Considering the strong randomness of EV, the V2G aggregator needs to optimize the daily scheduling of EVs participating in peak shaving demand response

Abstract

The application provides a V2G aggregator's day-to-day optimization scheduling system for EVs participating in peak shaving demand response, which considers strong randomness of EVs, introduces three indexes of EV historical credit, capacity factor and pre-reservation rate, quantifies individual performance of EVs participating in peak shaving demand response, considers EV charging and discharging equality constraints, EV charging and discharging power constraints, battery safety constraints, travel constraints, supply and demand constraints and emergency response constraints, and obtains an optimal scheduling plan of the V2G aggregator for the EVs.

Description

Technical Field

[0001] This invention belongs to the fields of power systems and electric vehicle technology, and specifically relates to a V2G aggregator intraday optimization scheduling system for EV participation in peak shaving demand, taking into account the strong randomness of EVs. Background Technology

[0002] Electric vehicles (EVs), as a new type of load, can effectively reduce the economic costs associated with traditional power plants increasing their reserve capacity to meet grid peak-shaving demands, and the grid needing to invest more in energy storage and other peak-shaving demand response facilities. EVs, as flexible demand-side resources, participate in demand response regulation, which can improve the revenue of V2G aggregators and EVs. While meeting their own charging needs, they participate in the response in an aggregated manner, enhancing the regulation capability of the response system. Previous studies on EV participation in grid demand response have mostly focused on single time scales, such as day-ahead or intraday. However, for EVs with strong randomness, day-ahead demand response strategies cannot guarantee the execution of the response plan by EVs the following day. Intraday single-scale demand response optimization scheduling, due to its short regulation time, provides insufficient preparation time for EVs, affecting actual charging station response and easily leading to unsatisfactory peak-shaving demand response results. The risk of EV default during the intraday demand response phase on the user side still exists.

[0003] Meanwhile, as the scale of EVs continues to expand, if EVs continue to participate in grid demand response directly as individuals, it will place enormous pressure on the grid, given the limited computing power of the dispatch center. The grid will struggle to distribute instructions to each responding entity, and it will be uncertain whether the entity will even receive the response. Therefore, using V2G aggregators or charging stations as agents to aggregate and dispatch EVs, provide information integration and transmission, and achieve zoned control, coupled with certain market guidance or incentive policies, can significantly optimize dispatch efficiency. Summary of the Invention

[0004] To address this, this invention considers the uncertainty of intraday EVs and the reliability requirements of V2G aggregators' responses. It introduces three indicators—pre-fulfillment rate, historical integrity, and capacity factor—as the basis for V2G aggregators to optimize EV scheduling. To solve the problem of scarce initial transaction data, a dynamic quantitative evaluation method is adopted in the intraday optimized scheduling model to improve EV participation. This invention also considers the strong randomness of EVs and the intraday optimized scheduling system method for V2G aggregators' response to EV participation in peak shaving demand. It implements intraday default emergency scheduling for large-scale EVs and establishes an intraday EV optimized scheduling model with the optimal comprehensive indicator of integrity, capacity, and pre-fulfillment rate. This improves the reliability of V2G aggregator responses and reduces the uncertainty risk brought by EV randomness to the grid's peak shaving demand response.

[0005] By introducing three indicators—EV historical integrity, capacity factor, and pre-fulfillment rate—the performance of individual EVs participating in peak demand response is quantified. Considering the constraints of EV charge-discharge equation, EV charge-discharge power, battery safety, travel, supply and demand, and emergency response, the optimal scheduling plan for EVs by V2G aggregators is obtained.

[0006] To ensure the reliability of V2G aggregators' responses, reduce default costs, and improve work efficiency, a dynamic quantitative value of historical integrity, including direct trust and recommended trust of EVs, is introduced into the intraday optimized scheduling model. This includes three indicators: the pre-honor rate reflecting the intraday response behavior of EVs and their contribution to the completion of the day-ahead scheduling plan; and a capacity factor to address the increased operating costs brought about by the expansion of EV scale. A demand allocation model is established to maximize user participation in peak-shaving demand response, incorporating integrity, capacity, and pre-honor rate metrics. This improves the reliability of peak-shaving demand response for both EVs and V2G aggregators while maintaining the stability of the peak-shaving demand response market.

