Method, system and electronic equipment for quantifying spare capacity of electric vehicle

Abstract

The present invention discloses a method, system, and electronic device for quantifying the spare capacity of electric vehicles, relating to the technical field of vehicle-grid data interaction. The present invention constructs an electric vehicle charging power optimization model based on electric vehicle operating data, time-of-use electricity prices, and electric vehicle charging power to formulate an electric vehicle power plan. A multi-timescale lower reserve quantification model for electric vehicles is established, taking into account electric vehicle battery capacity constraints. A multi-timescale upper reserve quantification model for electric vehicles is established, taking into account electric vehicle off-grid time and electric vehicle travel energy demand. Based on the established upper and lower reserve quantification models, rolling optimization is used to achieve real-time quantification of spare capacity. This method improves the robustness of spare capacity quantification while enhancing its universality, thereby providing effective support for optimized scheduling of electric vehicles and having practical significance for electric vehicles' participation in grid interaction.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-network data interaction, and in particular to a method, system and electronic equipment for quantifying the spare capacity of an electric vehicle. Background Art

[0002] As a clean means of transportation, electric vehicles (EVs) have seen a significant increase in their ownership, driven by policy support. The increasing number of EVs creates new challenges for power system power balance, while also providing a significant amount of mobile energy storage resources. Therefore, guiding the orderly charging of EVs is a crucial measure for supporting the safe and economical operation of the power grid. Quantifying EV reserve capacity is fundamental to achieving efficient interaction between EVs and the power grid. Numerous scholars and experts have conducted research on this topic, proposing methods for quantifying reserve capacity. However, existing methods primarily optimize planned power and EV reserve capacity, aiming to maximize returns from participating in the reserve market. These methods consider the relationship between planned power, reserve capacity, and the power cap during optimization, but fail to consider the impact of deployed reserve capacity on future reserve capacity. Furthermore, global reserve capacity quantification carries the risk of failure during real-time operation, as changes in EV charging power can directly impact the accuracy of reserve capacity quantification. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method, system and electronic equipment for quantifying the spare capacity of an electric vehicle.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for quantifying the spare capacity of an electric vehicle, comprising:

[0006] Construct an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power;

[0007] generating an electric vehicle power plan based on the electric vehicle charging power optimization model, and using the electric vehicle power plan as a baseline power for quantifying the electric vehicle reserve capacity;

[0008] Building a multi-time-scale lower reserve optimization model for electric vehicles based on the baseline power and the capacity constraints of the electric vehicle battery;

[0009] A multi-time-scale upper reserve optimization model for electric vehicles is established based on the off-grid time of electric vehicles and the energy requirements of electric vehicle travel.

[0010] Based on the lower reserve optimization model and the upper reserve optimization model, rolling optimization is used to achieve real-time quantification of reserve capacity.

[0011] Preferably, the electric vehicle charging power optimization model is:

[0012]

[0013] Where i is the number of the electric vehicle, t is the time, Δt is a time interval, P i (t) is the charging power of electric vehicle i at time t, p tou (t) is the charging electricity price at time t, is the time when electric vehicle i is connected to the grid, The pick-up time for the electric car i.

[0014] Preferably, the lower standby optimization model is:

[0015]

[0016] The capacity constraint of electric vehicle batteries is:

[0017]

[0018] ΔP i d,max (t)≥0;

[0019]

[0020] Where ΔP i d,max (t) is the maximum reserve of electric vehicle i at time t, t now is the current moment, K is the time window, κ is the time label index, P i (t) is the charging power of electric vehicle i at time t, is the maximum charging power of the charging pile, is the maximum charging power of the electric vehicle battery, min(*) takes the minimum value, Δt is a time interval, S max is the maximum state of charge of the electric vehicle, S i (t now ) is the state of charge of electric vehicle i at the current moment, is the rated capacity of the battery of electric vehicle i, and η is the charging efficiency of the electric vehicle.

[0021] Preferably, the upper standby optimization model is:

[0022]

[0023] Constraints of the above backup optimization model:

[0024] P i (t)-ΔP i u,max (t)≥0;

[0025] ΔP i u,max (t)≥0;

[0026]

[0027]

[0028] Where ΔP i u,max (t) is the maximum reserve of electric vehicle i at time t, t now is the current moment, K is the time window, κ is the time label index, is the maximum charging power of the charging pile, is the maximum charging power of the electric vehicle battery, min(*) takes the minimum value, P i (t) is the charging power of electric vehicle i at time t, is the pick-up time of electric car i, P max is the maximum charging power.

[0029] Preferably, the real-time quantification of the reserve capacity is achieved by using rolling optimization based on the lower reserve optimization model and the upper reserve optimization model, specifically including:

[0030] Based on the lower reserve optimization model and the upper reserve optimization model, the real-time power of the electric vehicle is controlled by using rolling optimization and a real-time reserve response signal;

[0031] updating the state of charge information of the electric vehicle according to the real-time power obtained by the control, and determining whether a new electric vehicle is connected to the power grid;

[0032] If new electric vehicles are connected to the power grid, the total number of electric vehicles is updated, and the planned power and backup capacity for multiple future time windows are determined based on the updated state of charge information and the time when the electric vehicles are off the grid.

[0033] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0034] The electric vehicle spare capacity quantification method provided by the present invention constructs an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power to formulate an electric vehicle power plan; considers the electric vehicle battery capacity constraint to establish a lower reserve quantification model for electric vehicles at multiple time scales; considers the electric vehicle off-grid time and the electric vehicle travel energy demand to establish an upper reserve quantification model for electric vehicles at multiple time scales; based on the established upper and lower reserve quantification models, rolling optimization is used to achieve real-time quantification of the reserve capacity to achieve real-time quantification of the reserve capacity of electric vehicles at multiple time scales, which can improve the robustness of the reserve capacity quantification while improving the universality, thereby providing effective support for the optimal scheduling of electric vehicles and having practical significance in the participation of electric vehicles in grid interaction.

[0035] Corresponding to the above-mentioned electric vehicle reserve capacity quantification method, the present invention also provides the following implementation structure:

[0036] A system for quantifying the spare capacity of an electric vehicle, comprising:

[0037] A first model building module is used to build an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power;

[0038] a baseline power determination module, configured to generate an electric vehicle power plan based on the electric vehicle charging power optimization model, and use the electric vehicle power plan as a baseline power for quantifying the electric vehicle's spare capacity;

[0039] A second model building module is used to build a multi-time-scale lower reserve optimization model of the electric vehicle based on the baseline power and the capacity constraint of the electric vehicle battery;

[0040] The third model building module is used to establish an upper reserve optimization model for electric vehicles at multiple time scales based on the off-grid time of electric vehicles and the travel energy requirements of electric vehicles;

[0041] The capacity real-time quantification module is used to realize the real-time quantification of the spare capacity by using rolling optimization based on the lower spare optimization model and the upper spare optimization model.

[0042] An electronic device, comprising:

[0043] A memory for storing a logic control program;

[0044] A processor is connected to the memory and is used to retrieve and execute the logic control program to implement the above-mentioned electric vehicle spare capacity quantification method.

[0045] Preferably, the memory is a computer readable storage device.

[0046] Since the technical effects achieved by the above-mentioned implementation structure provided by the present invention are the same as the technical effects achieved by the electric vehicle reserve capacity quantification method provided by the present invention, they will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A flow chart of the electric vehicle spare capacity quantification method provided by the present invention;

[0049] Figure 2 A schematic diagram of the definition of the lower standby quantization model provided in an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the upper standby quantization model definition provided in an embodiment of the present invention;

[0051] Figure 4 This is a structural diagram of the electric vehicle spare capacity quantification system provided by the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0053] The purpose of the present invention is to provide a method, system and electronic equipment for quantifying the spare capacity of electric vehicles, which can improve the robustness of the spare capacity quantification and provide effective support for the optimized scheduling of electric vehicles.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] like Figure 1 As shown, the electric vehicle spare capacity quantification method provided by the present invention includes:

[0056] Step 100: Construct an EV charging power optimization model based on EV operation data, time-of-use electricity prices, and EV charging power. In this step, the EV operation data used includes the time the EV was connected to the grid, the state of charge (SOC) at the time of connection, the time the EV was picked up, and the expected power level.

