An electric power distribution network carrying capacity evaluation method based on super-charging station order management strategy

Abstract

The present application relates to the technical field of power system planning and operation, in particular to a distribution network carrying capacity evaluation method based on orderly management strategy of super charging station, the method steps are as follows: determining the remaining open capacity constraint of each substation and feeder under the distribution network; using historical charging load data to calculate the maximum load curve of super charging station, and then obtaining the maximum capacity demand under each installed capacity; constructing an integer linear programming model with the maximum total capacity of super charging station as the target, and considering the remaining open capacity constraint; solving the model of each substation respectively to obtain the optimal access scheme, and accumulating all the solving results to obtain the distribution network carrying capacity evaluation result. The present application solves the problem that the existing method cannot match the periodical fluctuation of super charging load and has low precision by calculating the remaining open capacity in detail and constructing the integer linear programming model, effectively improving the accuracy and engineering applicability of distribution network carrying capacity evaluation.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation technology, and more specifically, to a method for assessing the carrying capacity of distribution networks based on an orderly management strategy for supercharging stations. Background Technology

[0002] With the rapid development of the electric vehicle industry, supercharging technology, due to its ability to quickly replenish energy and provide users with a convenience similar to refueling a gasoline vehicle, has become the preferred choice for users in fast-charging scenarios such as "charge and go" and is thus becoming the core direction for the future construction and development of fast-charging stations. Supercharging stations are characterized by high single-pile power and large single-station capacity, and their load is significantly cyclical. After cluster access, it is easy to cause local overload of distribution network feeders and substations, which has become a major problem restricting the large-scale deployment of supercharging stations and the safe and stable operation of the distribution network.

[0003] Assessing the carrying capacity of supercharging stations is one of the main means of balancing the development of supercharging and the safety of the power grid. However, the existing carrying capacity assessment methods still have the following defects: (1) When calculating the remaining capacity of the distribution network, the existing assessment methods use the static calculation method of rated capacity and historical maximum load, without considering the time-varying characteristics of supercharging load, resulting in a large deviation in the remaining capacity assessment results; (2) Supercharging vehicles and fast-charging vehicles have different load characteristics. When the current assessment methods statistically analyze the load of supercharging stations, they do not effectively distinguish between supercharging vehicles and fast-charging vehicles, so they cannot accurately reflect the actual capacity demand of supercharging stations; (3) The existing assessment methods mostly use simple enumeration or empirical values ​​to estimate the number of stations that can be connected, which cannot be accurate to the carrying capacity of each level, resulting in poor engineering applicability.

[0004] Therefore, how to further improve the accuracy and engineering applicability of power distribution network carrying capacity assessment is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a distribution network carrying capacity assessment method based on an orderly management strategy for supercharging stations, in order to solve the technical problem of how to further improve the accuracy and engineering applicability of distribution network carrying capacity assessment.

[0006] This invention is achieved through the following technical solution: a method for assessing the carrying capacity of a distribution network based on an orderly management strategy for supercharging stations, comprising the following steps:

[0007] Obtain the distribution network facility parameters within the area to be evaluated, and determine the remaining open capacity constraints of each substation and the remaining open capacity constraints of each feeder under each substation based on the distribution network facility parameters. Obtain historical charging load data of the supercharging stations connected in the area to be evaluated. The historical charging load data includes charging power data of supercharging vehicles and fast charging vehicles. Calculate the maximum load curve of the supercharging station based on the historical charging load data. The maximum capacity requirement of the supercharging station under each applied capacity is calculated based on the maximum load curve. An integer linear programming model is constructed with the goal of maximizing the total capacity of supercharging stations connected to all feeders under the substation, and considering the constraints of the remaining open capacity of feeders and the remaining open capacity of the substation. The integer linear programming model is solved to obtain the optimal supercharging station access scheme for each feeder under each substation. The optimal supercharging station access schemes for each feeder under each substation are summed to obtain the carrying capacity assessment results of the distribution network in the area to be evaluated.

[0008] According to a preferred embodiment, before calculating the maximum load curve based on the historical charging load data, the method further includes data cleaning of the historical charging load data. The data cleaning includes abnormal data removal and data interpolation. The abnormal data includes vehicle data with power higher than a preset power threshold and vehicle data with effective power data coverage lower than a preset coverage threshold within the statistical period.