[0007] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0008] A V2G aggregator-based intraday optimized scheduling system for EV participation in peak shaving demand, considering the strong randomness of EVs, is characterized by:

[0009] Based on computer and internet systems, and considering the uncertainty of intraday EVs and the response reliability requirements of V2G aggregators, three indicators are introduced: EV historical integrity, capacity factor, and pre-fulfillment rate, as the basis for V2G aggregators to optimize EV scheduling.

[0010] To address the initial shortage of transaction data, a dynamic quantitative evaluation method is adopted in the intraday optimized scheduling model. Considering the strong randomness of EVs, the intraday optimized scheduling system for V2G aggregators responding to EV participation in peak shaving demand implements intraday default emergency scheduling for large-scale EVs. The intraday EV optimized scheduling model with the best comprehensive index of integrity, capacity, and pre-compliance rate is adopted to improve the reliability of V2G aggregator response and reduce the uncertainty risk brought by EV randomness to the grid's peak shaving demand response.

[0011] Furthermore, the method for calculating the three performance indicators of each EV—pre-fulfillment rate, historical integrity, and capacity factor—includes the following steps:

[0012] Step S31: Calculate the EV pre-conservation rate;

[0013] The purpose of establishing a pre-emptive compliance rate as a reference standard for dispatching EVs is to guide EV users to submit their daily declarations based on the previously awarded bid volume, thereby reducing the additional costs for aggregators to initiate emergency responses to make up for peak-shaving shortfalls. The pre-emptive compliance rate is defined as the ratio of the EV's declared power after the intraday emergency response mechanism is activated to the previously awarded bid power. To incentivize EVs to participate in emergency responses, the pre-emptive compliance rate for participating EVs is set to 1, as shown in the following formula:

[0014]

[0015] In the formula: The pre-contract fulfillment rate of electric vehicle i in V2G aggregator j during the t-hour period of the day; The power of electric vehicles reported for charging and discharging within i days by V2G aggregator j after the emergency mechanism is activated; This refers to the charging and discharging peak-shaving demand response power that electric vehicle i recently won in the bid for V2G aggregator j;

[0016] Step S32: Calculate EV's historical integrity score;

[0017] Direct trust is defined as the average ratio of actual scheduling commands issued by V2G aggregators to actual EV response volume over the past month, calculated directly based on transaction records between the two parties, as shown in the following formula:

[0018]

[0019] In the formula: R ij,its The direct trust level of electric vehicle i in V2G aggregator j; These represent the number of electric vehicles i that were successfully bid for by V2G aggregator j during time period t within the past month; The actual response power of electric vehicle i in the aggregated supplier j within a historical month during time period t; T e The total number of time periods in which electric vehicles (i) participated in V2G aggregator j's peak demand response within a historical month;

[0020] When the initial number of transactions between electric vehicle i and V2G aggregator j is less than the prescribed number, the historical trust level is determined by the direct trust level R. ij,its With recommendation trust level R ij,rec Together they constitute a whole; when the number of direct transactions between electric vehicle i and V2G aggregator j exceeds the prescribed number, it means that both parties have sufficient historical transaction data to assess the trust level of historical transactions. The reference value of the recommended trust level is negligible. The historical trust level is shown in the following formula:

[0021]

[0022] In the formula: The historical credibility of electric vehicle i among V2G aggregators j during the intraday t period; R ij,rec Let k be the recommendation trust level of electric vehicle i in V2G aggregator j; k and m are the number of direct transactions between electric vehicle i and V2G aggregator j within one month and the prescribed number of transactions, respectively.

[0023] Step S33: Calculate the EV capacity factor;

[0024] The capacity factor is defined as the ratio of the declared power of an EV to the average of the total declared power of all EVs.

[0025]

[0026] In the formula: The capacity factor of electric vehicle i in V2G aggregator j during the intraday time period t; The total number of EV applications submitted by V2G aggregators for intraday optimized scheduling after the V2G aggregator activates the emergency response mechanism during time period t.

[0027] Furthermore, based on the latest application information and considering the risks brought about by temporary EV scheduling, V2G aggregators will conduct intraday scheduling of the invited EV groups for the optimal response period t, taking into account the comprehensive indicators of EV cluster integrity, capacity, and pre-reservation rate.

