addition of charging loads. It shows that after connecting a large number of charging piles, the voltage at some end nodes fluctuates and even approaches the lower limit (0.956 per-unit value). To scientifically quantify the safety hazards caused by this fluctuation, this embodiment no longer simply counts the number of times the limit is exceeded, but instead introduces a risk quantification model.

[0058] S402, Construct a distribution network operation risk assessment model based on an exponential severity function: The operational risk assessment model aims to quantify the severity of voltage overruns, current overruns, and transformer overloads. This embodiment uses a nonlinear severity function. Using exponential form to measure the normalized loss The formula for scaling up is as follows: , In the formula, Severity of loss; This is the amplification factor used to adjust the sensitivity to the severity of the loss. The larger the value, the more sensitive the model is to out-of-limit behavior. Based on this function, the distribution network under [specific conditions] is calculated. Average voltage exceeding limit risk at any given time Average current exceeding the limit risk and the risk of overload of distribution transformers .

[0059] Specifically, regarding the risk of overload in distribution transformers, the amount of loss... The definition logic is: when the transformer load factor Less than or equal to the overload limit value At that time, it was considered risk-free, and the amount of loss was... =0; when Greater than At that time, the amount of loss Substituting this into the aforementioned exponential function will result in a sharply increased risk value, as shown in the following formula: , In the formula, Severity of overload loss of distribution transformer.

[0060] Furthermore, the power distribution network in Average voltage exceeding limit risk at any given time The calculation formula is as follows: , The distribution network is Risk of average current exceeding limit at any time The calculation formula is as follows: , The distribution network is Risk of overload of distribution transformer at all times The calculation formula is as follows: , in, yes The severity of voltage drop at time node i. yes The severity of current loss in branch i at time i yes The severity of overload losses in time-distribution transformers. Furthermore, and These represent the number of nodes and branches in the distribution network, respectively.

[0061] Considering the above three types of risks, a weighted summation is used to obtain the distribution network's... Comprehensive operational risks at all times Its expression is as follows: , In the formula, The weighting coefficients for voltage, current, and transformer risks are set according to the actual operation and maintenance priorities of the power grid. Based on these coefficients, the maximum daily operational risk of the distribution network is determined. .

[0062] S403, Determining the Acceptable Number of Charging Stations (NACP) in a Distribution Network Area Based on Monte Carlo Simulation: Given the highly random nature of user travel behavior, calculations based on a single scenario are insufficient to reflect real-world risks. Therefore, this embodiment employs the Monte Carlo method to analyze the travel behavior of electric vehicle users. Subsampling (e.g.) (times). For each sampling Regenerate the charging load and calculate the corresponding maximum daily operating risk of the distribution network. Then, calculations were performed. Average maximum operational risk per sampling The calculation formula is as follows: , Based on this, assess the acceptance capacity of the distribution network: set the permissible risk limits for the distribution network. (This value is set by the power grid company according to safety standards.) The number of charging piles connected to the distribution network can be adjusted. ,observe The changing trend. When the calculated average maximum operating risk... Approaching or just reaching the aforementioned risk limit value At this time, the corresponding number of charging piles This refers to the number of acceptable charging piles (NACP) identified in the existing distribution network area.

[0063] This embodiment overcomes the problem of biased evaluation results caused by parameter determinism in traditional assessment methods by introducing an exponential severity function and Monte Carlo simulation. This method not only considers the multidimensional risks of voltage, current, and transformer overload, but also accurately characterizes the physical fact that "the greater the limit, the more severe the risk and the greater the cost" through nonlinear functions. This helps power grid planners accurately calculate the maximum acceptance potential of existing distribution networks for electric vehicle charging stations within a controllable risk range.

[0064] Example 2, Figure 6 A device for assessing the configuration scale of charging piles in existing distribution network areas is provided, including: The data acquisition module is used to acquire load data of existing distribution network areas and resident travel data of the target area; The load forecasting module is used to establish a mapping relationship between travel purpose, travel time and driving characteristics based on residents' travel data, and to predict the charging load demand of electric vehicles. The site selection optimization module is used to establish a charging pile site selection optimization model with the objective function of minimizing the investment payback period, combined with the renovation and construction costs of distribution network lines and transformers, and to calculate the operating revenue of the charging piles based on the charging load demand, and to solve the optimization model to obtain the optimal layout of the charging piles. The risk assessment and capacity determination module is used to perform power flow calculation by superimposing the charging load demand onto the load data based on the optimal layout, calculate the average maximum operating risk of the distribution network, and determine the acceptable number of charging piles in the distribution network area according to the risk limit value. The average maximum operating risk is nonlinearly amplified using an exponential severity function to assess the risks of voltage over-limit, current over-limit, and transformer overload. The sensitivity to the severity of over-limit is controlled by adjusting the amplification factor.

