Method and system for evaluating capability of EV aggregator participating in electric power climbing auxiliary service

Abstract

The invention provides a method and a system for evaluating the capability of an electric vehicle (EV) aggregator participating in an electric power climbing auxiliary service. The method comprises the following steps: establishing an optimization target model of the EV aggregator participating in the electric power climbing auxiliary service based on the characteristics of the EV aggregator participating in the electric power climbing auxiliary service; checking the risk that the EV aggregator participates in the electric power climbing auxiliary service to obtain a risk cost expected value; and introducing the risk cost expected value as a penalty term into the optimization target model to obtain a final risk aversion decision-making model, and evaluating the capability of the EV aggregator participating in the electric power climbing auxiliary service through the risk aversion decision-making model. According to the method, the uncertainty and the energy consumption demand of the charging behavior of the EV user are considered, an optimization decision model for reporting the charging power and the climbing capacity of the EV aggregator is established, the risks of insufficient charging and insufficient climbing capacity of the user can be well avoided, and the charging plan and the climbing auxiliary service capacity reported to the market are reasonably optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power auxiliary services, and in particular to a method and system for evaluating the ability of EV aggregators to participate in power ramp auxiliary services. Background Art

[0002] The large-scale access of distributed photovoltaics has changed the net load curve characteristics of the power system, and has also posed new challenges to the power system's ability to ramp up, that is, to continuously increase or decrease power in a short period of time (15 minutes to 1 hour). It has also spawned a regulation product called ramping assistance service. EV (electric vehicle) is a high-quality demand-side regulation resource in the power grid, but the dispatchable capacity of a single EV is limited. A large number of EVs at charging stations can be aggregated, and aggregators can act as agents to participate in the market and dispatch, thereby improving the overall responsive capacity of the EV cluster. In order to meet both electricity demand and ramping demand, it is necessary to optimize the dispatch model to constrain the power of the aggregator, and consider and limit market risks, so as to fully tap the dispatchable space of the aggregator. The paper “Random Optimization of Grid Bidding for Vehicle Frequency Regulation Capacity” established a risk constraint model with interest as the opportunity constraint object. The paper “Random Dispatch of Electric Vehicle Clusters Participating in Day-Ahead and Reserve Market Risks Based on Downside Risk Constraint Method” quantified risks from the perspective of transaction risks. The paper “The Impact of Risk Aversion on Procurement of Flexible Demand-Side Resource Reserves from a Perspective” applied the ellipsoid uncertainty set to the random optimization problem, used CVaR to exclude extreme scenarios, and applied the probability distribution obtained based on data analysis theory to the optimization. However, all of them lacked consideration of real-time uncertainty issues. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for evaluating the ability of EV aggregators to participate in power ramp assistance services, aiming to solve the above-mentioned problems in the prior art.

[0004] An embodiment of the present invention provides a method for evaluating the ability of an EV aggregator to participate in a power ramping assistance service, including:

[0005] Based on the characteristics of EV aggregators participating in power ramping assistance services, an optimization target model for EV aggregators participating in power ramping assistance services is established;

[0006] Calculate the risk of EV aggregators participating in power ramping assistance services and obtain the expected value of risk cost; and

[0007] The expected value of risk cost is introduced as a penalty term into the optimization target model to obtain a final risk aversion decision model, and the ability of EV aggregators to participate in power ramping assistance services is evaluated through the risk aversion decision model.

[0008] An embodiment of the present invention provides a system for evaluating the ability of EV aggregators to participate in power ramp assistance services, including:

[0009] An initial target module, used to establish an optimization target model for EV aggregators to participate in the electric ramping assistance service based on the characteristics of EV aggregators participating in the electric ramping assistance service;

[0010] A risk estimation module, used to calculate the risk of EV aggregators participating in the power ramping assistance service and obtain the expected value of risk cost; and

[0011] A capability evaluation module is used to introduce the expected value of the risk cost as a penalty term into the optimization target model to obtain a final risk aversion decision model, and to evaluate the capability of EV aggregators to participate in the power ramping assistance service through the risk aversion decision model.