[0009] According to a preferred embodiment, the calculation of the maximum load curve specifically includes: Determine the average load of similar vehicles in different periods and time periods; The average load maximum value of each vehicle type in each time period within the statistical period is calculated based on the average load. Based on the average maximum load and the proportion of each vehicle type, the maximum load value of the supercharging station in each time period within the statistical period is obtained, and the maximum load curve is formed.

[0010] According to a preferred embodiment, the average load of similar vehicles in each period and time segment is calculated using the following expression:

[0011]

[0012] In the above formula, Indicates date Time period The average load of supercharging vehicles This indicates the number of supercharging vehicles. Indicates date Time period The charging power of supercharging vehicles date Time period The average load of fast-charging vehicles Indicates the number of fast-charging vehicles. Indicates date Time period The charging power of fast-charging vehicles.

[0013] According to a preferred embodiment, the expression for the maximum average load of each vehicle type in each time period within the statistical period based on the average load is as follows:

[0014]

[0015] In the above formula, Indicates time period The maximum load of supercharging vehicles Indicates the total number of days. Indicates time period Maximum load of fast-charging vehicles.

[0016] According to a preferred embodiment, the maximum load value of the supercharging station is calculated as follows:

[0017] In the above formula, Indicates time period The maximum load value of the supercharging station This indicates the number of charging devices in the rated configuration of a supercharging station. Indicates the first The percentage of vehicles using supercharging annually.

[0018] According to a preferred embodiment, the maximum capacity requirement of the supercharging station under each applied capacity is calculated based on the maximum load curve, as shown in the following expression:

[0019] In the above formula, Indicates the first Preset installation capacity Indicates the installed capacity is Supercharging stations during the period Maximum capacity requirement This indicates the maximum allowable load rate of the transformer at the supercharging station. This represents the maximum load value of a single supercharging station throughout the entire time period. This represents the total number of moments.

[0020] According to a preferred embodiment, the objective function expression for maximizing the total capacity of the supercharging station is as follows:

[0021] In the above formula, Indicates substation The total capacity of all supercharging stations that can be connected to by the subordinate feeders. Indicates substation The collection of all subordinate feeders, A natural number greater than or equal to 1, representing a substation. Subordinate feeder The accessed Number of charging stations with pre-set installation capacity exceeding the limit. This indicates the total number of types of preset installation capacity.

[0022] According to a preferred embodiment, the remaining available capacity constraint for the feeder is that the total load of the feeder connected to the supercharging station in any given time period does not exceed its remaining available capacity, as expressed below:

[0023]

[0024]

[0025] In the above formula, Indicates feeder During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates feeder Rated capacity, Indicates feeder The maximum allowable load rate, Indicates feeder During the period The maximum load.

[0026] According to a preferred embodiment, the remaining available capacity constraint of the substation is that the total cumulative load of all feeders connected to the supercharging station under the substation in any given time period does not exceed its remaining available capacity, as expressed in the following expression:

[0027]

[0028]

[0029] In the above formula, Indicates substation During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates substation Rated capacity, Indicates substation The maximum allowable load rate, Indicates substation During the period The maximum load value, Indicates feeder On the date Time period The actual operating load.

[0030] The technical solution of the distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations provided by the present invention has at least the following advantages and beneficial effects: (1) By calculating the remaining open capacity of substations and feeders in a fine-grained manner and introducing time-based constraints, it can more accurately match the significant time-based fluctuation characteristics of supercharging load, effectively solving the problem of large deviation in the assessment results of existing methods; (2) By distinguishing and processing the load data of supercharging vehicles and fast-charging vehicles, and constructing a maximum load curve that fits the actual situation after data cleaning, it can avoid the distortion of capacity demand estimation caused by confusing vehicles with different charging characteristics; (3) The integer linear programming model replaces the traditional simple enumeration or empirical estimation method, and with the goal of maximizing the total access capacity, it solves the optimal access scheme under the premise of strictly meeting the power grid safety operation constraints, providing scientific and quantitative decision support for the large-scale layout of supercharging stations. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall process of the distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the calculation process for the remaining open capacity of the distribution network provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the calculation process for the maximum capacity requirement of a supercharging station provided in Embodiment 1 of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0033] Example 1 This invention provides a method for assessing the carrying capacity of a distribution network based on an orderly management strategy for supercharging stations. Figure 1 This is a schematic diagram of the overall process of the power distribution network carrying capacity assessment method. (See attached diagram) Figure 1 As shown, the method for assessing the carrying capacity of the power distribution network includes the following steps: Step S1: Calculate the remaining open capacity of the distribution network; See Figure 2 As shown, this step specifically includes: Step S101: Obtain the distribution network facility parameters in the area to be evaluated through the distribution network SCADA system and substation monitoring platform; in this embodiment, the distribution network facility parameters include feeder parameters and substation parameters, wherein the feeder parameters include the rated capacity and load data of the feeder, and the substation parameters include the rated capacity and topology affiliation of the substation.