[0057] The implementation process is as follows: establish the objective function with the minimum charging cost of electric vehicles, establish the electric vehicle charging power constraints, battery physical constraints, and charging station total charging capacity constraints.

[0058] To simplify the charging process, the discretization of the time scale is illustrated using a 15-minute period as an example, and it is assumed that the charging power of the electric vehicle does not change within a period. The time scale of one day is then divided into 96 time periods. The time when the electric vehicle is connected to the grid means the moment when the charging power optimization begins, and the optimized charging power is greater than or equal to zero. The time of picking up the vehicle is the time when the electric vehicle unplugs the charging gun, at which time the electric vehicle power is zero. The objective function is established with the minimum charging cost of the electric vehicle. The specific calculation method of the objective function (i.e., the electric vehicle charging power optimization model) is as follows:

[0059]

[0060] Where i is the number of the electric vehicle, t is the time, and Δt is a time interval, which is 15 minutes. i (t) is the charging power of electric vehicle i at time t, p tou (t) is the charging electricity price at time t, is the time when electric vehicle i is connected to the grid. The pick-up time for the electric car i.

[0061] As shown in equation (1), electric vehicles use a time-of-use electricity price when charging. This is because the load varies between peaks and valleys. The grid or aggregator formulates a price strategy to balance the load. During peak load periods, charging prices are higher, while during valley periods, prices are lower. This encourages users to reduce charging during peak load periods and increase charging during valley periods. Based on time-of-use electricity prices, it is assumed that electric vehicle users are completely rational and have the same response characteristics in order to reduce charging costs.

[0062] Electric vehicle batteries have time-coupled characteristics. To meet the travel needs of electric vehicle users and the power limitations during charging, the following constraints exist:

[0063]

[0064]

[0065]

[0066]

[0067] S min ≤S i (t)≤S max (6)

[0068]

[0069]

[0070] Where, The maximum charging power that can be achieved by the charging pile. E is the maximum charging power that an electric vehicle battery can support. i (t) is the battery capacity of electric vehicle i during period t. is the rated capacity of the electric vehicle battery. i (t) is the SOC of electric vehicle i in period t. η is the charging efficiency of the electric vehicle. S min The minimum SOC of an electric vehicle is usually 0. max It is the maximum SOC of electric vehicles, usually 1. is the SOC of electric vehicle i when it is connected to the grid. It is the SOC that electric vehicle i expects to achieve when the charging gun is unplugged, reflecting the charging demand of the electric vehicle.

[0071] Among the above constraints, formula (2) represents the charging power limit of electric vehicles. The charging power of electric vehicles is not only limited by the maximum charging power that the electric vehicle charging pile can support, but also by the charging power allowed by the electric vehicle battery itself. Therefore, the optimized charging power of the electric vehicle is the minimum of the two, thereby ensuring the charging safety of the battery. Formula (3) indicates that the charging power of the electric vehicle is zero when the electric vehicle is not plugged in the charging gun, that is, before it is connected to the power grid and after it is unplugged and off the grid. Formula (4) shows how to calculate the SOC of the electric vehicle, where the rated power of the electric vehicle belongs to the parameters of the electric vehicle's own battery. Formula (5) represents the recursive formula of the time period before and after the SOC of the electric vehicle, which takes into account the influence of the charging power and charging efficiency of the current period. Formula (6) represents the characteristics of the energy storage of the electric vehicle battery. Since the battery power of the electric vehicle cannot exceed the rated capacity of the battery, the SOC of the electric vehicle is less than 1. Since the battery power of the electric vehicle cannot be negative, the SOC is greater than 1. Formula (7) shows that the SOC of the electric vehicle at the moment of grid access is Formula (8) indicates that the SOC of an electric vehicle at the time of leaving the grid should be greater than the SOC expected by the electric vehicle user. The SOC expected by the user reflects the energy demand of the electric vehicle user.