[0012] An embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned method for evaluating the ability of EV aggregators to participate in electric ramp assistance services are implemented.

[0013] An embodiment of the present invention also provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps of the above-mentioned method for evaluating the ability of EV aggregators to participate in power ramp assistance services are implemented.

[0014] The use of the embodiments of the present invention may include the following beneficial effects: the embodiments of the present invention take into account the uncertainty of EV users' charging behavior and energy demand, and establish an optimization decision model for EV aggregator charging power and climbing capacity reporting, which can better avoid the risks of insufficient user charging and insufficient climbing ability, reasonably optimize the charging plan and the climbing auxiliary service capacity reported to the market, take into account both energy costs and service benefits, and minimize the overall energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 is a flow chart of a method for evaluating the ability of an EV aggregator to participate in a power ramping assistance service according to an embodiment of the present invention;

[0017] Figure 2 is a schematic diagram of operation constraints for EV participation in auxiliary services according to an embodiment of the present invention;

[0018] Figure 3 is a schematic diagram of scene optimization results according to an embodiment of the present invention;

[0019] Figure 4 is a schematic diagram of the sensitivity of the penalty coefficient of an embodiment of the present invention;

[0020] Figure 5 Schematic diagram of a system for evaluating the capability of EV aggregators to participate in electric ramp assistance services according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0022] Method Embodiment

[0023] According to an embodiment of the present invention, a method for evaluating the ability of an EV aggregator to participate in a power ramping assistance service is provided. Figure 1 is a flow chart of a method for evaluating the ability of an EV aggregator to participate in a power ramping assistance service according to an embodiment of the present invention. Figure 1 As shown, the method for evaluating the ability of EV aggregators to participate in the power ramp assistance service according to an embodiment of the present invention specifically includes:

[0024] Step S101, based on the characteristics of EV aggregators participating in the power ramping assistance service, an optimization target model for EV aggregators participating in the power ramping assistance service is established, specifically including:

[0025] According to the operation constraints of the EV aggregator participating in the power ramping auxiliary service, the value interval range of the optimization variable shown in Formula 1 is obtained, and the total amount of uplink and downlink reserve power that can be called by the EV aggregator during the reserve period is obtained through the optimization variable. Based on the total amount of uplink and downlink reserve power that can be called, and considering the risks faced by the EV aggregator when participating in the power ramping auxiliary service and the benefits obtained by participating in the reserve market, the optimization target model of the EV aggregator participating in the power ramping auxiliary service is established through Formulas 2 and 3;

[0026]

[0027]

[0028]

[0029] Among them, P charge,t Indicates the need to purchase power for charging from the energy market, R up,t and R down,t They represent the up-ramp and down-ramp capacities reported to the system, P max is the maximum power of charging and discharging the vehicle battery, u t is the number of vehicles charged during this period, t represents any standby period, Denote the risk cost Ψ and the random variable u t The mapping function between represents the reporting plan of the aggregator in this period, α and β are penalty coefficients, and π ch,t Indicates the charging power price, E de_charge,t represents the power shortage after the end of period t, π R_up,t and π R_down,t Respectively represent the uphill and downhill climbing service prices, E de_up,t Indicates insufficient power for climbing response, C t represents the total cost that the aggregator needs to pay during period t, t span is the time span of a standby period;

[0030] Step S102, calculating the risk of EV aggregators participating in the power ramping assistance service to obtain the expected value of risk cost, specifically including:

[0031] The risk of EV aggregators participating in the power ramping assistance service is calculated through the improved conditional value-at-risk (CVaR) model. Assuming that EV aggregators do not incur risk costs when participating in the power ramping assistance service, the expected value of risk costs is obtained as shown in Formula 4.

[0032]

[0033] Among them, C CVaR represents the expected value of the risk cost incurred in all risk cost scenarios, f t (u t ) represents the random variable u t The probability distribution function of

[0034] Step S103, introducing the expected value of risk cost as a penalty term into the optimization target model to obtain a final risk aversion decision model, and evaluating the ability of EV aggregators to participate in the power ramping assistance service through the risk aversion decision model, specifically including:

[0035] Through the power constraint of formula 1, the expected value of risk cost is introduced into the optimization target model as a penalty term to obtain the final risk aversion decision model shown in formula 5;

[0036] C=min{π ch,t P charge,t t span -π R_up,t R up,t -π R_down,t R down,t +C CVaR (Ω t )}Formula 5;

[0037] Where C represents the minimum energy cost that the aggregator needs to pay during time period t.

