Electric vehicle space-time dual-scale optimization scheduling method and device considering user satisfaction

Abstract

The invention relates to an electric vehicle space-time dual-scale optimization scheduling method and device considering user satisfaction. The method comprises the following steps: collecting charging demand data, power grid historical load data, user satisfaction related data and charging station related information of an electric vehicle; based on the collected data, establishing an electric vehicle time layer optimization scheduling model considering user satisfaction and load fluctuation; the optimal charging strategy of the electric vehicle is optimized by taking the maximization of the user charging cost satisfaction degree and the minimization of the power grid load fluctuation as optimization objectives; constructing an electric vehicle space layer optimization scheduling model considering user satisfaction and load fluctuation; and a target function is constructed by taking user travel satisfaction maximization as an optimization target, and a space layer electric vehicle charging station distribution condition is obtained through optimization according to a time layer optimal strategy. Compared with the prior art, the method can guide automobile charging and select a proper charging station while improving the user satisfaction, and achieves the balance between the high efficiency of charging scheduling and the load of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimal scheduling, and in particular to a spatio-temporal dual-scale optimal scheduling method for electric vehicles considering user satisfaction and load fluctuations. Background Art

[0002] As an important part of new energy transportation vehicles, the management of the charging demand of electric vehicles has become a core technical challenge in the field of smart grids. With the rapid popularization of electric vehicles and the large-scale access of renewable energy, the strong randomness and strong dispersion at both the power generation and consumption ends have exacerbated the load fluctuations of the power grid. How to effectively balance the contradiction between the charging demand of electric vehicles and the load fluctuations of the power grid has become one of the key issues in current smart grid scheduling.

[0003] Current charging scheduling research mainly focuses on suppressing the power grid load through centralized or distributed control strategies. However, these methods usually only optimize the load distribution at the technical level and lack sufficient consideration of user charging satisfaction. With the popularization of electric vehicles, the user experience has become an important factor affecting their market acceptance. Current technical means often cannot effectively cope with problems such as too long charging queue time and too high charging cost, thus reducing user satisfaction. In addition, existing technologies lack comprehensive consideration in the scheduling optimization of time and space dimensions, and it is difficult to fully alleviate the load fluctuations of the power grid caused by the access of new energy and the increase in the number of electric vehicles while improving user satisfaction. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a spatio-temporal dual-scale optimal scheduling method and device for electric vehicles considering user satisfaction and load fluctuations.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] As the first aspect of the present invention, a spatio-temporal dual-scale optimal scheduling method for electric vehicles considering user satisfaction is provided, and the steps include:

[0007] Collect the charging demand data of electric vehicles, the historical load data of the power grid, the user satisfaction-related data, and the relevant information of the charging stations. The user satisfaction-related data includes charging cost, detour distance, and queuing waiting time;

[0008] Based on the collected charging cost and the historical load data of the power grid, establish a time-layer optimal scheduling model for electric vehicles considering user satisfaction and load fluctuations; with the maximization of user charging cost satisfaction and the minimization of power grid load fluctuations as the optimization objectives, and with charging demand, power grid load capacity, and charging time arrangement as constraints, optimize the optimal charging strategy of electric vehicles;

[0009] Based on the collected user detour distance, queuing waiting time, and charging station - related information, an optimization scheduling model for electric vehicles at the spatial layer considering user satisfaction and load fluctuation is constructed; a target function is constructed with the maximization of user travel satisfaction as the optimization goal, and with charging demand and grid load capacity as constraint conditions, the allocation of electric vehicle charging stations at the spatial layer is optimized according to the optimal strategy at the time layer.

[0010] As a preferred technical solution, the charging demand data of the electric vehicle includes the charging start time, end time, charging duration, initial SOC, and off - grid SOC of each electric vehicle;

[0011] The historical load data of the grid includes the load curve, peak - valley difference, and load fluctuation;

[0012] The data related to user satisfaction includes charging cost, detour distance, and queuing waiting time;

[0013] The collected charging - station - related information includes the location of the charging station, the number of charging piles, and the capacity of the charging station.

[0014] As a preferred technical solution, the user's charging - cost satisfaction is expressed as:

[0015]

[0016] In the formula, C i is the charging cost of the i - th user, is the maximum acceptable charging cost of user i, and w expense is the weight coefficient of cost satisfaction.

[0017] As a preferred technical solution, the objective function of the electric - vehicle time - layer optimization scheduling model specifically includes maximizing the user's charging - cost satisfaction function and minimizing the grid load fluctuation and charging time span:

[0018]

[0019] In the formula, is the grid load at time t, is the average value of the grid load, and are the charging end time and start time of the i - th electric vehicle respectively.