[0034] Step S102: Determine the calculation formula for the remaining open capacity of the distribution network based on the parameters of the distribution network facilities. The remaining open capacity of the distribution network includes the remaining open capacity of each substation under the distribution network and the remaining open capacity of the feeders under each substation. In some implementations, the calculation expression for the remaining open capacity of the feeder is as follows:

[0035]

[0036] In the above formula, Indicates feeder During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates feeder Rated capacity, Indicates feeder The maximum allowable load rate, It is usually taken as 0.85. Indicates feeder During the period The maximum load value, This indicates the total number of days.

[0037] The formula for calculating the remaining available capacity of the substation is as follows:

[0038]

[0039] In the above formula, Indicates substation During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates substation Rated capacity, Indicates substation The maximum allowable load rate, It is usually taken as 0.8. Indicates substation During the period The maximum load value, Indicates feeder On the date Time period The actual operating load.

[0040] Step S103: Set the remaining open capacity constraints for feeders and substations; The remaining available capacity constraint for the feeder is that the total load connected to the supercharging station by the feeder in any given time period does not exceed its remaining available capacity, as expressed below:

[0041] In the above formula, Indicates feeder During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates feeder Rated capacity, Indicates feeder The maximum allowable load rate, Indicates feeder During the period The maximum load.

[0042] The constraint on the remaining available capacity of the substation is that the total cumulative load of all feeders connected to the supercharging station under the substation in any given time period does not exceed its remaining available capacity, as expressed below:

[0043] In the above formula, Indicates substation During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates substation Rated capacity, Indicates substation The maximum allowable load rate, Indicates substation During the period The maximum load value, Indicates feeder On the date Time period The actual operating load.

[0044] Step S2: Calculation of the maximum load curve of the supercharging station; See Figure 3 As shown, this step specifically includes: Step S201: Obtain historical charging load data of supercharging vehicles and fast-charging vehicles connected to supercharging stations in the area to be evaluated. The historical charging load data is consistent with the data of the distribution network in terms of time granularity, which is 15 minutes. The data dimension includes the power of a single vehicle at different times, covering typical scenarios such as weekdays, weekends, and all four seasons.

[0045] Step S202: Perform data cleaning on the historical charging load data. The data cleaning includes removing abnormal data and data interpolation. The abnormal data includes vehicle data with power higher than a preset power threshold and vehicle data with effective power data coverage lower than a preset coverage threshold within the statistical period.

[0046] Regarding the removal of vehicle data with power exceeding a preset power threshold, this embodiment sets a power threshold for supercharging vehicles. Fast charging vehicle power threshold Eliminate those that meet the requirements and The power data, among which, Indicates supercharging vehicles power, Indicates fast charging vehicles The power; in some implementations, the supercharging vehicle power threshold. kW, power threshold for fast charging vehicles kW; Regarding the removal of vehicle data where the effective power data coverage rate is lower than a preset coverage threshold within a statistical period, this embodiment determines the effective power data coverage rate of each vehicle within the statistical period. If the effective power data coverage rate is lower than the preset coverage threshold... If so, then all data for that vehicle will be removed. This is to ensure the integrity of the data for supercharging and fast-charging vehicles included in the statistics.

[0047] Regarding data interpolation, this embodiment uses linear interpolation to fill in missing data, as shown in the following expression:

[0048] In the above formula, This indicates that power data for a certain vehicle during a certain period was missing. and These represent the effective power adjacent to the missed time period, and Both represent the index for the corresponding time period.

[0049] Step S203: Calculate the maximum load curve of the supercharging station based on the historical charging load data after data cleaning; Step S2031: Determine the average load of similar vehicles in each period and time period; The average load calculation for the same type of vehicles in each period and time period is expressed as follows:

[0050]

[0051] In the above formula, Indicates date Time period The average load of supercharging vehicles This indicates the number of supercharging vehicles. Indicates date Time period The charging power of supercharging vehicles date Time period The average load of fast-charging vehicles Indicates the number of fast-charging vehicles. Indicates date Time period Charging power of fast-charging vehicles. (Indicated by date) Time period For example, the average load of supercharging vehicles is kW, average load of fast-charging vehicles is kW.