[0065] Example 3, A device for assessing the configuration scale of charging piles in existing power distribution network areas, such as Figure 7 As shown, it includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement any of the embodiments in Example 1.

[0066] Since the device for assessing the configuration scale of charging piles in existing power distribution network areas described in this embodiment is the same device used to implement the method in Embodiment 1 of this invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the method described in Embodiment 1 of this application. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any device used by those skilled in the art to implement the method in this application embodiment falls within the scope of protection of this application.

[0067] Example 4, A readable storage medium having a computer program stored thereon, such as Figure 8 As shown, when the computer program is executed by the processor, it implements any of the embodiments in Example 1.

[0068] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0069] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0070] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0073] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the configuration scale of charging piles in existing power distribution network areas, characterized in that, Includes the following steps: Obtain resident travel data for the target area of ​​the existing power distribution network; Predict the charging load demand for electric vehicles based on residents' travel data; With the goal of minimizing the investment payback period, an optimization model for charging pile site selection is established in conjunction with the construction cost of power distribution network renovation. The operating revenue of the charging piles is calculated based on the charging load demand, and the optimization model is solved accordingly to obtain the optimal layout of the charging piles. The average maximum operating risk of the distribution network is calculated under the optimal layout, and the acceptable number of charging piles in the distribution network area is determined according to the risk limit value; wherein, the average maximum operating risk is nonlinearly quantified by an exponential severity function for voltage, current over-limit and transformer overload risks.

2. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 1, characterized in that, The method of predicting the charging load demand of electric vehicles based on residents' travel data includes establishing a mapping relationship between travel purpose, travel time, and driving characteristics, specifically: A three-peaked mixed Gaussian distribution was used to fit the time of the first trip of the day for electric vehicles; The driving time and driving distance of electric vehicles were fitted using a log-normal distribution; Based on the fitted time and distance distributions, and combined with the parking duration for different travel purposes, the charging load demand of electric vehicles is predicted.

3. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 1, characterized in that, The investment payback period is the year in which the cumulative net profit of the charging pile over its entire life cycle is zero; In the calculation of the cumulative net profit, the cost items specifically include the construction cost of charging piles, the estimated investment cost of line reconstruction, the cost of public transformer renovation, parking space rental and operation and maintenance costs.

4. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 3, characterized in that, The charging pile site selection optimization model is solved iteratively using the bisection method, including: Calculate the cumulative net profit at the end of the charging pile's service life; if it is less than zero, it is determined that the cost cannot be recovered. Set initial upper and lower bounds for the time interval, iteratively calculate the cumulative net profit of the intermediate years, and continuously narrow the time interval according to the positive or negative sign of the net profit until the year in which the cumulative net profit converges to zero is found. The combination of charging pile locations and types corresponding to that year is the optimal solution.

5. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 1, characterized in that, The average maximum operating risk is composed of a weighted average of voltage over-limit risk, branch current over-limit risk, and distribution transformer overload risk.

6. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 5, characterized in that, The expression for the exponential severity function is as follows: In the formula, Severity of loss; This is the amplification factor used to adjust the sensitivity to the severity of the loss; This is the normalized loss value; Regarding the overload risk of distribution transformers, the amount of loss Defined as: when the transformer load factor Less than or equal to the overload limit value hour, =0; when Greater than hour, .

7. The method for evaluating the configuration scale of charging piles in existing distribution network areas according to claim 6, characterized in that, Determining the acceptable number of charging piles in the distribution network area includes: Several Monte Carlo sampling operations were conducted on the travel behavior of electric vehicle users. For each sampling, calculate the maximum daily operational risk of the distribution network; Calculate the average maximum operating risk; Adjust the number of charging piles connected to the distribution network. When the average maximum operating risk reaches the preset risk limit value of the distribution network, the corresponding number of charging piles is the acceptable number of charging piles for the existing distribution network area.

8. A device for evaluating the configuration scale of charging piles in existing power distribution network areas, characterized in that, include: The data acquisition module is used to acquire residents' travel data in the target area of ​​the existing power distribution network; The load forecasting module is used to predict the charging load demand of electric vehicles based on residents' travel data. The site selection optimization module is used to establish a charging pile site selection optimization model with the goal of minimizing the investment payback period and in combination with the construction cost of power distribution network renovation; it calculates the operating revenue of charging piles based on the charging load demand and solves the optimization model accordingly to obtain the optimal layout of charging piles; The risk assessment and capacity determination module is used to calculate the average maximum operating risk of the distribution network under the optimal layout, and determine the acceptable number of charging piles in the distribution network area based on the risk limit value; wherein, the average maximum operating risk adopts an exponential severity function to nonlinearly quantify the risks of voltage and current over-limit and transformer overload.

9. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes, according to the instructions of the computer program, a method for evaluating the configuration scale of charging piles in existing distribution network areas as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for evaluating the configuration scale of charging piles in existing distribution network areas as described in any one of claims 1-7.

Images