Electric vehicle schedulable potential assessment method considering user two-dimensional response will

Abstract

The invention discloses an electric vehicle schedulable potential evaluation method considering a user two-dimensional response will, and the method comprises the steps: building a user participation power grid scheduling uncertainty model under different excitation levels, which comprises a response rate model of electric vehicle user participation scheduling and a charging time margin uncertainty response model; based on the uncertainty model of users participating in power grid dispatching under different excitation levels, calculating the acceptability of the electric vehicle, and quantifying the response intention of the electric vehicle users participating in dispatching; and based on the user acceptability, the schedulable potential of the electric vehicle cluster is evaluated. According to the method, the responsivity and the response deviation of different users under different incentive levels are analyzed aiming at the problem that the acceptance analysis of the electric vehicle users participating in the power grid dispatching is not comprehensive, the responsivity model of the electric vehicle users under different incentive levels is constructed, the dispatching willingness of the users under different incentive levels is reflected, and the user experience is improved. Therefore, the accuracy of electric vehicle dispatchable capacity evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle acceptance capacity assessment, and more specifically, to an electric vehicle dispatchability potential assessment method taking into account a user's two-dimensional response willingness. Background Art

[0002] As a demand-side resource with huge potential, electric vehicles can be effectively used in power system auxiliary services such as peak load regulation, frequency regulation and spinning reserve through reasonable scheduling. However, with the continuous development of the scale of electric vehicles, the charging and discharging of electric vehicles are affected by user behavior, which is random and uncertain, making it difficult to evaluate the dispatchable potential of electric vehicle clusters. Therefore, a reasonable assessment of the dispatchable potential of electric vehicles is of great guiding significance for promoting sustainable development in the energy field and further developing electric vehicle development planning.

[0003] At present, the research methods for evaluating the dispatchable potential of electric vehicles have the following deficiencies: From a technical perspective, electric vehicles are potential, high-quality demand-side dispatchable resources, with the characteristics of adjustable load and energy storage. Their application prospects in peak regulation, frequency regulation, and standby are gradually gaining attention. Electric vehicle clusters are generally located at load centers and can quickly switch between charging and discharging states to adjust the load. However, the dispersion of electric vehicle clusters in time and space, as well as the inconsistency of battery status, have brought difficulties to centralized regulation. From the perspective of user acceptance of dispatch, the vast majority of existing electric vehicles are owned by private users, and power grid companies do not need to share their purchase costs. Therefore, the power grid cannot obtain the dispatching rights of electric vehicles. The distributed characteristics of electric vehicles make it difficult for them to directly access centralized wholesale power supply hours. Therefore, existing research still lacks a comprehensive assessment of the responsiveness of electric vehicle users to participate in dispatch. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for evaluating the dispatchable potential of electric vehicles taking into account the user's two-dimensional response willingness, in order to alleviate the inaccurate dispatchable capacity evaluation caused by insufficient evaluation of the acceptance of electric vehicle users to participate in scheduling, thereby giving full play to the dispatchable potential of electric vehicles and optimizing the time distribution of charging load in the distribution network.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for evaluating the dispatchable potential of electric vehicles considering the user's two-dimensional response willingness comprises the following steps: establishing an uncertainty model of user participation in power grid dispatch under different incentive levels, including a response rate model of electric vehicle users participating in dispatch and an uncertainty response model of charging time margin; calculating the acceptance of electric vehicles and quantifying the response willingness of electric vehicle users to participate in dispatch based on the uncertainty model of user participation in power grid dispatch under different incentive levels; and evaluating the dispatchable potential of electric vehicle clusters based on user acceptance.

[0006] In the above scheme, the response rate model of electric vehicle users participating in the dispatch is specifically:

[0007] Where: The incentive level is Time The response rate of electric vehicles participating in grid dispatch; and Respectively The maximum and minimum values ​​of responsiveness when electric vehicles participate in dispatching.

[0008] In the above scheme, an uncertainty response model of the charging time margin is constructed, and the specific steps are as follows: obtain first data of the electric vehicle, the first data including the charged power of the electric vehicle when it enters and leaves the grid, the rated capacity of the electric vehicle and the charging power of the electric vehicle; calculate the shortest charging time required for the electric vehicle based on the first data; calculate the time margin of the electric vehicle based on the shortest charging time required for the electric vehicle; calculate the time margin response rate of the electric vehicle user based on the time margin of the electric vehicle.