[0028] Furthermore, the individual performance of EVs participating in peak demand response is quantified, taking into account EV charge-discharge equation constraints, EV charge-discharge power constraints, battery safety constraints, travel constraints, supply and demand constraints, and emergency response constraints, in order to obtain the optimal scheduling plan for EVs by V2G aggregators.

[0029] Furthermore, the construction process of the intraday EV optimization scheduling model with the optimal comprehensive index of integrity, capacity, and pre-fulfillment rate includes the following steps:

[0030] Step S41: Establish the objective function of the intraday optimization scheduling model for V2G aggregators;

[0031] EV cluster scheduling is optimized by using the best comprehensive index of integrity, capacity, and pre-reservation rate.

[0032]

[0033] In the formula: is the total value of the comprehensive index of integrity, capacity, and pre-reservation rate of the EV clusters participating in peak demand response in V2G aggregator j; T is the number of time periods throughout the day. ω is a flag indicating whether electric vehicle i in V2G aggregator j won the bid during time period t, where 1 indicates a successful bid and 0 indicates a failed bid; 1,in ~ω 3,in These are the weighting coefficients for the intraday scheduling model, set according to different priorities in scheduling.

[0034] Step S42: Establish constraints for the intraday optimization scheduling model. The constraints include EV charge / discharge equation constraints, EV charge / discharge power constraints, battery safety constraints, travel constraints, supply and demand constraints, and emergency response constraints.

[0035]

[0036] In the formula: η c η d These are the charging and discharging efficiencies of EVs, respectively. The battery charge of electric vehicle i under V2G aggregator j during time period t; The upper and lower limits of battery safety constraints for electric vehicle i under V2G aggregator j during time period t; The rated battery capacity of electric vehicle i in V2G aggregator j; The minimum amount of electricity required for electric vehicle i under V2G aggregator j to meet travel demand during time period t; These represent the winning bids of aggregator j during the daytime period t. The peak-shaving demand response power of electric vehicle i in the V2G aggregator j during the time period t is the power of the V2G aggregator winning the bid. The supply and demand constraint inequality means that the power of the V2G aggregator winning the bid shall not be higher than the dispatchable power declared by the EV.

[0037] Specifically, when an EV accepts an emergency mechanism invitation, EVs that have already accepted the invitation should charge and discharge at a power not exceeding the maximum limit to ensure that the overall EVs do not default. If this still cannot meet the peak-shaving demand response shortfall, an emergency invitation will be sent to other idle EVs within the entire V2G aggregator's network. When scheduling the invited EVs, the V2G aggregator should consider whether there are subsequent invitation periods and battery safety constraints. That is, the emergency response constraints are:

[0038]

[0039] In the formula, The total winning bid charging and discharging power of electric vehicle i in V2G aggregator j for all invited time periods after time period t; Let be the set of all invited time periods for electric vehicle i in V2G aggregator j after time period t. This refers to the k-th time period within the set.

[0040] Compared to existing technologies, this invention and its preferred solutions consider historical and current indicators of EV participation in the peak demand response market. It comprehensively quantifies EV response performance from three aspects: historical integrity, pre-fulfillment rate, and capacity factor, establishing an intraday optimized scheduling model. Historical integrity and pre-fulfillment rate help guide EV response compliance behavior, reduce the uncertainty risk brought by strong randomness, and improve the response reliability of EVs and V2G aggregators. The capacity factor can reduce the probability of EV cluster defaults caused by too many scheduled individuals for V2G aggregators, improving the peak demand response quality of V2G aggregators. Attached Figure Description

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0042] Figure 1 This is a schematic diagram of the comprehensive index values ​​of EV integrity, capacity, and pre-fulfillment rate within a 21-day period in an embodiment of the present invention. Detailed Implementation

[0043] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0044] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0045] In a specific embodiment, the scheduling method generated by the system provided by the present invention can be considered to include the following steps:

[0046] Step S1: Based on the peak-shaving bidding results reported by the power grid, the V2G aggregator pre-dispatchs the response power of the invited EVs within its jurisdiction and sends a notification to the EVs through the e-platform, which is then confirmed by both parties.

[0047] Step S2: Based on the EV feedback information received 30 minutes before the response deadline, the V2G aggregator activates the default emergency mechanism as needed, and the EV submits an emergency declaration.

[0048] Step S3: Calculate the three performance indicators for each EV: pre-fulfillment rate, historical integrity, and capacity factor;

[0049] Step S4: Establish an intraday optimized scheduling model for V2G aggregators to respond to EV participation demand. Based on the latest application information and considering the risks of temporary EV scheduling, V2G aggregators will schedule the invited EV groups for intraday response time period t based on the optimal comprehensive index of EV cluster integrity, capacity, and pre-reservation rate. In principle, once the intraday response EV is confirmed, it will not be canceled, and EVs should arrive at the charging station 5-15 minutes in advance to wait for the response.

[0050] Step S3 specifically includes:

[0051] Step S31: Calculate the EV pre-contractual rate.

[0052] The purpose of establishing a pre-emptive compliance rate as a reference standard for dispatching EVs is to guide EV users to submit their daily bids based on the previously awarded volume, thereby reducing the additional costs for aggregators to initiate emergency responses to make up for peak-shaving shortfalls. The pre-emptive compliance rate is defined as the ratio of the EV's declared power after the emergency response mechanism is activated to the previously awarded power. To incentivize EVs to participate in emergency response, the pre-emptive compliance rate for participating EVs is set to 1, as shown in the following formula:

[0053]

[0054] In the formula: The pre-contract fulfillment rate of electric vehicle i in V2G aggregator j during the t-hour period of the day; The power of electric vehicles reported for charging and discharging within i days by V2G aggregator j after the emergency mechanism is activated; This refers to the charging and discharging peak-shaving demand response power of electric vehicles that were recently awarded to V2G aggregator j.

[0055] Step S32: Calculate EV's historical integrity score.

[0056] Direct trust is defined as the average ratio of actual dispatch commands issued by V2G aggregators to actual EV response volume over the past month, calculated directly based on the transaction records of both parties, as follows:

[0057]

[0058] In the formula: R ij,its The direct trust level of electric vehicle i in V2G aggregator j; These represent the number of electric vehicles i that were successfully bid for by V2G aggregator j during time period t within the past month; The actual response power of electric vehicle i in the aggregated supplier j within a historical month during time period t; T e This represents the total number of time periods within a historical month during which electric vehicles (i) participated in V2G aggregator j's peak-shaving demand response.

[0059] When the initial number of transactions between electric vehicle i and V2G aggregator j is less than the prescribed number, the historical trust level is determined by the direct trust level R. ij,its With recommendation trust level R ij,rec Together they constitute a whole; when the number of direct transactions between electric vehicle i and V2G aggregator j exceeds the prescribed number, it means that both parties have sufficient historical transaction data to assess the trust level of historical transactions, and the reference value of the recommended trust level can be ignored. The historical trust level is shown in the following formula:

[0060]

[0061] In the formula: The historical credibility of electric vehicle i among V2G aggregators j during the intraday t period; R ij，rec denoted as the recommendation trust level of electric vehicle i in V2G aggregator j; k and m are the number of direct transactions between electric vehicle i and V2G aggregator j within one month and the specified number of transactions, respectively, with m ranging from 15 to 25.

[0062] Step S33: Calculate the EV capacity factor.

[0063] Given the continuous expansion of the EV market, the workload for V2G aggregators in scheduling individual EVs will increase significantly. To improve efficiency and reduce their own operating costs, V2G aggregators will prioritize EVs with higher dispatchable power. The capacity factor is defined as the ratio of the declared power of an EV to the average of the total declared power of all EVs.

[0064]

[0065] In the formula: The capacity factor of electric vehicle i in V2G aggregator j during the intraday time period t; The total number of EV applications submitted by V2G aggregators for intraday optimized scheduling after the V2G aggregator activates the emergency response mechanism during time period t.

[0066] Step S4 specifically includes:

[0067] Step S41: Establish the objective function of the intraday optimization scheduling model for V2G aggregators.

[0068] To ensure credibility, reduce default costs, and improve work efficiency, the optimal scheduling of EVs is achieved by using a comprehensive index of EV cluster integrity, capacity, and pre-reservation rate.

[0069]

[0070] In the formula: is the total value of the comprehensive index of integrity, capacity, and pre-reservation rate of the EV clusters participating in peak demand response in V2G aggregator j; T is the number of time periods throughout the day. ω is a flag indicating whether electric vehicle i in V2G aggregator j won the bid during time period t; 1 indicates winning the bid, and 0 indicates not winning. 1,in ~ω 3,in These are the weighting coefficients for the intraday scheduling model, set according to different priorities in scheduling.

[0071] Step S42: Establish constraints for the intraday optimization scheduling model. The constraints include EV charge / discharge equation constraints, EV charge / discharge power constraints, battery safety constraints, travel constraints, supply and demand constraints, and emergency response constraints.

[0072]

[0073] In the formula: η c η d These are the charging and discharging efficiencies of EVs, respectively. The battery charge of electric vehicle i under V2G aggregator j during time period t; The upper and lower limits of battery safety constraints for electric vehicle i under V2G aggregator j during time period t; The rated battery capacity of electric vehicle i in V2G aggregator j; The minimum amount of electricity required for electric vehicle i under V2G aggregator j to meet travel demand during time period t; These represent the winning bids of aggregator j during the daytime period t. The peak-shaving demand response power of electric vehicle i in the V2G aggregator j during the time period t is the power of the V2G aggregator winning the bid. The supply and demand constraint inequality means that the power of the V2G aggregator winning the bid shall not be higher than the power of the EV that is declared as dispatchable.

[0074] Specifically, when an EV accepts an emergency mechanism invitation, EVs that have already accepted the invitation should charge and discharge at a power not exceeding the maximum limit to ensure that the overall EVs do not default. If the peak-shaving demand response shortfall still cannot be met, an emergency invitation will be sent to other idle EVs within the entire V2G aggregator's network. When scheduling the invited EVs, the V2G aggregator should consider whether there are subsequent invitation periods and battery safety constraints. The emergency response constraints are as follows:

[0075]

[0076] In the formula, The total winning bid charging and discharging power of electric vehicle i in V2G aggregator j for all invited time periods after time period t; Let be the set of all invited time periods for electric vehicle i in V2G aggregator j after time period t. This refers to the k-th time period within the set.

[0077] The following is a specific example of the present invention.

[0078] A certain region has a V2G aggregator with 80 electric vehicles. The charging power for slow charging of these EVs is uniformly distributed between 3 and 4 kW, while the charging power for fast charging is uniformly distributed between 40 and 60 kW. EV parameters are shown in Table 1, and the battery capacity safety range is 0.1-0.9 SOC. Historical integrity is considered in the intraday scheduling model. Capacity factor and the rate of early compliance The weighting coefficients are 0.4, 0.3, and 0.3 respectively. Taking time period 21 of the day as an example, Tables 2 and 3 show the historical integrity of EVs participating in peak demand response during time period 21, and Table 4 shows the bidding status of charging and discharging power before time period 21.

[0079] Table 1 EV Parameters

[0080]

[0081] Table 2 Historical Integrity of EVs Participating in Response During Time Period 21

[0082]

[0083]

[0084] Continued from Table 3

[0085] 0.8393 0.8788 1 0.7361 0.7277 1 1 0.5600 0.9600 0.6600 0.5201

[0086] Table 3: Historical Integrity of Emergency EV Applications (within the day) for Time Period 21

[0087] Historical integrity 1 0.4400 0.6601 0.8000 0.9510 0.5400 0.6100 0.7700 0.3560

[0088] Table 4. EV Bidding Volume within 21 Days of the Period

[0089] 1 4 9 3.49 2 3.95 10 3.65 3 3 11 4 4 4 12 3.86 5 4 13 3 6 3.87 14 3.35 7 3.75 15 3.45 8 4 16 3.19

[0090] In order to compare the V2G aggregator response reliability with the intraday optimized scheduling method for EV participation in peak shaving demand considering the strong randomness of EVs in this invention (Scenario 2), this invention constructs an optimized scheduling method with the highest comprehensive charging and discharging efficiency (Scenario 1).

[0091] Scenario 1: Taking into account factors such as battery state of charge level and EV travel, V2G aggregators use minimizing the overall charging and discharging revenue difference as the objective function to schedule EV peak demand response.

[0092] Scenario 2: Considering the strong randomness of EVs, V2G aggregators optimize EV participation in peak-shaving demand response scheduling based on the optimal comprehensive indicators of trustworthiness, capacity, and pre-fulfillment rate. Figure 1 As shown.

[0093] The difference in scheduling results between the two EV optimization scheduling models lies in the fact that in scenario 2, the V2G aggregator abandons scheduling EV3 and EV10, which have poor performance indicators. The comprehensive integrity-capacity-pre-honor rate indicators for scenarios 1 and 2 are 14.12 and 16.11, respectively. The lower the probability of reliable execution of the response plan, the greater the risk of default for the V2G aggregator when actually participating in grid peak-shaving demand response, and its market reputation will be negatively affected. By using the method of this invention to schedule EVs with excellent performance indicators, the default risk of EV clusters can be effectively managed, and the reliability of V2G aggregator responses can be improved.

[0094] Table 5 Optimization Results

[0095] Comprehensive index of integrity, capacity, and pre-fulfillment rate 14.12 16.11

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0101] This patent is not limited to the above-described preferred embodiments. Anyone can derive other forms of V2G aggregator intraday optimization scheduling systems for EV participation in peak demand response that take into account the strong randomness of EVs under the guidance of this patent. All equivalent changes and modifications made within the scope of the claims of this invention shall fall within the scope of this patent.

Claims

1. A V2G aggregator-based intraday optimized scheduling system for EV participation in peak shaving demand, considering the strong randomness of EVs, characterized in that: Based on computer and internet systems, and considering the uncertainty of intraday EVs and the response reliability requirements of V2G aggregators, three indicators are introduced: EV historical integrity, capacity factor, and pre-fulfillment rate, as the basis for V2G aggregators to optimize EV scheduling. To address the initial shortage of transaction data, a dynamic quantitative evaluation method is adopted in the intraday optimized scheduling model. Considering the strong randomness of EVs, the intraday optimized scheduling system for V2G aggregators responding to EV participation in peak shaving demand is implemented. Intraday default emergency scheduling is carried out for large-scale EVs. The intraday EV optimized scheduling model with the best comprehensive index of integrity, capacity and pre-compliance rate is adopted to improve the reliability of V2G aggregator response and reduce the uncertainty risk brought by EV randomness to the grid peak shaving demand response. The method for calculating the three performance indicators of each EV—pre-fulfillment rate, historical integrity, and capacity factor—includes the following steps: Step S31: Calculate the EV pre-conservation rate; The purpose of establishing a pre-emptive compliance rate as a reference standard for dispatching EVs is to guide EV users to submit their daily declarations based on the previously awarded bid volume, thereby reducing the additional costs for aggregators to initiate emergency responses to make up for peak-shaving shortfalls. The pre-emptive compliance rate is defined as the ratio of the EV's declared power after the intraday emergency response mechanism is activated to the previously awarded bid power. To incentivize EVs to participate in emergency responses, the pre-emptive compliance rate for participating EVs is set to 1, as shown in the following formula: In the formula: The pre-contract fulfillment rate of electric vehicle i in V2G aggregator j during the t-hour period of the day; , The power of electric vehicles reported for charging and discharging within i days by V2G aggregator j after the emergency mechanism is activated; , This refers to the charging and discharging peak-shaving demand response power that electric vehicle i recently won in the bid for V2G aggregator j; Step S32: Calculate EV's historical integrity score; Direct trust is defined as the average ratio of actual scheduling commands issued by V2G aggregators to actual EV response volume over the past month, calculated directly based on transaction records between the two parties, as shown in the following formula: In the formula: The direct trust level of electric vehicle i in V2G aggregator j; , These represent the number of electric vehicles i that were successfully bid for by V2G aggregator j during the current day's t period within the past month's t period; , The actual response power of electric vehicle i in the aggregated supplier j within a historical month during time period t; The total number of time periods in which electric vehicles (i) participated in V2G aggregator j's peak demand response within a historical month; When the initial number of transactions between electric vehicle i and V2G aggregator j is less than the prescribed number, the historical trust level is determined by the direct trust level. With recommendation trust Together they constitute a whole; when the number of direct transactions between electric vehicle i and V2G aggregator j exceeds the prescribed number, it means that both parties have sufficient historical transaction data to assess the trust level of historical transactions. The reference value of the recommended trust level is negligible. The historical trust level is shown in the following formula: In the formula: The historical credibility of electric vehicle i among V2G aggregators j during the intraday t period; Let k be the recommendation trust level of electric vehicle i in V2G aggregator j; and m be the number of direct transactions between electric vehicle i and V2G aggregator j within one month and the prescribed number of transactions, respectively. Step S33: Calculate the EV capacity factor; The capacity factor is defined as the ratio of the declared power of an EV to the average of the total declared power of all EVs. In the formula: The capacity factor of electric vehicle i in V2G aggregator j during the intraday time period t; The total number of EV applications submitted by V2G aggregators for intraday optimized scheduling after the V2G aggregator activates the emergency response mechanism during time period t.

2. The intraday optimized scheduling system for V2G aggregators to respond to EV participation in peak shaving demand, considering the strong randomness of EVs, as described in claim 1, is characterized in that: Based on the latest application information and considering the risks associated with temporary EV scheduling, V2G aggregators will prioritize the optimal comprehensive index of EV cluster integrity, capacity, and pre-reservation rate when scheduling the invited EV groups within the intraday response period t.

3. The intraday optimized scheduling system for V2G aggregators to respond to EV participation in peak shaving demand, considering the strong randomness of EVs, as described in claim 2, is characterized in that: The performance of individual EVs participating in peak demand response is quantified, taking into account EV charge-discharge equation constraints, EV charge-discharge power constraints, battery safety constraints, travel constraints, supply and demand constraints, and emergency response constraints, in order to obtain the optimal scheduling plan for EVs by V2G aggregators.

4. The intraday optimized scheduling system for V2G aggregators to respond to EV participation in peak shaving demand, considering the strong randomness of EVs, as described in claim 3, is characterized in that: The process of constructing the intraday EV optimization scheduling model with the optimal comprehensive index of integrity, capacity, and pre-fulfillment rate includes the following steps: Step S41: Establish the objective function of the intraday optimization scheduling model for V2G aggregators; EV cluster scheduling is optimized by using the best comprehensive index of integrity, capacity, and pre-reservation rate. In the formula: is the total value of the comprehensive index of integrity, capacity, and pre-reservation rate of the EV clusters participating in peak demand response in V2G aggregator j; T is the number of time periods throughout the day. This is a flag indicating whether electric vehicle i in V2G aggregator j won the bid during time period t; 1 indicates winning the bid, and 0 indicates not winning the bid. These are the weighting coefficients for the intraday scheduling model, set according to different priorities in scheduling. Step S42: Establish constraints for the intraday optimization scheduling model. The constraints include EV charge / discharge equation constraints, EV charge / discharge power constraints, battery safety constraints, travel constraints, supply and demand constraints, and emergency response constraints. In the formula: , These are the charging and discharging efficiencies of EVs, respectively. The battery charge of electric vehicle i under V2G aggregator j during time period t; , The upper and lower limits of battery safety constraints for electric vehicle i under V2G aggregator j during time period t; The rated battery capacity of electric vehicle i in V2G aggregator j; The minimum amount of electricity required for electric vehicle i under V2G aggregator j to meet travel demand during time period t; , These represent the winning bids of aggregator j during the daytime period t. , The peak-shaving demand response power of electric vehicle i in the V2G aggregator j during the time period t is the power of the V2G aggregator winning the bid. The supply and demand constraint inequality means that the power of the V2G aggregator winning the bid shall not be higher than the dispatchable power declared by the EV. Specifically, when an EV accepts an emergency mechanism invitation, EVs that have already accepted the invitation should charge and discharge at a power not exceeding the maximum limit to ensure that the overall EVs do not default. If this still cannot meet the peak-shaving demand response shortfall, an emergency invitation will be sent to other idle EVs within the entire V2G aggregator's network. When scheduling the invited EVs, the V2G aggregator should consider whether there are subsequent invitation periods and battery safety constraints. That is, the emergency response constraints are: In the formula, , The total winning bid charging and discharging power of electric vehicle i in V2G aggregator j for all invited time periods after time period t; