Adjustable capability polymerization method of electric vehicle group, medium and electronic equipment

Abstract

The invention provides an adjustable capability aggregation method of an electric vehicle group, a medium and electronic equipment. The adjustable capability aggregation method comprises the following steps: acquiring a demand mode of an electric vehicle user, and constructing a first data calculation mode and a second data calculation mode according to the demand mode; determining a power adjustment feasible region of each electric vehicle load monomer in the to-be-aggregated electric vehicle group; clustering the to-be-aggregated electric vehicle group according to the model parameter of each electric vehicle in the to-be-aggregated electric vehicle group, and determining a clustering result of the to-be-aggregated electric vehicles; performing internal approximation on the power regulation feasible region of each single electric vehicle in each type of electric vehicles to obtain an internal approximation result; and according to the internal approximation result, summing the power regulation feasible regions of all the single electric vehicles in each type of electric vehicle cluster, and determining an aggregation feasible region of each clustering result of the to-be-aggregated electric vehicle group. According to the embodiment of the invention, the aggregation of the adjustable capability of the flexible resource is more convenient and quicker.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a method, a medium and an electronic device for aggregating adjustable capabilities of an electric vehicle group. Background Art

[0002] With the advancement of the electric energy substitution strategy, the power load has been rising year by year, bringing major challenges to the safe and economical operation of the power system. Exploiting the flexible load adjustable resources of electric vehicles to participate in power peak regulation can delay power investment, and is an effective way to reduce the peak-to-valley difference of the power system and improve the safe and economic operation of the power system. The flexible characteristic curve of the load of a single device has the characteristics of periodic jump and small adjustable power. In contrast, the flexible load group has the characteristics of considerable number, flexible scheduling mode, and huge potential. Therefore, how to study the aggregation method of the adjustable capacity of flexible resources has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] Therefore, the embodiments of the present invention provide a method, a medium and an electronic device for aggregating the adjustable capabilities of an electric vehicle group, so that the aggregation of the adjustable capabilities of flexible resources is more convenient and quick.

[0004] In order to solve the above problems, the present invention provides an adjustable capacity aggregation method for an electric vehicle group, comprising: a construction module, the construction module is used to obtain the demand mode of electric vehicle users and construct a first data calculation mode and a second data calculation mode according to the demand mode; a determination module, the determination module is used to determine the power regulation feasible domain of each electric vehicle load unit in the electric vehicle group to be aggregated according to the first data calculation mode and the second data calculation mode; a clustering module, the clustering module is used to cluster the electric vehicle group to be aggregated according to the model parameters of each electric vehicle in the electric vehicle group to be aggregated, and determine the clustering result of the electric vehicles to be aggregated; an approximation module, the approximation module is used to perform inner approximation on the power regulation feasible domain of each single electric vehicle in each type of electric vehicle according to the clustering result, and obtain the inner approximation result; a summation module, the summation module is used to sum the power regulation feasible domains of all single electric vehicles in each type of electric vehicle cluster according to the inner approximation result, and determine the aggregation feasible domain of each clustering result of the electric vehicle group to be aggregated.

[0005] In an example of the present invention, obtaining the demand pattern of electric vehicle users and constructing the first data calculation pattern and the second data calculation pattern according to the demand pattern also includes: constructing single-unit operation models and time-series energy boundaries of multiple electric vehicles according to the demand pattern.

[0006] In one example of the present invention, constructing a single-unit operation model of multiple electric vehicles according to demand mode includes: constructing a single-unit operation model according to a rated power charging mode, an adjustable charging mode, a flexible charging and discharging mode, and a battery replacement mode.

[0007] In an example of the present invention, constructing the single-unit operation models and sequential energy boundaries of multiple electric vehicles according to demand also includes: obtaining battery parameters of a single electric vehicle, and constructing the sequential energy boundary according to the battery parameters.

[0008] In one embodiment of the present invention, the battery parameters include: upper and lower limits of battery power, initial grid-connected power, expected off-grid power, and the latest charging start time.

[0009] In one example of the present invention, determining the feasible domain of power regulation of each electric vehicle load cell in the electric vehicle group to be aggregated according to the first data calculation method and the second data calculation method also includes: characterizing the operating model and timing energy boundary of a single electric vehicle resource with Polytopes to determine the feasible domain of power regulation of each single electric vehicle.