[0009] In the above scheme, the time margin of the electric vehicle is as follows:

[0010] Where: For the The time margin of an electric vehicle, For the The shortest charging time required for an electric car, For the Total time electric vehicles are connected to the grid; The time margin response rate of electric vehicle users is calculated as follows:

[0011] Where: For the The time margin response rate of electric vehicles; For the The expected charging time margin for an electric vehicle.

[0012] In the above scheme, the acceptance of electric vehicles is as follows:

[0013] In the formula, For the The acceptance of electric vehicles participating in dispatch; and are the weights of the stimulus level response rate and the time margin response rate, The incentive level is Time The response rate of electric vehicles participating in grid dispatching.

[0014] In the above scheme, the dispatchable potential of the electric vehicle cluster is evaluated based on user acceptance, and the dispatch capacity of the electric vehicle cluster is evaluated specifically by the following formula;

[0015] Where: upward dispatchable capacity for electric vehicle clusters; downward dispatchable capacity for electric vehicle clusters; is the number of electric vehicles; is the total number of time periods, For the Electric car The upward adjustment capability of the time period, For the Electric car The downward adjustment capability of the time period, For the Electric vehicles in Real-time charging and discharging power during the time period.

[0016] In the above scheme, the upward adjustable capacity of the electric vehicle is calculated using the following formula:

[0017] Where: is the maximum discharge power of the electric vehicle, For the Electric cars in The amount of electricity in each period; For the Time period Whether the electric vehicle is connected to the charging station, =1 and =0 respectively Electric cars in Connect and disconnect the charging pile in different time periods. is the minimum SOC limit of electric vehicles; The downward adjustment capability of electric vehicles is calculated using the following formula:

[0018] Where: For the Electric car Downward adjustment capability for each time period; is the maximum charging power of the electric vehicle, It is the maximum SOC limit of electric vehicles.

[0019] Technical effects and advantages of the electric vehicle dispatchable potential evaluation method considering the user's two-dimensional response willingness of the present invention: 1. Aiming at the problem of incomplete analysis on the acceptance of electric vehicle users to participate in grid dispatch, the present invention analyzes the responsiveness and response deviation of different users under different incentive levels, constructs a response rate model of electric vehicle users under different incentive levels, reflects the dispatch willingness of users under different incentive levels, and thus improves the accuracy of the assessment of the dispatchable capacity of electric vehicles.

[0020] 2. In view of the lack of research on the impact of electric vehicle charging time margin on scheduling willingness in the past, the present invention analyzes the charging time margin of electric vehicles based on the travel time pattern and charge state of electric vehicles, and uses the two-dimensional cloud model improved by the entropy weight method to calculate the acceptance of electric vehicle users' participation in scheduling under the joint effect of the two dimensions of incentive level and charging time margin, which comprehensively reflects the level of users' participation in power grid scheduling, thereby improving the applicability of electric vehicle dispatchable capacity assessment.

[0021] 3. The present invention aims at the charging and discharging load characteristics of electric vehicles and proposes a method for calculating the up and down dispatchable capacity taking into account the charging and discharging power of a single electric vehicle. By fitting the travel time distribution and power consumption of an electric vehicle cluster, a Monte Carlo simulation algorithm is used to evaluate the up and down dispatchable capacity of the electric vehicle cluster. While taking into account user acceptance and charging demand, an accurate evaluation of the dispatchable capacity of the electric vehicle cluster is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the dispatchable potential evaluation of electric vehicles considering the user's two-dimensional response willingness of the present invention.

[0023] Figure 2 This is a feasible domain diagram for charging and discharging a single electric vehicle of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0025] In this embodiment, in order to effectively solve the problem of imperfect analysis of the acceptance of electric vehicle users to participate in scheduling and inaccurate evaluation of the dispatchable capacity of electric vehicle load clusters, considering the user's response to different incentive levels and charging time margins, a method for evaluating the dispatchable potential of electric vehicles considering the user's two-dimensional response willingness is proposed, which is applied to the scenario composed of the power grid dispatch center, electric vehicle aggregator, charging station and distribution network. First, based on the principle of user psychology, the responsiveness of different electric vehicle users to different incentive levels is analyzed, and an uncertainty model of electric vehicle users participating in power grid scheduling under different incentive levels is constructed; secondly, starting from the user's travel time law, an uncertainty response model based on charging time margin is established, and a two-dimensional cloud model improved according to the entropy weight method is introduced to weight the influence of incentive level and charging time margin, and quantify the response willingness of electric vehicle users to participate in scheduling from two dimensions; finally, based on the feasible domain of electric vehicle charging and discharging, the charging and discharging load characteristics of electric vehicles are fully considered, the dispatchable potential of single electric vehicles is evaluated, and the dispatchable capacity of electric vehicle clusters is calculated by statistical analysis of electric vehicle travel laws and charge states, thereby realizing an accurate evaluation of the dispatchable potential of electric vehicle clusters. Specifically, Figure 1 As shown, the method is performed in the following steps: Step 1: Based on the principles of user psychology, establish an uncertainty model of user participation in power grid dispatching under different incentive levels; Step 1.1: Construct a user responsiveness model under different incentive levels; Step 1.1.1, use formula (1) to analyze the deviation of user response under different incentive levels: (1) In formula (1): For the The responsiveness deviation of the electric vehicles participating in the dispatch; For the The incentive level for electric vehicles to participate in dispatch; is the incentive level when all electric vehicle users fully participate in the dispatch, where and is the coefficient of variation of the response deviation with the stimulus level, for The level of motivation when the trend changes from increasing to decreasing.

[0026] Step 1.1.2: Use formula (2) to analyze the user's responsiveness at different incentive levels: (2) In formula (2): For the The higher the responsiveness of electric vehicles participating in grid dispatch, the stronger the willingness of users to participate in grid dispatch. is the slope of user responsiveness changing with the stimulus level.

[0027] Step 1.2: Build a user response state model; Step 1.2.1. Due to the influence of factors such as travel habits and sensitivity to incentive levels, the response behavior of electric vehicle users is uncertain. Formula (3) is used to express the user's dispatchable willingness: (3) In formula (3): For the The response status of electric vehicles participating in grid dispatching, among which Indicates Electric vehicles do not participate in dispatching. Indicates that electric vehicles participate in dispatching; It is the critical responsiveness of electric vehicle users participating in scheduling and not participating in scheduling.

[0028] Step 1.3, construct a response rate model for electric vehicle users to participate in scheduling; Step 1.3.1: In the user response uncertainty model based on user psychology principles, the direct representation of user response behavior is the user response rate. The user response rate under a certain incentive level is expressed using formula (4): (4) In formula (4): The incentive level is Time The response rate of electric vehicles participating in grid dispatch; and Respectively The maximum and minimum values ​​of responsiveness when electric vehicles participate in dispatching.

[0029] Step 2: Considering the travel time pattern of electric vehicle users, an uncertainty response model of charging time margin is established; Step 2.1, charging time margin is also an important factor affecting user participation response. The ratio of actual charging time margin to expected charging time margin is introduced to analyze user participation; Step 2.1.1: Use formula (5) to calculate the total time that electric vehicles are connected to the grid: (5) In formula (5): For the Total time electric vehicles are connected to the grid; For the The time when electric vehicles are off the grid; For the The time when electric vehicles are connected to the grid.

[0030] Step 2.1.2: Use formula (6) to calculate the shortest charging time required for an electric vehicle: (6) In formula (6): For the The shortest charging time required for an electric vehicle; and Respectively The amount of charge of an electric vehicle when it is connected to the grid and when it is off the grid; is the rated capacity of the electric vehicle; For the Charging power of an electric car.

[0031] Step 2.1.3, use formula (7) to calculate the time margin of the electric vehicle: (7) In formula (7): No. The time margin of an electric vehicle.

[0032] Step 2.2, using formula (8) to calculate the time margin response rate of electric vehicle users; (8) In formula (8): For the The time margin response rate of electric vehicles; For the The expected charging time margin for an electric vehicle.

[0033] Step 3: Quantify the response willingness of electric vehicle users to participate in scheduling based on the two-dimensional cloud model improved by the entropy weight method; Step 3.1: Standardize the data and use formula (9) to calculate the entropy weight of information entropy; (9) In formula (9): For the The acceptance weights derived from the cloud models; For the The entropy of the cloud model.

[0034] Step 3.2: Use the entropy weight to weight the two one-dimensional cloud models to obtain a two-dimensional cloud model determined by the excitation level and the margin coefficient. Use formula (10) to calculate The acceptance calculation expression of an electric vehicle is: (10) In formula (10): For the The acceptance of electric vehicles participating in dispatch; and are the weights of the excitation level response rate and the time margin response rate respectively.

[0035] Step 4: Based on the feasible domain of electric vehicle charging and discharging, evaluate the dispatch potential of a single electric vehicle; Step 4.1: Construct the maximum adjustable working area of ​​a single electric vehicle, such as Figure 2 Point A indicates The electric vehicle is connected to the charging pile at all times, and its charge value is . Figure 2 The upper boundary ABC of the shaded part indicates that the electric vehicle starts charging at a certain charging power immediately after connecting to the charging pile. When the power value reaches the maximum power When the electric vehicle is connected to the charging pile, it will immediately discharge to the grid at a certain power. When the SOC value decreases to the minimum value, When the electric vehicle turns to idle state, corresponding to Figure 2 In addition, in order to ensure that the SOC of the electric vehicle can reach the expected minimum value to meet the user's travel needs when it leaves the charging station at a certain time, ,Segment DE represents the boundary where mandatory charging is required before traveling, and EVs will no longer have regulation elasticity.,The demand response capability of EVs at any moment is related to the charge and discharge state and SOC, as well as the departure time.,The domain is determined by its energy and power boundaries, within which the charge and discharge trajectory of EVs can move continuously according to the response task.

[0036] Step 4.2, calculate the dispatchable potential of a single electric vehicle; Step 4.2.1: Calculate the charging and discharging power of electric vehicles using formula (11) (11) In formula (11): For the Electric vehicles in Real-time charging and discharging power during the time period; and They are Electric vehicles in Charging power and discharging power during the time period; and They are Electric vehicles in A 0-1 variable for the charging and discharging state of the period, Indicates Electric vehicles in The period is in a non-charging state. Indicates Electric vehicles in The time period is in charging state; Indicates Electric vehicles in The period is in a non-discharging state. Indicates Electric vehicles in The period is in the discharge state.

[0037] Step 4.2.2: To simplify the analysis, ignore the power change of the electric vehicle within a unit period and use formula (12) to calculate the Electric vehicle power consumption in each period: (12) In formula (12): For the Electric cars in The amount of electricity in each period; For the The amount of electricity consumed by electric vehicles when they are connected to the grid; Indicates Time period Whether the electric vehicle is connected to the charging station, =1 and =0 respectively means Electric cars in Connect and disconnect charging piles in different time periods; The charging completion period; The length of the unit period.

[0038] Step 4.3: Calculate the upward adjustable capacity of the electric vehicle using formula (13): (13) In formula (13): For the Electric car Upward adjustment capability for each time period; is the maximum discharge power of the electric vehicle. Step 4.4: Calculate the downward adjustment capability of the electric vehicle using formula (14): (14) In formula (14): For the Electric car Downward adjustment capability for each time period; The maximum charging power for electric vehicles.

[0039] Step 5: Evaluate the dispatch potential of electric vehicle clusters based on user acceptance; Step 5.1: The dispatchable capacity of the electric vehicle cluster is not only related to the inherent properties of the battery (such as battery capacity, state of charge, charge and discharge characteristic curve, charge and discharge power and charge and discharge efficiency, etc.), but also affected by the driving habits of electric vehicle users (such as driving distance, driving time, vehicle parking location, etc.). The grid access time of the electric vehicle cluster is calculated by formula (15) and follows a normal distribution: (15) In formula (15): The grid access time distribution of electric vehicle clusters; is the average time of electric vehicle cluster access to the grid, and its value is 18; is the network access time variance, and its value is 3.3.

[0040] Step 5.2: Calculate the off-grid time of the electric vehicle cluster using formula (16) and it follows the normal distribution: (16) In formula (16): The grid access time distribution of electric vehicle clusters; is the average time of electric vehicle cluster access to the grid, and its value is 8; The electric vehicle cluster is the grid access time variance, which is 3.2.

[0041] Step 5.3: Calculate the electric vehicle grid-connected power value through formula (17) and it follows the normal distribution: (17) In formula (17): The grid access time distribution of electric vehicle clusters; is the average time of electric vehicle cluster access to the grid, and its value is 0.3; The electric vehicle cluster is the grid access time variance, which is set to 0.15.

[0042] Step 5.4: Based on the Monte Carlo simulation algorithm, the dispatch capacity of the electric vehicle cluster is evaluated using formula (18): (18) In formula (18): upward dispatchable capacity for electric vehicle clusters; downward dispatchable capacity for electric vehicle clusters; is the number of electric vehicles; The total number of time periods.

[0043] In summary, the present invention firstly analyzes the responsiveness of different electric vehicle users to different incentive levels based on the principle of user psychology, and constructs an uncertainty model for electric vehicle users to participate in grid dispatching under different incentive levels; secondly, starting from the user travel time pattern, an uncertainty response model based on charging time margin is established, and a two-dimensional cloud model improved according to the entropy weight method is introduced to quantify the response willingness of electric vehicle users to participate in dispatching; finally, based on the feasible domain of electric vehicle charging and discharging, the dispatchable potential of a single electric vehicle is evaluated, and the dispatchable potential of an electric vehicle cluster is evaluated by using a Monte Carlo simulation algorithm through the analysis of the travel pattern and charge state of electric vehicles. The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0044] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0046] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0047] Finally: 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 in the protection scope of the present invention.

Claims

1. A method for evaluating the dispatchability potential of electric vehicles considering the user's dual-dimensional response willingness, characterized in that: The steps include: Establish uncertainty models for user participation in grid dispatch under different incentive levels, including a response rate model for electric vehicle users to participate in dispatch and an uncertainty response model for charging time margin; Based on the uncertainty model of users' participation in grid dispatch under different incentive levels, the acceptance of electric vehicles is calculated and the response willingness of electric vehicle users to participate in dispatch is quantified; Assess the dispatchability potential of electric vehicle clusters based on user acceptance.

2. The electric vehicle dispatchability potential evaluation method considering the user's dual-dimensional response willingness according to claim 1 is characterized in that: The response rate model of electric vehicle users participating in scheduling is specifically: Where: The incentive level is Time The response rate of electric vehicles participating in grid dispatch; and Respectively The maximum and minimum values ​​of responsiveness when two electric vehicles participate in dispatching.

3. The electric vehicle dispatchability potential assessment method considering the user's dual-dimensional response willingness according to claim 2 is characterized in that: The uncertainty response model of the charging time margin is constructed, and the specific steps are as follows: Acquiring first data of the electric vehicle, the first data including the charge amount of the electric vehicle when it is connected to the grid and when it is off the grid, the rated capacity of the electric vehicle, and the charging power of the electric vehicle; Calculate the shortest charging time required for the electric vehicle according to the first data; Calculate the time margin of electric vehicles based on the shortest charging time required by electric vehicles; According to the time margin of electric vehicles, the time margin response rate of electric vehicle users is calculated.

4. According to the method for evaluating the dispatchable potential of electric vehicles taking into account the user's dual-dimensional response willingness according to claim 3, the time margin of the electric vehicle is as follows: Where: For the The time margin of an electric vehicle, For the The shortest charging time required for an electric car, For the Total time electric vehicles are connected to the grid; The time margin response rate of electric vehicle users is calculated as follows: Where: For the The time margin response rate of electric vehicles; For the The expected charging time margin for an electric vehicle.

5. According to the method for evaluating the dispatchable potential of electric vehicles taking into account the user's dual-dimensional response willingness according to claim 4, the acceptance of the electric vehicle is expressed as follows: In the formula, For the The acceptance of electric vehicles participating in dispatch; and are the weights of the stimulus level response rate and the time margin response rate, The incentive level is Time The response rate of electric vehicles participating in grid dispatching.

6. The method for evaluating the dispatchable potential of electric vehicles considering the user's dual-dimensional response willingness according to claim 4, wherein the dispatchable potential of the electric vehicle cluster is evaluated based on user acceptance, and the dispatch capacity of the electric vehicle cluster is evaluated specifically by the following formula; Where: upward dispatchable capacity for electric vehicle clusters; downward dispatchable capacity for electric vehicle clusters; is the number of electric vehicles; is the total number of time periods, For the Electric car The upward adjustment capability of the time period, For the Electric car The downward adjustment capability of the time period For the Electric vehicles in Real-time charging and discharging power during the period.

7. According to the method for evaluating the dispatchable potential of electric vehicles taking into account the user's dual-dimensional response willingness according to claim 6, the upward adjustment capability of the electric vehicle is calculated using the following formula: Where: is the maximum discharge power of the electric vehicle, For the Electric cars in The amount of electricity in each period; For the Time period Whether the electric vehicle is connected to the charging pile, =1 and =0 respectively Electric cars in Connect and disconnect the charging pile in different time periods. is the minimum SOC limit of electric vehicles; The downward adjustment capability of electric vehicles is calculated using the following formula: Where: For the Electric car Downward adjustment capability for each time period; is the maximum charging power of the electric vehicle, It is the maximum SOC limit of electric vehicles.

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