[0038] The above technical solution of the embodiment of the present invention is described in detail below in conjunction with the specific situation of the method for evaluating the ability of EV aggregators to participate in the power ramp assistance service in the embodiment of the present invention.

[0039] The embodiment of the present invention takes the provision of spare capacity by electric vehicle (EV) aggregators in the hill climbing assistance service market as the research object, considers the uncertainty of EV users' charging behavior and energy demand, and establishes an optimization decision model for EV aggregators to report charging power and hill climbing capacity. Firstly, considering the risk that the EV aggregator's response to the hill climbing capacity is insufficient after being called or the charging demand of EV users cannot be met, a risk cost assessment method based on conditional risk value is proposed to weigh the benefits and risks of EV aggregators providing spare capacity, and an optimization model with the goal of maximizing benefits is established; secondly, based on simulation examples, the characteristics and advantages of EV aggregators providing hill climbing assistance services in different application scenarios are compared and analyzed, and the effectiveness of the proposed model is verified.

[0040] The embodiment of the present invention enables the aggregator to achieve a balance between risk and benefit by evaluating the risk of ramping or charging error after ramping is called, and then quantifying the risk and adding it to the optimization target. Therefore, the embodiment of the present invention introduces the conditional value at risk (CVaR) theory as a risk indicator, which has been widely used in power market research. By introducing the quantified risk cost as a penalty item into the optimization target, a risk aversion decision model is established. CVaR can be used to quantify and analyze the economic risk when wind power provides backup, and can also be used to explore the backup capacity of demand response resources under uncertainty. Based on the above research, the embodiment of the present invention proposes a risk aversion model for EV aggregators to participate in the energy and ramping auxiliary service market at the same time. The main contributions of the embodiment of the present invention are two-fold: first, a method for describing the uncertainty scenario of EV aggregators participating in ramping is proposed; second, an improved CVaR model is proposed, which calculates the operating risk of the aggregator (insufficient charging power, insufficient backup power) as a conditional risk value cost based on the uncertainty scenario, which is used for reporting backup capacity and formulating charging plans, and analyzes the characteristics and advantages of EV aggregators providing auxiliary service backup through model simulation and comparison of different application scenarios.

[0041] 1. Flexibility modeling of EV aggregators participating in hill climbing assistance services

[0042] 1.1 Charging Contract Mechanism Design

[0043] Based on the minimum and average charging EVs in each period, the aggregator evaluates the charging demand and flexibility of the period, and optimizes the energy and ramp capacity from the perspective of maximizing overall benefits. For each backup period t, the aggregator needs to optimize three variables in the market: P charge,t , R up,t , R down,t , where P charge,t It is necessary to purchase power for charging from the energy market, R up,t and R down,t They are the up-ramp and down-ramp capacities reported to the system (both are positive numbers). From the perspective of the aggregator, the embodiment of the present invention defines the up-ramp as increasing the charging power, i.e., absorbing more power from the grid, and the down-ramp as decreasing the charging power, i.e., absorbing less power from the grid or providing power to the grid. The actual ramp power responded by the aggregator is denoted as P RR,t , then at time point t, the total power P of the EV aggregator interacting with the grid EV,t It can be expressed as the sum of charging power and climbing power:

[0044]

[0045] However, aggregators need to provide ramping to the system and charging power to EV users at the same time. Both sides have the risk of not being able to fully meet demand, so punitive measures are needed:

[0046] (1) Penalty for insufficient charging

[0047] If the vehicle fails to charge when it is off-grid, the aggregator will compensate the owner in proportion to the power shortage. base , the charging price is π ch,t , then from the perspective of the aggregator, the power shortage after the end of period t is E de_charge,t , the penalty to be paid is απ ch,t E de_charge,t , α is the penalty coefficient.