[0020] As a preferred technical solution, the constraint conditions of the electric - vehicle time - layer optimization scheduling model include:

[0021] Charging - demand constraint: The actual charging amount of the i - th electric vehicle is greater than or equal to the charging demand of the electric vehicle;

[0022] Grid load capacity constraint: The sum of the actual charging amounts of electric vehicles is less than or equal to the maximum capacity of the grid load;

[0023] Charging time arrangement constraint: The arranged charging time is between the start charging time and the end charging time of the electric vehicle.

[0024] As a preferred technical solution, the user travel satisfaction is expressed as:

[0025]

[0026] In the formula, D i is the detour distance of the i-th user, Q i is the queuing waiting time of the i-th user, D max and Q max are respectively the maximum detour distance and the maximum queuing waiting time among all users, w dist and w wait are respectively the weight coefficients of the detour distance satisfaction and the queuing waiting time satisfaction.

[0027] As a preferred technical solution, the objective function of the electric vehicle spatial layer optimization scheduling model is specifically to maximize the user travel satisfaction function

[0028]

[0029] In the formula, n is the total number of users.

[0030] As a preferred technical solution, the constraint conditions of the electric vehicle spatial layer optimization scheduling model include:

[0031] Charging station selection constraint:

[0032]

[0033] In the formula, n is the total number of users; is an indicator function. If user i selects charging station j for charging, the function value is 1, otherwise it is 0;

[0034] Charging station capacity limit constraint:

[0035]

[0036] In the formula, is the capacity of charging station j;

[0037] Charging station allocation uniqueness constraint:

[0038]

[0039] In the formula, m is the total number of charging stations, ensuring that each user only selects one charging station for charging.

[0040] As a second aspect of the present invention, there is provided an electronic device, comprising:

[0041] One or more processors;

[0042] A memory for storing one or more programs;

[0043] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned spatio-temporal dual-scale optimal scheduling method for electric vehicles considering user satisfaction.

[0044] As a third aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned spatio-temporal dual-scale optimal scheduling method for electric vehicles considering user satisfaction are implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The spatio-temporal dual-scale optimal scheduling method constructed by the present invention takes into account both user satisfaction and grid load fluctuations. By collecting the charging demands of electric vehicles, historical grid load data, user satisfaction-related data, and charging station information, a comprehensive optimization model for the time layer and the space layer is constructed. By comprehensively considering the complex relationship between the charging demands of electric vehicles and grid load fluctuations, and by establishing an optimization model in two dimensions of time and space, the aim is to achieve the efficiency of charging scheduling and the balance of grid load. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the spatio-temporal dual-scale optimal scheduling of electric vehicles considering user satisfaction and load fluctuations according to the present invention;

[0048] Figure 2 It is a schematic diagram of the comparison of grid loads under the traditional strategy and the spatio-temporal dual-scale optimal scheduling strategy based on the present invention;

[0049] Figure 3 It is a schematic diagram of the comparison of user satisfaction under the traditional strategy and the spatio-temporal dual-scale optimal scheduling strategy based on the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0051] Embodiment 1

[0052] As one of the implementation manners of the present invention, this embodiment provides a spatio-temporal dual-scale optimal scheduling method for electric vehicles considering user satisfaction and load fluctuation, as follows Figure 1 shown, which includes the following steps:

[0053] S1. Collect the charging demand data of electric vehicles, including the charging start time, end time, charging duration, initial SOC (remaining battery power), off-grid SOC, etc. of each electric vehicle. Then, collect the historical load data of the power grid, including the load curve, peak-valley difference value, load fluctuation condition, etc., and evaluate the load capacity and load fluctuation of the power grid. Further, collect the data related to user satisfaction, such as charging cost, detour distance, queuing waiting time, etc., for constructing a user cost satisfaction function and a travel satisfaction function to improve the user charging experience. Finally, collect the relevant information of the charging station, including the location of the charging station, the number of charging piles, the capacity of the charging station, etc., for constructing a spatial layer optimal scheduling model.

[0054] S2. Based on the collected data such as charging cost and historical load of the power grid, construct an optimal scheduling model for electric vehicle charging on the time scale. The optimal scheduling of electric vehicle charging needs to consider the benefits of both the power grid and electric vehicle users. For the power grid, it is necessary to reduce the peak-valley difference value of the load curve and minimize the fluctuation; for users, it is necessary to reduce the cost as much as possible to minimize the cost. Therefore, the specific construction process of the optimal scheduling model for electric vehicle charging at the time layer is as follows:

[0055] 2.1 Objective function

[0056] 2.1.1. Maximize the user cost satisfaction function:

[0057]

[0058] In the formula, is the charging cost satisfaction of the i-th user, which can be expressed as:

[0059]

[0060] In the formula, C i is the charging cost of the i-th user, is the maximum acceptable charging cost of user i, and w expense is the weight coefficient of the cost satisfaction.