[0010] In one embodiment of the present invention, the electric vehicle group to be aggregated is clustered according to the model parameters of each electric vehicle in the electric vehicle group to be aggregated, and determining the clustering result of the electric vehicles to be aggregated also includes: performing DBSCAN clustering on the electric vehicle group to be aggregated according to the model parameters of each electric vehicle in the electric vehicle group to be aggregated, specifically including: step 1: constructing a data sample set according to the model parameters of each single electric vehicle in the electric vehicle cluster to be aggregated, and normalizing the initial data; step 2: setting the neighborhood radius and minimum density ; Step 3: Randomly select a non-clustered point according to Computation and Euclidean distance is less than The number of points , and these points and Grouped into one category, among which: is a vector and The Euclidean distance of and is a vector and No. Dimensions; is the total dimension. Step 4: If Go to step 5, otherwise go to step 7; Step 5: Traverse other points in the current class and calculate the neighborhood of each point separately The number of unclustered points in the cluster is included in the current class; Step 6: If the number of unclustered points in the neighborhood of all points in the current class is less than , go to step 7, otherwise return to step 5; Step 7: If there are still unclustered points, return to step 3, otherwise clustering is completed.

[0011] In one example of the present invention, based on the inner approximation result, the power regulation feasible domains of all individual electric vehicles in each type of electric vehicle cluster are summed to determine the aggregated feasible domain of each clustering result of the electric vehicle group to be aggregated, which also includes: performing Minkowski sum on the power regulation feasible domains of all individual electric vehicles in each type of electric vehicle cluster to determine the aggregated feasible domain of each clustering result of the electric vehicle group to be aggregated.

[0012] After adopting the technical solution of the present invention, the following technical effects can be achieved: (1) Based on the established feasible domain of electric vehicles, Minkowski adjustable capacity aggregation is performed to determine the aggregation feasible domain of electric vehicle clusters with the same clustering results, and the adjustable capacity evaluation of the flexible electric vehicle load group is realized. Then, according to the aggregation feasible domain of each clustering result of the determined electric vehicle cluster to be aggregated, the adjustable capacity of the electric vehicle cluster to be aggregated is evaluated. In addition, the aggregation feasible domain of the electric vehicle cluster to be aggregated can be submitted to the power grid dispatching center for dispatching strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 A schematic diagram of a flow chart of a method for aggregating adjustable capabilities of an electric vehicle group provided in an embodiment of the present invention; Figure 2 A schematic diagram of electric vehicle clustering results provided by an embodiment of the present invention; Figure 3 A schematic diagram of adjustable capacity of an electric vehicle cluster 1 provided in an embodiment of the present invention; Figure 4 A schematic diagram of adjustable capacity of an electric vehicle cluster 2 provided in an embodiment of the present invention; Figure 5 A schematic diagram of adjustable capacity of an electric vehicle cluster 3 provided in an embodiment of the present invention; Figure 6 A schematic diagram of adjustable capacity of an electric vehicle cluster 4 provided in an embodiment of the present invention; Figure 7 A schematic diagram of a module of a readable storage medium; Figure 8 The present invention provides a structure of an electronic device.

[0014] Description of reference numerals: 80. Electronic device; 81. Processor; 82. Memory; 83. Input device; 84. Output device; 200. Readable storage medium; 210. Computer executable instructions. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] [First embodiment] See also Figure 1-Figure 6 , a method for aggregating adjustable capabilities of an electric vehicle group, comprising: Step S100: obtaining the demand mode of the electric vehicle user and constructing a first data calculation mode and a second data calculation mode according to the demand mode; Step S200: determining a power regulation feasible region for each electric vehicle load cell in the electric vehicle group to be aggregated according to the first data calculation method and the second data calculation method; Step S300: clustering the electric vehicles to be aggregated according to the model parameters of each electric vehicle in the electric vehicles to be aggregated, and determining the clustering result of the electric vehicles to be aggregated; Step S400: performing inner approximation on the power regulation feasible region of each individual electric vehicle in each type of electric vehicles according to the clustering result to obtain an inner approximation result; Step S500: According to the inner approximation result, the power regulation feasible regions of all individual electric vehicles in each type of electric vehicle cluster are summed to determine the aggregation feasible region of each clustering result of the electric vehicle group to be aggregated.