[0072] Step 101: Generate an electric vehicle power plan based on the electric vehicle charging power optimization model, and use the electric vehicle power plan as the baseline power for quantifying the electric vehicle reserve capacity. That is, the charging plan obtained with the lowest charging cost as the goal is used as the baseline power for quantifying the electric vehicle reserve capacity.

[0073] Step 102: Build a multi-timescale lower reserve optimization model for electric vehicles based on the baseline power and the capacity constraints of the electric vehicle battery. The lower reserve for electric vehicles represents the additional charging power that the electric vehicle adds to the planned power when the grid frequency is high (high), and the definition of reserve has a time attribute. In response to the lower reserve, the electric vehicle increases its power consumption and reduces the system frequency. Therefore, the lower reserve optimization model for electric vehicles is established by considering the capacity constraints and power cap constraints of the electric vehicle battery.

[0074] Since step 100 is described with a time period of 15 minutes, the standby time in this step is also described with a time period of 15 minutes. Figure 2 Explain the definition of spare under electric vehicles, such as Figure 2 As shown in the figure, the gray rectangular area enclosed by the black solid line during the period t1-t4 represents the planned power of the electric vehicle, and the lower reserve of the electric vehicle during the period t1-t2 is quantified, that is, the ability of the electric vehicle to increase its charging power. Figure 2 If the EV increases its charging power between t1 and t2, the overall charging plan changes to prevent the EV from exceeding the battery's rated capacity. If the dark gray lower reserve fully responds, the EV's charging power curve shifts to the area enclosed by the dashed line, reducing the light gray charging area between t3 and t4 to meet the charging capacity constraint.

[0075] Based on this, the multi-time-scale reserve optimization model of electric vehicles established in this step is:

[0076]

[0077] The constraints of the multi-time-scale reserve optimization model for electric vehicles are:

[0078]

[0079] ΔP i d,max (t)≥0 (11)

[0080]

[0081] Where ΔP i d,max (t) is the maximum reserve that electric vehicle i can provide at time t, t nowis the current moment, K is the time window for solving the next backup, and κ is the time label index.

[0082] The present invention transforms the quantification of electric vehicle reserve capacity into an optimization problem, wherein Equation (9) represents the optimization target of electric vehicle reserve quantification, i.e., the maximum reserve that all electric vehicles can provide. Equation (10) represents the power constraint between electric vehicle reserve, planned power, and maximum charging power, i.e., after executing reserve adjustment power based on planned power, the power of the electric vehicle does not exceed the maximum charging power that can be executed. Equation (11) indicates that the reserve of the electric vehicle is greater than zero. Equation (12) represents the constraint that the reserve should meet after the electric vehicle responds to the reserve, considering energy coupling. Since the reserve is manifested as an increase in charging power, the constraint satisfied by the reserve is that the SOC of the electric vehicle does not exceed the maximum SOC after responding to the reserve, and the time window of the reserve quantification is K. It is necessary to consider the impact of the reserve response at the previous moment on the reserve quantification at the next moment. When quantifying the reserve capacity, it is impossible to predict the response of the reserve. Therefore, this patent considers the worst scenario, i.e., all the reserve at the previous moment are called. Then, the reserve evaluated at the next moment has strong robustness and can effectively support the optimized scheduling of electric vehicles.