[0061] 2.1.2. Minimize the power grid load fluctuation:

[0062]

[0063] In the formula, is the power grid load at time t, is the average value of the power grid load.

[0064] 2.1.3 Minimization of charging time span:

[0065]

[0066] Wherein, and are the charging end time and start time of the i-th electric vehicle respectively.

[0067] 2.2 Constraint conditions:

[0068] 2.2.1. Charging demand constraint:

[0069]

[0070] Wherein, is the actual charging amount of the i-th electric vehicle, is the charging demand of the i-th electric vehicle.

[0071] 2.2.2. Grid load capacity constraint:

[0072]

[0073] Wherein, is the maximum capacity of the grid load.

[0074] 2.2.3. Charging time arrangement constraint:

[0075]

[0076] Wherein, are the start charging time and end charging time of the i-th electric vehicle respectively.

[0077] S3. According to the collected information such as the detour distance of users, queuing waiting time, and relevant information of charging stations, construct a spatial layer optimization model, that is, the optimal choice of charging stations by electric vehicle users. This model aims to maximize the travel satisfaction of users and seek the optimal scheduling strategy. Among them, the travel satisfaction comprehensively considers two key factors: the detour distance of users and the queuing waiting time. By reducing the detour and waiting time of users, the travel satisfaction of users is improved. Therefore, the specific construction process of the spatial layer charging optimization scheduling model for electric vehicles is as follows:

[0078] 3.1 Objective function

[0079]

[0080] Wherein: is the travel satisfaction of the i-th user, and its mathematical expression is as follows:

[0081]

[0082] Where: D i is the detour distance of the i-th user, Q i is the queuing waiting time of the i-th user, D max and Q max are respectively the maximum detour distance and the maximum queuing waiting time among all users, w dist and w wait are respectively the weight coefficients of the satisfaction degree of detour distance and the satisfaction degree of queuing waiting time.

[0083] 3.2 Constraints

[0084] 3.2.1. Charging station selection constraint:

[0085]

[0086] Where n is the total number of users; is an indicator function, if user i selects charging station j for charging, the function value is 1, otherwise it is 0.

[0087] 3.2.2. Charging station capacity limit constraint:

[0088]

[0089] Where is the capacity of charging station j.

[0090] 3.2.3. Charging station allocation uniqueness constraint:

[0091]

[0092] Where m is the total number of charging stations, ensuring that each user selects only one charging station for charging.

[0093] S4. According to the above model design, first, according to the charging time arrangement determined by the time-layer optimization scheduling model, combined with the charging station allocation determined by the space-layer optimization scheduling model, calculate the cost satisfaction and travel satisfaction of each user. Subsequently, evaluate the load fluctuation of the power grid under this scheduling plan to ensure that the power grid load is within an acceptable capacity range.

[0094] Example 2

[0095] This embodiment provides a specific numerical example to further illustrate the present invention. In this embodiment, it is assumed that there are 10 electric vehicles and 3 charging stations, and each station is equipped with 2 charging piles. Each electric vehicle needs to be charged with 50 kWh, and the charging cost is $0.2 / kWh. To simplify the calculation, the weight coefficients for cost satisfaction, detour distance satisfaction, and queuing waiting time satisfaction are all set to 1. In addition, a maximum detour distance of 5 kilometers and a maximum waiting time of 30 minutes are also set as considerations for user satisfaction.

[0096] Strategy A (traditional scheduling): Under this strategy, electric vehicles randomly select charging stations and start charging immediately, without considering user satisfaction or grid load optimization.

[0097] Table 1 Scheduling results of the traditional strategy

[0098]

[0099] Strategy B (optimized scheduling): Strategy B optimized based on the method of the present invention comprehensively considers user cost satisfaction and user travel satisfaction, and at the same time pays attention to grid load fluctuations to achieve spatio-temporal dual-scale optimized scheduling.

[0100] Table 2 Scheduling results of the spatio-temporal dual-scale optimization method proposed by the present invention

[0101]

[0102]

[0103] Based on the method and constraints proposed in this embodiment, according to the charging time arrangement determined by the time-layer optimized scheduling model, combined with the charging station allocation determined by the space-layer optimized scheduling model, calculate the cost satisfaction and travel satisfaction of each user. Subsequently, evaluate the load fluctuation of the power grid under this scheduling plan to ensure that the power grid load is within an acceptable capacity range. Finally, it can be obtained Figures 2-3The results are shown as follows. As can be seen from the figure, the optimized Strategy B based on the method of the present invention is significantly superior to the traditional Strategy A in terms of user satisfaction. Although the cost satisfaction of the two strategies is the same because the charging cost is fixed in this implementation case, Strategy B significantly reduces the detour distance and waiting time of users by optimizing the charging time and location, thus improving the travel satisfaction. Specifically, the vehicles of Strategy B optimized based on the method of the present invention charge during off-peak hours, effectively avoiding the peak of the grid load, and at the same time, the travel satisfaction of users is improved due to shorter detours and waiting times. In addition, from the grid load data, the grid load of Strategy B optimized based on the method of the present invention during the grid peak hours (09:00 - 13:00 and 17:00 - 21:00) is significantly lower than that of Strategy A, indicating that this optimized scheduling method can effectively reduce the grid load fluctuation without sacrificing user satisfaction.

[0104] Example 3

[0105] As a second aspect of the present invention, the present application further provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the electric vehicle spatio-temporal dual-scale optimized scheduling method considering user satisfaction and load fluctuation as described above. In addition to the above-mentioned processors, memory, and interfaces, any device with data processing capabilities where the device in the embodiment is located usually includes other hardware according to the actual functions of the device with data processing capabilities, which will not be elaborated here.

[0106] Example 4

[0107] As a third aspect of the present invention, the present application further provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the electric vehicle spatio-temporal dual-scale optimized scheduling method considering user satisfaction and load fluctuation as described above is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or will be output.

[0108] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field according to the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. A method for optimizing the scheduling of electric vehicles in time and space in two scales considering user satisfaction, characterized in that the steps include: Collect data on electric vehicle charging demand, historical grid load data, user satisfaction data, and charging station information; Based on the collected charging costs and historical load data of the power grid, an electric vehicle time-layer optimization scheduling model is established that takes into account user satisfaction and load fluctuations. The optimal charging strategy for electric vehicles is optimized with the maximization of user charging cost satisfaction and the minimization of power grid load fluctuations as the optimization goals, and the charging demand, power grid load capacity and charging time schedule as constraints. Based on the collected information about user detour distances, waiting times in queues, and charging stations, a spatial-layer optimization scheduling model for electric vehicles is constructed that takes into account user satisfaction and load fluctuations. The objective function is constructed with the maximization of user travel satisfaction as the optimization goal, and the charging demand and grid load capacity are used as constraints. The spatial-layer allocation of electric vehicle charging stations is optimized according to the optimal strategy of the time layer.

2. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: The collected charging demand data of electric vehicles includes the charging start time, end time, charging duration, initial SOC and off-grid SOC of each electric vehicle; the historical load data of the power grid includes the load curve, peak-to-valley difference and load fluctuation; the collected charging station related information includes the location of the charging station, the number of charging piles and the capacity of the charging station.

3. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: The user's satisfaction with charging costs is expressed as: In the formula, C i is the charging cost of the i-th user, is the maximum acceptable charging cost for user i, w expense is the weight coefficient of cost satisfaction.

4. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 3 is characterized in that: The objective function of the electric vehicle time-layer optimization scheduling model specifically includes maximizing the user charging cost satisfaction function and minimizing the grid load fluctuation and electricity time span: In the formula, is the grid load at time t, is the average grid load, and are the charging end time and start time of the i-th electric vehicle respectively.

5. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 4 is characterized in that: The constraints of the electric vehicle time layer optimization scheduling model include: Charging demand constraint: the actual charging amount of the i-th electric vehicle is greater than or equal to the charging demand of the electric vehicle; Grid load capacity constraint: The sum of the actual charging capacity of electric vehicles is less than or equal to the maximum capacity of the grid load; Charging time scheduling constraint: The scheduled charging time is between the time when the EV starts charging and the time when the EV ends charging.

6. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: The user travel satisfaction is expressed as: Where D i is the detour distance of the i-th user, Q i is the waiting time of the ith user, D max and Q max are the maximum detour distance and maximum queue waiting time among all users, respectively, dist and w wait are the weight coefficients of satisfaction with detour distance and satisfaction with queue waiting time respectively.

7. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 6 is characterized in that: The objective function of the electric vehicle space layer optimization scheduling model is specifically to maximize the user travel satisfaction function In the formula, n is the total number of users, is the travel satisfaction of the i-th user.

8. The electric vehicle time-space dual-scale optimization scheduling method considering user satisfaction according to claim 1 is characterized in that: The constraints of the electric vehicle space layer optimization scheduling model include: Charging station selection constraints: In the formula, n is the total number of users; is an indicator function. If user i chooses charging station j for charging, the function value is 1, otherwise it is 0; Charging station capacity limitation constraints: In the formula, is the capacity of charging station j; Charging station allocation uniqueness constraint: Where m is the total number of charging stations, ensuring that each user only chooses one charging station for charging.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the electric vehicle spatiotemporal dual-scale optimization scheduling method considering user satisfaction as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the spatiotemporal dual-scale optimization scheduling method for electric vehicles considering user satisfaction are implemented as described in any one of claims 1 to 8.

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