[0052] Step S2032: Calculate the maximum average load of each vehicle type in each time period within the statistical period based on the average load; The expression for calculating the maximum average load of each vehicle type in each time period within the statistical period based on the average load is as follows:

[0053]

[0054] In the above formula, Indicates time period The maximum load of supercharging vehicles Indicates time period Maximum load of fast-charging vehicles. By time period. For example, take 365 days maximum value 98kW maximum value 15kW.

[0055] Step S2033: Based on the average maximum load and the proportion of each vehicle type, obtain the maximum load value of the supercharging station in each time period within the statistical period, and form the maximum load curve.

[0056] The maximum load value of the supercharging station is calculated using the following expression:

[0057] In the above formula, Indicates time period The maximum load value of the supercharging station This indicates the number of charging devices in the rated configuration of a supercharging station, such as the number of charging spaces / charging piles. Indicates the first The percentage of vehicles using supercharging annually. For example, kW, Step S3: Calculate the maximum capacity requirement of the supercharging station; In this embodiment, the maximum capacity requirement of the supercharging station under each applied capacity is calculated based on the maximum load curve, and the expression is as follows:

[0058] In the above formula, Indicates the first Preset installation capacity Indicates the installed capacity is Supercharging stations during the period Maximum capacity requirement This indicates the maximum allowable load rate of the transformer at the supercharging station. This represents the maximum load value of a single supercharging station throughout the entire time period. This represents the total number of moments.

[0059] It should be noted that the installed capacity can be 600kW, 1.2MW, 1.6MW, 2.0MW, etc. This embodiment calculates the maximum capacity requirement of the supercharging station corresponding to each installed capacity based on the above calculation expression.

[0060] Step S4: Constructing the integer linear programming model; Considering that there is no topological membership between substations in the distribution network, this embodiment uses the method of solving each substation independently and summing the solution results to calculate the overall carrying capacity of the distribution network. In this embodiment, an integer linear programming model is constructed for each substation. The integer linear programming model aims to maximize the total capacity of the supercharging station connected to all feeders under the substation, and considers the constraints of the remaining open capacity of the feeders and the remaining open capacity of the substation.

[0061] The objective function expression for maximizing the total capacity of the supercharging station is as follows:

[0062] In the above formula, Indicates substation The total capacity of all supercharging stations that can be connected to by the subordinate feeders. Indicates substation The collection of all subordinate feeders, A natural number greater than or equal to 1, representing a substation. Subordinate feeder The accessed Number of charging stations with pre-set installation capacity exceeding the limit. This indicates the total number of types of preset installation capacity.

[0063] Step S5: Solve the integer linear programming model and output the bearing capacity assessment results; The integer linear programming model is solved using a solver. Standardized model parameters are input independently for each substation, and the solution process is started until the optimal solution that satisfies all constraints is obtained. That is, the optimal combination of the number of supercharging stations with the largest total capacity connected to each feeder under the substation is obtained.

[0064] Furthermore, the optimal combination of supercharging station numbers for each feeder under each substation is summed to obtain the carrying capacity assessment result of the distribution network in the area to be evaluated, namely the total number of supercharging stations that can be connected to the distribution network, the total access capacity, and the overall distribution of each type of installed capacity.

[0065] In summary, this embodiment, by calculating the remaining available capacity of substations and feeders with fine granularity and introducing time-based constraints, can more accurately match the significant time-based fluctuations in supercharging load, effectively solving the problem of large deviations in the evaluation results of existing methods. By distinguishing and processing the load data of supercharging vehicles and fast-charging vehicles, and constructing a realistic maximum load curve after data cleaning, it can avoid the distortion of capacity demand estimation caused by confusing vehicles with different charging characteristics. An integer linear programming model is used to replace the traditional simple enumeration or empirical estimation method, and the optimal access scheme is solved under the premise of strictly meeting the power grid safety operation constraints, with the goal of maximizing the total access capacity. This provides scientific and quantitative decision support for the large-scale deployment of supercharging stations.

[0066] 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 power distribution network carrying capacity evaluation method based on super-charging station orderly management strategy, characterized in that, Includes the following steps: Obtain the distribution network facility parameters within the area to be evaluated, and determine the remaining open capacity constraints of each substation and the remaining open capacity constraints of each feeder under each substation based on the distribution network facility parameters. Obtain historical charging load data of the supercharging stations connected in the area to be evaluated. The historical charging load data includes charging power data of supercharging vehicles and fast charging vehicles. Calculate the maximum load curve of the supercharging station based on the historical charging load data. The maximum capacity requirement of the supercharging station under each applied capacity is calculated based on the maximum load curve. An integer linear programming model is constructed with the goal of maximizing the total capacity of supercharging stations connected to all feeders under the substation, and considering the constraints of the remaining open capacity of feeders and the remaining open capacity of the substation. The integer linear programming model is solved to obtain the optimal supercharging station access scheme for each feeder under each substation. The optimal supercharging station access schemes for each feeder under each substation are summed to obtain the carrying capacity assessment results of the distribution network in the area to be evaluated.

2. The power distribution network carrying capacity assessment method based on the super-charge field station order management strategy of claim 1, wherein, Before calculating the maximum load curve based on the historical charging load data, the method further includes data cleaning of the historical charging load data. The data cleaning includes abnormal data removal and data interpolation. The abnormal data includes vehicle data with power higher than a preset power threshold and vehicle data with effective power data coverage lower than a preset coverage threshold within the statistical period.

3. The method for evaluating the carrying capacity of power distribution network based on the order management strategy of hyper-overflow station according to any one of claims 1 to 2, characterized in that, The calculation of the maximum load curve specifically includes: Determine the average load of similar vehicles in different periods and time periods; The average load maximum value of each vehicle type in each time period within the statistical period is calculated based on the average load. Based on the average maximum load and the proportion of each vehicle type, the maximum load value of the supercharging station in each time period within the statistical period is obtained, and the maximum load curve is formed.

4. The power distribution network carrying capacity assessment method based on the super-charge field station order management strategy of claim 3, wherein, The average load calculation for the same type of vehicles in each period and time period is expressed as follows: In the above formula, Date Time period Average load of super-charging vehicles, Number of super-charging vehicles, Date Time period Charging power of super-charging vehicles, Date Time period Average load of fast-charging vehicles, Number of fast-charging vehicles, Date Time period Charging power of fast-charging vehicles.

5. The method for power distribution network carrying capacity assessment based on super-charge field station order management strategy according to claim 4, characterized in that, The expression for calculating the maximum average load of each vehicle type in each time period within the statistical period based on the average load is as follows: In the above formula, representing the time period maximum load of the super-charging vehicle, representing the total number of dates, representing the time period maximum load of the fast-charging vehicle.

6. The method for power distribution network carrying capacity assessment based on super-charge field station order management strategy according to claim 5, characterized in that, The maximum load value of the supercharging station is calculated using the following expression: In the above formula, Indicates the time period The maximum load value of the super charging station, Indicates the number of charging devices rated by the super charging station, Indicates the first Year super charging vehicle proportion.

7. The distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations as described in claim 6, characterized in that, Based on the maximum load curve, the maximum capacity requirement of the supercharging station under each applied capacity is calculated as follows: In the above formula, Indicates the first Preset installation capacity Indicates the installed capacity is Supercharging stations during the period Maximum capacity requirement This indicates the maximum allowable load rate of the transformer at the supercharging station. This represents the maximum load value of a single supercharging station throughout the entire time period. This represents the total number of moments.

8. The distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations as described in claim 7, characterized in that, The objective function expression for maximizing the total capacity of the supercharging station is as follows: In the above formula, Indicates substation The total capacity of all supercharging stations that can be connected to by the subordinate feeders. Indicates substation The collection of all subordinate feeders, A natural number greater than or equal to 1, representing a substation. Subordinate feeder The accessed Number of charging stations with pre-set installation capacity exceeding the limit. This indicates the total number of types of preset installation capacity.

9. The distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations as described in claim 8, characterized in that, The remaining available capacity constraint for the feeder is that the total load connected to the supercharging station by the feeder in any given time period does not exceed its remaining available capacity, as expressed below: In the above formula, Indicates feeder During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates feeder Rated capacity, Indicates feeder The maximum allowable load rate, Indicates feeder During the period The maximum load.

10. The distribution network carrying capacity assessment method based on the orderly management strategy of supercharging stations as described in claim 9, characterized in that, The constraint on the remaining available capacity of the substation is that the total cumulative load of all feeders connected to the supercharging station under the substation in any given time period does not exceed its remaining available capacity, as expressed below: In the above formula, Indicates substation During the period The minimum remaining available capacity that can be connected to a supercharging station. Indicates substation Rated capacity, Indicates substation The maximum allowable load rate, Indicates substation During the period The maximum load value, Indicates feeder On the date Time period The actual operating load.

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