[0083] Step 103: Establish an upper reserve optimization model for electric vehicles on multiple time scales based on the off-grid time of the electric vehicle and the electric vehicle's travel energy requirements. The upper reserve of an electric vehicle represents the reduction in charging power of the electric vehicle based on the planned power when the grid frequency is low (low). The electric vehicle responds to the upper reserve by reducing charging power and increasing the system frequency. When the electric vehicle reduces charging power, there is a risk that the amount of electricity cannot meet the electric vehicle's travel energy requirements. Therefore, the electric vehicle's travel energy requirements are considered when quantifying the upper reserve of the electric vehicle. At the same time, to improve the robustness of the reserve capacity calculation, a multi-time-scale reserve capacity quantification model for electric vehicles is established within the optimized time window, assuming that all reserve capacity in the historical period is fully utilized.

[0084] like Figure 3 As shown in the figure, the gray rectangular area enclosed by the black solid line during the period t1-t3 represents the planned power of the electric vehicle, and the upper reserve of the electric vehicle during the period t1-t2 is quantified, that is, the ability of the electric vehicle to reduce its charging power. Figure 3 In the figure, it is represented by a small dark gray rectangle. If the electric vehicle reduces its charging power during t1-t2, in order to ensure that the electric vehicle user's energy demand can be met when the electric vehicle is off-grid, the electric vehicle increases its power consumption during t3-t4 to supplement the energy reduced during t1-t2.

[0085] Based on this, the multi-time-scale upper reserve optimization model of electric vehicles established in this step is:

[0086]

[0087] The constraints corresponding to the upper backup optimization model are:

[0088]

[0089] ΔP i u,max (t)≥0 (15)

[0090]

[0091]

[0092] Where ΔP i u,max (t) is the maximum reserve that electric vehicle i can provide at time t, t now is the current moment, K is the time window for solving the next backup, and κ is the time label index.

[0093] Similar to the quantification of the lower reserve of electric vehicles, the quantification of the upper reserve of electric vehicles is transformed into an optimization problem, where Equation (13) represents the optimization goal of the quantification of the upper reserve of electric vehicles, that is, the maximum upper reserve that all electric vehicles can provide. Equation (14) represents the power constraint between the upper reserve of electric vehicles and the planned power. Since the upper reserve of electric vehicles is the reduced charging power of electric vehicles, the upper reserve of electric vehicles does not exceed the value of the planned power. Equation (15) indicates that the upper reserve of electric vehicles is greater than zero. Equation (17) represents the constraints that the upper reserve should meet after the electric vehicle responds to the upper reserve, considering energy coupling. Since the upper reserve is manifested as a reduction in charging power, the constraint satisfied by the upper reserve is that after responding to the upper reserve, when the electric vehicle unplugs the charging gun and goes off the grid, the SOC of the electric vehicle meets the travel demand of the electric vehicle. Since the travel demand of the electric vehicle can be expressed by SOC, but this will increase the complexity of the constraint, this patent converts the energy constraint satisfied by the upper reserve into the relationship between the upper reserve, planned power and maximum charging power, that is, after the electric vehicle responds to the upper reserve, charging at the maximum charging power in the future period can meet the total energy demand of the electric vehicle, and the total energy demand of the electric vehicle is represented by the original planned power. At the same time, in order to ensure the effectiveness of the upper reserve quantification, the upper reserve of electric vehicles is quantified in the worst scenario, that is, all the upper reserves at the previous moment are called.

[0094] Step 104: Based on the lower and upper reserve optimization models, rolling optimization is used to achieve real-time quantification of reserve capacity. This step primarily establishes a full-time-scale reserve capacity quantification model based on rolling optimization. This model considers the real-time power and reserve capacity utilization of electric vehicles, models the real-time power control process of electric vehicles, and quantifies the upper and lower reserve capacities based on changes in the electric vehicle's SOC, providing a foundation for optimal scheduling of electric vehicles.

[0095] Among them, when quantifying real-time backup capacity, it is necessary to update the information of electric vehicles, use rolling optimization and real-time backup response signals to control the real-time power of electric vehicles, update the SOC information of electric vehicles based on the actual execution power, and at the same time, determine whether new electric vehicles are connected to the power grid. If new electric vehicles are connected, the total number N of electric vehicles is updated at the same time, and the planned power and backup capacity of multiple time windows in the future are further calculated based on the updated SOC information and the time when the electric vehicles are off the grid.

[0096] The real-time power of electric vehicle i in period t can be expressed as:

[0097]

[0098] Where, P i a (t) is the real-time power of electric vehicle i in period t, α d For electric vehicles, α u Backup call signal for electric vehicles.

[0099] The updated expression of electric vehicle SOC is:

[0100]

[0101] The SOC of electric vehicles that are currently connected to the grid is updated. At the same time, it is determined whether there are electric vehicles connected or disconnected, and the electric vehicle cluster is updated. Then, the next time period is entered, and the planned power of electric vehicles is formulated according to formulas (1) to (8). The lower and upper reserves of electric vehicles are quantified according to formulas (9) to (12) and (13) to (17), respectively, to obtain the reserve capacity of multiple time windows. However, in actual response, only the reserve of the current time period is responded to. The real-time power is calculated, the SOC is updated, and the next time period is entered. The above process is repeated until the end of the optimization period.

[0102] Based on the above description, the present invention takes into account the real-time update of electric vehicle information on the basis of traditional reserve capacity quantification, and proposes a robust real-time electric vehicle reserve capacity quantification method. Taking into account the energy demand of electric vehicle users, an electric vehicle planned power optimization model is proposed to provide a basis for the quantification of electric vehicle reserve capacity. The real-time reserve capacity quantification model takes into account the limitations of electric vehicle battery capacity and the lower and upper reserve of electric vehicle power demand quantification respectively. In the reserve capacity quantification model, the worst application scenario is taken into account, which improves the robustness of reserve capacity quantification and ensures that the reserve capacity of electric vehicles in multiple time periods can be effectively called. The quantification of reserve capacity at multiple time scales has a stronger guiding significance for the optimal scheduling of electric vehicle energy consumption. Based on the method of the present invention, the visualization of electric vehicle reserve capacity can be conveniently realized, which has very important application value for scientific research institutions and industrial and commercial circles to realize the participation of electric vehicles in grid interaction technology and the use of electric vehicles in the electricity market.

[0103] In addition, corresponding to the above-mentioned electric vehicle reserve capacity quantification method, the present invention also provides the following implementation structure:

[0104] A system for quantifying the spare capacity of electric vehicles, such as Figure 4 As shown, the system includes:

[0105] The first model building module 400 is used to build an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power.

[0106] The baseline power determination module 401 is configured to generate an electric vehicle power plan based on the electric vehicle charging power optimization model, and use the electric vehicle power plan as the baseline power for quantifying the electric vehicle's spare capacity.

[0107] The second model building module 402 is used to build a multi-time-scale lower reserve optimization model for electric vehicles based on the baseline power and the capacity constraint of the electric vehicle battery.

[0108] The third model building module 403 is used to establish an upper reserve optimization model for electric vehicles at multiple time scales based on the off-grid time of the electric vehicles and the travel energy requirements of the electric vehicles.

[0109] The capacity real-time quantification module 404 is configured to implement real-time quantification of the spare capacity by using rolling optimization based on the lower spare optimization model and the upper spare optimization model.

[0110] An electronic device, comprising:

[0111] The memory is used to store the logic control program.

[0112] The processor is connected to the memory and is used to call and execute the logic control program to implement the above-mentioned electric vehicle spare capacity quantification method.

[0113] The memory may be a computer-readable storage device.

[0114] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0115] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for quantifying the spare capacity of an electric vehicle, characterized in that: include: Construct an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power; generating an electric vehicle power plan based on the electric vehicle charging power optimization model, and using the electric vehicle power plan as a baseline power for quantifying the electric vehicle reserve capacity; Building a multi-time-scale lower reserve optimization model for electric vehicles based on the baseline power and the capacity constraints of the electric vehicle battery; A multi-time-scale upper reserve optimization model for electric vehicles is established based on the off-grid time of electric vehicles and the energy requirements of electric vehicle travel. Based on the lower reserve optimization model and the upper reserve optimization model, using rolling optimization to achieve real-time quantification of reserve capacity; Wherein, the lower standby optimization model is: The capacity constraint of electric vehicle batteries is: Where, is the maximum reserve of electric vehicle i at time t, t now is the current moment, K is the time window, κ is the time label index, P i (t) is the charging power of electric vehicle i at time t, is the maximum charging power of the charging pile, is the maximum charging power of the electric vehicle battery, min(*) takes the minimum value, Δt is a time interval, S max is the maximum state of charge of the electric vehicle, S i (t now ) is the state of charge of electric vehicle i at the current moment, is the rated capacity of the battery of electric vehicle i, and η is the charging efficiency of the electric vehicle; The upper standby optimization model is: Constraints of the above backup optimization model: Where, is the maximum reserve of electric vehicle i at time t, is the pick-up time of electric car i, P max is the maximum charging power.

2. The electric vehicle spare capacity quantification method according to claim 1, characterized in that: The electric vehicle charging power optimization model is: Where i is the number of the electric vehicle, t is the time, Δt is a time interval, P i (t) is the charging power of electric vehicle i at time t, p tou (t) is the charging electricity price at time t, is the time when electric vehicle i is connected to the grid, The pick-up time for the electric car i.

3. The electric vehicle spare capacity quantification method according to claim 1, characterized in that: The method of implementing real-time quantification of the reserve capacity by using rolling optimization based on the lower reserve optimization model and the upper reserve optimization model specifically includes: Based on the lower reserve optimization model and the upper reserve optimization model, the real-time power of the electric vehicle is controlled by using rolling optimization and a real-time reserve response signal; Update the state of charge information of the electric vehicle according to the real-time power obtained by the control, and at the same time, determine whether there is a new electric vehicle connected to the power grid; If new electric vehicles are connected to the power grid, the total number of electric vehicles is updated, and the planned power and backup capacity for multiple future time windows are determined based on the updated state of charge information and the time when the electric vehicles are off the grid.

4. An electric vehicle spare capacity quantification system, characterized in that: include: A first model building module is used to build an electric vehicle charging power optimization model based on electric vehicle operation data, time-of-use electricity prices and electric vehicle charging power; a baseline power determination module, configured to generate an electric vehicle power plan based on the electric vehicle charging power optimization model, and use the electric vehicle power plan as a baseline power for quantifying the electric vehicle's spare capacity; A second model building module is used to build a multi-time-scale lower reserve optimization model of the electric vehicle based on the baseline power and the capacity constraint of the electric vehicle battery; The third model building module is used to establish an upper reserve optimization model for electric vehicles at multiple time scales based on the off-grid time of electric vehicles and the travel energy requirements of electric vehicles; A capacity real-time quantification module, configured to implement real-time quantification of the reserve capacity by using rolling optimization based on the lower reserve optimization model and the upper reserve optimization model; Wherein, the lower standby optimization model is: The capacity constraint of electric vehicle batteries is: Where, is the maximum reserve of electric vehicle i at time t, t now is the current moment, K is the time window, κ is the time label index, P i (t) is the charging power of electric vehicle i at time t, is the maximum charging power of the charging pile, is the maximum charging power of the electric vehicle battery, min(*) takes the minimum value, Δt is a time interval, S max is the maximum state of charge of the electric vehicle, S i (t now ) is the state of charge of electric vehicle i at the current moment, is the rated capacity of the battery of electric vehicle i, and η is the charging efficiency of the electric vehicle; The upper standby optimization model is: Constraints of the above backup optimization model: Where, is the maximum reserve of electric vehicle i at time t, is the pick-up time of electric car i, P max is the maximum charging power.

5. An electronic device, characterized in that: include: A memory for storing a logic control program; A processor is connected to the memory and is used to call and execute the logic control program to implement the electric vehicle spare capacity quantification method according to any one of claims 1 to 4.

6. The electronic device according to claim 5, characterized in that The memory is a computer-readable storage device.

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