[0017] Specifically, in one embodiment, step 101 includes: Considering user needs including rated power charging mode, adjustable charging mode, flexible charging and discharging mode and battery replacement mode, the single-operating body model of electric vehicles in various operating modes is: (1) Rated power charging method (1) (2) In the formula, , , , , , , Electric vehicles The grid-connected period, off-grid period, grid-connected initial SOC, off-grid expected SOC, rated charging power, charging efficiency and battery capacity; , They are exist The actual charging power during the period, SOC at the end of the period; The unit duration.

[0018] (2) Adjustable charging mode (3) (4) (3) Flexible charging and discharging methods (5) (6) In the formula, , They are Rated discharge power and discharge efficiency; , is a Boolean variable, and ;when hour, ; hour, and ; for The discharge threshold.

[0019] (4) Battery replacement method (7) (8) (9) (10) In the formula, The total scheduling period.

[0020] The temporal energy boundary of a single electric vehicle is: (11) In the formula, , , , , They are the upper and lower limits of the battery power of the electric vehicle, the initial power connected to the grid, the expected power off the grid, and the latest time to start charging.

[0021] In one embodiment, step 102 includes: The single electric vehicle operation model and time-series energy boundary constructed according to user needs are represented by Polytopes to achieve the mapping from resource adjustable capacity to feasible domain. Polytopes is a bounded and closed convex polytope, and its boundary is composed of multiple hyperplanes. This paper uses the form of half-plane to represent polytopes, and its compact form is: (12) (13) (14) (15) In the formula, is the feasible region for power regulation of a single electric vehicle load. is the power of a single electric vehicle during the dispatch cycle; and The inequality coefficient matrix and column vector that characterize the feasible region of a single electric vehicle model; represents the power constraint of the electric vehicle model, Represents the power constraint of the electric vehicle model; is the identity matrix, The charging and discharging efficiency coefficient of the electric vehicle model decision; T is the scheduling period; is the identity matrix; and are the lth electric car at time The upper and lower limits of energy.

[0022] In one embodiment, step 103 includes: From the above compact form, it can be seen that if the charging and discharging efficiency of two electric vehicles is the same, their Minkowski sum can be directly summed at the boundary, which will greatly reduce the computational complexity. According to the model parameters of each electric vehicle in the electric vehicle group to be aggregated, DBSCAN clustering is performed on the electric vehicle group to be aggregated to determine the clustering results of the electric vehicles to be aggregated, including: Step 1: According to the model parameters of each electric vehicle in the electric vehicle cluster to be aggregated, a data sample set is constructed and the initial data is normalized; Step 2: Set the neighborhood radius and minimum density ; Step 3: Randomly select a non-clustered point according to Computation and Euclidean distance is less than The number of points , and these points and Grouped into one category, among which: is a vector and The Euclidean distance of and is a vector and No. Dimensions; is the total dimension.

[0023] Step 4: If Go to step 5, otherwise go to step 7; Step 5: Traverse other points in the current class and calculate the neighborhood of each point separately The number of unclustered points in , and include these unclustered points into the current class; Step 6: If the number of unclustered points in the neighborhood of all points in the current class is less than , go to step 7 otherwise return to step 5; Step 7: If there are still unclustered points, return to step 3, otherwise clustering is completed.

[0024] In one embodiment, step 104 includes: The power regulation feasible region of all individual electric vehicles in each type of electric vehicle cluster is approximated internally: (16) In the formula, is the approximate constraint space of individual l; is the scaling factor of individual l; is the translation factor of individual l, and its specific value can be obtained by solving the following LP problem: (17) (18) (19) In the formula, ; ; ; ; N is the number of electric vehicles in this type of cluster.

[0025] Perform the Minkowski sum on the power regulation feasible domains of all individual electric vehicles in each type of electric vehicle cluster to determine the aggregated feasible domain of each clustering result of the electric vehicle cluster to be aggregated: (20) In the formula, The feasible domain of power aggregation for electric vehicle clusters.

[0026] So far, the adjustable capacity aggregation model of electric vehicle clusters based on user demand has been established and can be applied to actual examples.

[0027] Therefore, according to the aggregation feasible domain of each clustering result of the determined electric vehicle cluster to be aggregated, the adjustable capacity of the electric vehicle cluster to be aggregated can be evaluated. In addition, the aggregation feasible domain of the electric vehicle cluster to be aggregated can be submitted to the power grid dispatching center for dispatching strategy.

[0028] In order to verify the effectiveness of the proposed method, the following example is set: Example: The distribution range of electric vehicle load parameters is shown in Table 1. It is assumed that the load parameters of 7500 electric vehicles obey random distribution within the range shown; the electric vehicles charged after work during the daytime access the grid at around 9:00 and leave the grid at around 18:00; the electric vehicles charged after get off work at night access the grid at around 22:00 and leave the grid at around 8:00; the normal distribution is used to generate the access and off-grid times of 7500 electric vehicles; the battery capacity parameters are sampled from the common electric vehicle capacity data on the market to obtain 7500 samples.

[0029] Figure 2 The clustering results of electric vehicles: the number of cluster 1 is 1871; the number of cluster 2 is 1843; the number of cluster 3 is 1941; and the number of cluster 4 is 1844.

[0030] Figure 3 Electric vehicle cluster 1 has adjustable capacity. Electric vehicle cluster 1 mainly uses battery replacement. For users, the battery replacement time is mainly concentrated around 8:00 in the morning and around 20:00 in the evening. Because the adjustable capacity cannot be provided during the battery replacement, it can be seen that the upper and lower limits of the adjustable power are significantly reduced during these two time periods; and around 8:00 in the morning and around 20:00 in the evening, it is necessary to ensure that the new battery has sufficient power. In these two time periods, two peaks greater than 0 can be seen in the lower limit of the adjustable power.

[0031] Figure 4 Electric vehicle cluster 2 has adjustable capacity. Electric vehicle cluster 2 mainly adopts rated power charging mode. Electric vehicles are generally charged during the daytime between 9:00 and 18:00 after work, and at night between 20:00 and 8:00 after returning home. However, for electric vehicles using rated power charging mode, they are charged to the expected power at rated power after joining the network. Therefore, they have no adjustable capacity, and the upper and lower limits of the adjustable power are the same curve.

[0032] Figure 5Electric vehicle cluster 3 has adjustable capacity. Electric vehicle cluster 3 mainly adopts flexible charging and discharging mode. This type of electric vehicle can not only adjust the charging power, but also discharge to the power grid. During the off-grid time periods of 8:00-9:00 and 18:00-19:00, the expected capacity of the electric vehicle needs to be achieved. Therefore, the lower limit of the adjustable power increases during this time period, and the adjustable power decreases during this period.

[0033] Figure 6 Electric vehicle cluster 4 has adjustable capacity. Electric vehicle cluster 4 mainly adopts adjustable charging mode. The charging power of this type of electric vehicle can be adjusted, but it cannot be discharged. The lower limit of the adjustable power is 0. The changing trend of the adjustable power and the upper limit of the adjustable power is similar to that of electric vehicle cluster 3.

[0034] Therefore, the present application performs Minkowski electric vehicle adjustable capacity aggregation based on the operation model and time-series energy boundary of electric vehicles established in a user demand manner, thereby determining the aggregation feasible domain of electric vehicle clusters with the same clustering results, and realizing the adjustable capacity evaluation of electric vehicle load groups. Then, based on the determined aggregation feasible domain of each clustering result of the electric vehicle cluster to be aggregated, the adjustable capacity of the electric vehicle cluster to be aggregated is evaluated. In addition, the aggregation feasible domain of the electric vehicle cluster to be aggregated can be submitted to the power grid dispatching center for dispatching strategy.

[0035] [Second embodiment] See also Figure 7 The present invention also provides a computer-readable storage medium 200, which stores computer-executable instructions 210, and the computer-executable instructions 210 are used to execute any of the above methods.

[0036] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are only illustrative. For example, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0037] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned readable storage medium 200 includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0038] [Third embodiment] See also Figure 8 The present invention also provides an electronic device 80, which includes: a processor 81; a memory 82 for storing executable instructions of the processor 81; the processor 81 is used to read executable instructions from the memory and execute the instructions to implement any of the above methods.

[0039] The electronic device 80 includes one or more processors 81 and a memory 82 .

[0040] The processor 81 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0041] The memory 82 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 83 and an output device 84, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0042] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0043] The output device 84 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0044] Of course, to simplify, Figure 8 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for aggregating adjustable capabilities of an electric vehicle group, characterized in that: include: A construction module, the construction module is used to obtain a demand mode of an electric vehicle user and construct a first data calculation mode and a second data calculation mode according to the demand mode; A determination module, the determination module is used to determine a feasible region for power regulation of each electric vehicle load cell in the electric vehicle group to be aggregated according to the first data calculation method and the second data calculation method; A clustering module, the clustering module is used to cluster the electric vehicles to be aggregated according to the model parameters of each electric vehicle in the electric vehicles to be aggregated, and determine the clustering results of the electric vehicles to be aggregated; An approximation module, wherein the approximation module is used to perform an inner approximation on the power regulation feasible domain of each single electric vehicle in each type of electric vehicles according to the clustering result to obtain an inner approximation result; A summing module is used to sum the power regulation feasible domains of all individual electric vehicles in each type of electric vehicle cluster according to the inner approximation result, and determine the aggregation feasible domain of each clustering result of the electric vehicle group to be aggregated.

2. The adjustable capacity aggregation method of electric vehicle groups according to claim 1, characterized in that: The method of obtaining the demand mode of the electric vehicle user and constructing the first data calculation mode and the second data calculation mode according to the demand mode also includes: A plurality of single-unit operation models and time-series energy boundaries of the electric vehicle are constructed according to the demand mode.

3. The adjustable capacity aggregation method of electric vehicle groups according to claim 2, characterized in that: The step of constructing a plurality of single-unit operation models of the electric vehicle according to the demand mode includes: The single-cell operation model is constructed according to a rated power charging mode, an adjustable charging mode, a flexible charging and discharging mode, and a battery replacement mode.

4. The adjustable capacity aggregation method of electric vehicle groups according to claim 2, characterized in that: The step of constructing a plurality of single-unit operation models and sequential energy boundaries of the electric vehicles according to the demand mode also includes: The battery parameters of a single electric vehicle are obtained, and the time series energy boundary is constructed according to the battery parameters.

5. The adjustable capacity aggregation method of electric vehicle groups according to claim 4, characterized in that: The battery parameters include: upper and lower limits of battery power, initial grid-connected power, expected off-grid power, and the latest charging start time.

6. The adjustable capacity aggregation method of electric vehicle groups according to claim 2, characterized in that: The method of determining the power regulation feasible domain of each electric vehicle load unit in the electric vehicle group to be aggregated according to the first data calculation method and the second data calculation method also includes: The operation model and time-series energy boundary of each electric vehicle resource are represented by Polytopes to determine the power regulation feasible domain of each single electric vehicle.

7. The adjustable capacity aggregation method of electric vehicle groups according to claim 1, characterized in that: The step of clustering the electric vehicles to be aggregated according to the model parameters of each electric vehicle in the electric vehicles to be aggregated to determine the clustering result of the electric vehicles to be aggregated further comprises: Performing DBSCAN clustering on the electric vehicle group to be aggregated according to the model parameters of each electric vehicle in the electric vehicle group to be aggregated, specifically comprising: Step 1: constructing a data sample set according to the model parameters of each individual electric vehicle in the electric vehicle cluster to be aggregated, and normalizing the initial data; Step 2: Set the neighborhood radius and minimum density ; Step 3: Randomly select a non-clustered point according to Computation and Euclidean distance is less than The number of points , and these points and Grouped into one category, among which: is a vector and The Euclidean distance of and is a vector and No. Dimensions; is the total dimension; Step 4: If Go to step 5, otherwise go to step 7; Step 5: Traverse other points in the current class and calculate the neighborhood of each point separately The number of unclustered points in , and include these unclustered points into the current class; Step 6: If the number of unclustered points in the neighborhood of all points in the current class is less than , go to step 7 otherwise return to step 5; Step 7: If there are still unclustered points, return to step 3, otherwise clustering is completed.

8. The adjustable capacity aggregation method of electric vehicle groups according to claim 1, characterized in that: The method of summing the power regulation feasible regions of all individual electric vehicles in each type of electric vehicle cluster according to the inner approximation result to determine the aggregation feasible region of each clustering result of the electric vehicle group to be aggregated also includes: The Minkowski sum is performed on the power regulation feasible regions of all individual electric vehicles in each type of electric vehicle cluster to determine the aggregation feasible region of each clustering result of the electric vehicle group to be aggregated.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-8.

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