[0048] (2) Penalty for insufficient climbing

[0049] Aggregators may not be able to fully respond to ramping call requirements. When insufficient response occurs, additional penalties will be imposed based on the insufficient power. The up and down ramping service prices are π R_up,t and π R_down,t , the amount of electricity that is insufficient for climbing response is E de_up,t or E de_down,t , the fines to be paid to the system are βπ R_up,t E de_up,t or βπ R_down,t E de_down,t , β is the penalty coefficient.

[0050] 1.2 Characterization of EV Aggregator Flexibility

[0051] When EV participates in ramp climbing, its charging power curve will be the sum of the original planned charging power and the ramp climbing power. Considering that the physical constraints of EV battery scheduling mainly include power constraints and power constraints, where the power constraints also include the power that users need to charge, the power and power constraints of EV in a standby period are as follows: Figure 2 As shown;

[0052] In order to meet the risk assessment needs when reporting capacity, the embodiment of the present invention calculates the operating constraints of the EV aggregator as the total power margin for each backup period. In any backup period t, considering the operating constraints of the vehicle battery itself, P charge,t , R up,t and R down,t The possible value range of is:

[0053]

[0054] Among them, P max is the maximum power of charging and discharging the vehicle battery, u tThe number of vehicles charged during this period.

[0055] According to the battery operation constraints, the total amount of uplink and downlink backup power that the aggregator can call in a period of time (denoted as S a_up,t and S a_down,t ), the battery should not be overcharged, nor should it be lower than the guaranteed power of each EV. The available power can be determined by optimizing the variable P charge,t , R up,t , R down,t It turns out that:

[0056]

[0057] Among them, t span It is the time span of a standby period. In market practice, the length of a ramp trading period is 15 minutes. initial,k is the initial charge of vehicle k when it enters the grid, S max It is the maximum amount of electricity that can be charged into the vehicle.

[0058] 2. Risk aversion optimization model and solution

[0059] 2.1 Risk Cost Accounting

[0060] In each backup period, the aggregator faces two risks: insufficient ramp response and insufficient charging. up,t and R down,t When the ramp service is called, the aggregator will evenly increase or decrease the charging power R in the corresponding period. up,t or R down,t At the end of each period, the change in power due to ramping is R up,t t span / 2 or R down,t t span / 2, then the aggregator is either unable to respond to the up-ramp due to full charge, or insufficient charge due to responding to the down-ramp.

[0061]

[0062] Considering the above two risks and the benefits of participating in the backup market, the total cost C that the aggregator needs to pay in time period t is t :

[0063]

[0064] In the formula, π ch,t , π R_up,t and π R_down,t They are the charging power price in the energy market, the uplink capacity price and the downlink capacity price in the backup market, and the function is the risk cost Ψ and the random variable u t The mapping function between is the reporting plan of the aggregator in this period, and α and β are penalty coefficients.

[0065] 2.2 Definition and calculation of CVaR

[0066] The risk cost of formula (6) can only be calculated under given variables. The embodiment of the present invention calculates the CVaR in this scenario based on the concept of CVaR.

[0067] First, define the random variable u t The probability distribution of t (u t ), for any given risk cost μ, the loss in this period The probability of not being greater than μ is have:

[0068]

[0069] CVaR is defined as the expected value of risk cost for all scenarios where the risk cost is greater than μ for a given μ. In the model of the embodiment of the present invention, since the aggregator still has a considerable probability of not incurring risk cost, μ is defined as 0. Then the CVaR of the embodiment of the present invention is defined as the expected value of risk cost generated in all scenarios where risk cost occurs (denoted as C CVaR ), the equation is:

[0070]

[0071] 2.3 Optimization Model

[0072] Finally, the optimization problem of charging arrangement and reporting of spare capacity by the aggregator in time period t is obtained:

[0073]

[0074] Where C represents the minimum energy cost that the aggregator needs to pay during time period t.

[0075] Under the power constraint of formula (2), the optimization problem will weigh the penalty cost of not meeting the charging demand and backup demand and the total benefit to maximize the benefit.

[0076] 3. Example Analysis

[0077] The simulation results of the optimization models will be compared and displayed below. Figure 3 Taking a certain area (450 vehicles, closing time is 1 hour before operation) as an example, the optimization results under risk aversion decision-making are demonstrated.

[0078] Aggregators will provide up-ramps by increasing the charging capacity. It can be seen that the up-ramps provided by aggregators are generally higher than down-ramps. This is because the battery can provide up-ramps while also meeting charging needs. It is a more economical choice. It also shows that EV aggregators have a stronger ability to provide up-ramps.

[0079] Since the current market mechanism has not yet formed a relatively unified punishment system, Figure 4 The influence of different values ​​of penalty coefficients α and β on the optimization results is given. Among them, (a) is the average charging power purchased throughout the day under different α, (b) and (c) are the average upward and downward climbing capacities of the day under different β. It can be seen from the y-axis that α has almost no effect on the charging power, and the larger the β, the smaller the reported climbing capacity. It can be seen that the risk of insufficient climbing always exists, but the overall impact is small. It shows that under the flexible characterization method of the embodiment of the present invention, the situation of insufficient charging can be almost avoided, and the situation of insufficient climbing will occur but the overall impact is also small.

[0080] In summary, the embodiments of the present invention address the issue of EV aggregators participating in the energy market and the ramp assistance service market, propose a risk-averse decision-making model based on flexibility characterization, provide a flexibility characterization method for EV aggregators facing ramp assistance services, establish an optimization method that considers market uncertainty risks, and verify the effectiveness of the proposed model in ramp capacity optimization through simulation, and compare the operating results under different scenarios.

[0081] From the simulation results, after participating in the hill climbing assist service, the optimization model proposed in the embodiment of the present invention can better avoid the risk of insufficient EV charging, but the risk of insufficient climbing ability still exists, and its risk cost can be reduced through optimization to minimize the overall energy cost; in addition, the power consumption characteristics of EV determine that its ability to provide upward climbing is always higher than that of downward climbing.

[0082] System Example

[0083] According to an embodiment of the present invention, a system for evaluating the ability of EV aggregators to participate in electric ramp assistance services is provided. Figure 5 is a schematic diagram of a capability evaluation system for EV aggregators participating in power ramp assistance services according to an embodiment of the present invention. Figure 5 As shown, the capability evaluation system for EV aggregators to participate in the electric ramp assistance service according to an embodiment of the present invention specifically includes:

[0084] The initial target module 50 is used to establish an optimization target model for EV aggregators to participate in the electric ramp assist service based on the characteristics of EV aggregators participating in the electric ramp assist service, specifically for:

[0085] According to the operation constraints of the EV aggregator participating in the power ramping auxiliary service, the value interval range of the optimization variable shown in Formula 1 is obtained, and the total amount of uplink and downlink reserve power that can be called by the EV aggregator during the reserve period is obtained through the optimization variable. Based on the total amount of uplink and downlink reserve power that can be called, and considering the risks faced by the EV aggregator when participating in the power ramping auxiliary service and the benefits obtained by participating in the reserve market, the optimization target model of the EV aggregator participating in the power ramping auxiliary service is established through Formulas 2 and 3;

[0086]

[0087] Among them, P charge,t Indicates the need to purchase power for charging from the energy market, R up,t and R down,t They represent the up-ramp and down-ramp capacities reported to the system, P max is the maximum power of charging and discharging the vehicle battery, u t is the number of vehicles charged during this period, t represents any standby period, Denote the risk cost Ψ and the random variable u t The mapping function between represents the reporting plan of the aggregator in this period, α and β are penalty coefficients, and π ch,t Indicates the charging power price, E de_charge,t represents the power shortage after the end of period t, π R_up,t and π R_down,t Respectively represent the uphill and downhill climbing service prices, E de_up,t Indicates insufficient power for climbing response, C t represents the total cost that the aggregator needs to pay during period t, t span is the time span of a standby period;

[0088] The risk estimation module 52 is used to calculate the risk of EV aggregators participating in the power ramping assistance service and obtain the expected value of risk cost, which is specifically used for:

[0089] The risk of EV aggregators participating in the power ramping assistance service is calculated through the improved conditional value-at-risk (CVaR) model. Assuming that EV aggregators do not incur risk costs when participating in the power ramping assistance service, the expected value of risk costs is obtained as shown in Formula 4.

[0090]

[0091] Among them, C CVaR represents the expected value of the risk cost incurred in all risk cost scenarios, f t (u t ) represents the random variable u t The probability distribution function of

[0092] The capability evaluation module 54 is used to introduce the expected value of risk cost as a penalty term into the optimization target model to obtain a final risk aversion decision model, and evaluate the capability of EV aggregators to participate in the power ramping assistance service through the risk aversion decision model, specifically for:

[0093] Through the power constraint of formula 1, the expected value of risk cost is introduced into the optimization target model as a penalty term to obtain the final risk aversion decision model shown in formula 5;

[0094]

[0095] Where C represents the minimum energy cost that the aggregator needs to pay during time period t.

[0096] The embodiment of the present invention is a system embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0097] In summary, the embodiment of the present invention is aimed at the problem of electric vehicles participating in the energy market and the hill climbing assistance service market at the same time. First, a flexibility characterization method for electric vehicle aggregators providing hill climbing assistance services is given, and then the risk of insufficient hill climbing capacity or failure to meet the user's charging needs after the aggregator is called is considered. A risk cost assessment method based on conditional risk value is proposed to weigh the benefits and risks of EV aggregators providing backup, and then establish an optimization model with the goal of maximizing benefits. The embodiment of the present invention can better avoid the risks of insufficient user charging and insufficient hill climbing capacity, reasonably optimize the charging plan and the hill climbing assistance service capacity reported to the market, take into account energy costs and service benefits, and minimize the overall energy cost.

[0098] Device Example 1

[0099] An embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps described in the method embodiment when executed by the processor.

[0100] Device Example 2

[0101] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps described in the method embodiment are implemented.

[0102] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the ability of EV aggregators to participate in power ramping assistance services, characterized in that include: Based on the characteristics of EV aggregators participating in power ramping assistance services, an optimization target model for EV aggregators participating in power ramping assistance services is established; Calculate the risk of EV aggregators participating in power ramping assistance services and obtain the expected value of risk cost; as well as The expected value of risk cost is introduced as a penalty term into the optimization target model to obtain a final risk aversion decision model, and the ability of EV aggregators to participate in power ramping assistance services is evaluated through the risk aversion decision model.

2. The method according to claim 1, characterized in that Based on the characteristics of EV aggregators participating in power ramping assistance services, an optimization target model for EV aggregators to participate in power ramping assistance services is established, which specifically includes: According to the operation constraints of the EV aggregator participating in the power ramping auxiliary service, the value interval range of the optimization variable shown in Formula 1 is obtained, and the total amount of uplink and downlink reserve power that can be called by the EV aggregator during the reserve period is obtained through the optimization variable. Based on the total amount of uplink and downlink reserve power that can be called, and considering the risks faced by the EV aggregator when participating in the power ramping auxiliary service and the benefits obtained by participating in the reserve market, the optimization target model of the EV aggregator participating in the power ramping auxiliary service is established through Formulas 2 and 3; Among them, P charge,t Indicates the need to purchase power for charging from the energy market, R up,t and R down,t They represent the up-ramp and down-ramp capacities reported to the system, P max is the maximum power of charging and discharging the vehicle battery, u t is the number of vehicles charged during this period, t represents any standby period, Ψ| Ωt (u t ) represents the risk cost Ψ and the random variable u t The mapping function between t =[P charge,t ,R up,t ,R down,t ] represents the reporting plan of the aggregator in this period, α and β are penalty coefficients, and π ch,t Indicates the charging power price, E de_charge,t represents the power shortage after the end of period t, π R_up,t and π R_down,t Respectively represent the uphill and downhill climbing service prices, E de_up,t Indicates insufficient power for climbing response, C t represents the total cost that the aggregator needs to pay during period t, t span It is the time span of a backup period.

3. The method according to claim 2, characterized in that The risk of EV aggregators participating in the power ramping assistance service is calculated, and the expected value of risk cost is obtained, which includes: The risk of EV aggregators participating in the power ramping assistance service is calculated through the improved conditional value-at-risk (CVaR) model. Assuming that EV aggregators do not incur risk costs when participating in the power ramping assistance service, the expected value of risk costs is obtained as shown in Formula 4. Among them, C CVaR represents the expected value of the risk cost incurred in all risk cost scenarios, f t (u t ) represents the random variable u t The probability distribution function of .

4. The method according to claim 3, characterized in that The expected value of risk cost is introduced into the optimization target model as a penalty term to obtain the final risk aversion decision model, which specifically includes: Through the power constraint of formula 1, the expected value of risk cost is introduced into the optimization target model as a penalty term to obtain the final risk aversion decision model shown in formula 5; C = min{π ch,t P charge,t t span -π R_up,t R up,t -π R_down,t R down,t +C CVaR (Ω t )} Formula 5; Where C represents the minimum energy cost that the aggregator needs to pay during time period t.

5. A capability assessment system for EV aggregators to participate in electric ramp assistance services, characterized in that include: An initial target module, used to establish an optimization target model for EV aggregators to participate in the electric ramping assistance service based on the characteristics of EV aggregators participating in the electric ramping assistance service; The risk estimation module is used to calculate the risk of EV aggregators participating in the power ramping assistance service and obtain the expected value of risk cost; as well as A capability evaluation module is used to introduce the expected value of the risk cost as a penalty term into the optimization target model to obtain a final risk aversion decision model, and to evaluate the capability of EV aggregators to participate in the power ramping assistance service through the risk aversion decision model.

6. The system according to claim 5, characterized in that The initial target module is specifically used for: According to the operation constraints of the EV aggregator participating in the power ramping auxiliary service, the value interval range of the optimization variable shown in Formula 1 is obtained, and the total amount of uplink and downlink reserve power that can be called by the EV aggregator during the reserve period is obtained through the optimization variable. Based on the total amount of uplink and downlink reserve power that can be called, and considering the risks faced by the EV aggregator when participating in the power ramping auxiliary service and the benefits obtained by participating in the reserve market, the optimization target model of the EV aggregator participating in the power ramping auxiliary service is established through Formulas 2 and 3; Among them, P charge,t Indicates the need to purchase power for charging from the energy market, R up,t and R down,t They represent the up-ramp and down-ramp capacities reported to the system, P max is the maximum power of charging and discharging the vehicle battery, u t is the number of vehicles charged during this period, t represents any standby period, Denote the risk cost Ψ and the random variable u t The mapping function between represents the reporting plan of the aggregator in this period, α and β are penalty coefficients, and π ch,t Indicates the charging power price, E de_charge,t represents the power shortage after the end of period t, π R_up,t and π R_down,t Respectively represent the uphill and downhill climbing service prices, E de_up,t Indicates insufficient power for climbing response, C t represents the total cost that the aggregator needs to pay during period t, t span It is the time span of a backup period.

7. The system according to claim 6, characterized in that The risk estimation module is specifically used for: The risk of EV aggregators participating in the power ramping assistance service is calculated through the improved conditional value-at-risk (CVaR) model. Assuming that EV aggregators do not incur risk costs when participating in the power ramping assistance service, the expected value of risk costs is obtained as shown in Formula 4. Among them, C CVaR represents the expected value of the risk cost incurred in all risk cost scenarios, f t (u t ) represents the random variable u t The probability distribution function of .

8. The system according to claim 7, characterized in that The capability assessment module is specifically used for: Through the power constraint of formula 1, the expected value of risk cost is introduced into the optimization target model as a penalty term to obtain the final risk aversion decision model shown in formula 5; C = min{π ch,t P charge,t t span -π R_up,t R up,t -π R_down,t R down,t +C CVaR (Ω t )} Formula 5; Where C represents the minimum energy cost that the aggregator needs to pay during time period t.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method for evaluating the ability of an EV aggregator to participate in an electric ramping assistance service as described in any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of the method for evaluating the ability of an EV aggregator to participate in the power ramp assistance service as described in any one of claims 1 to 4 are implemented.

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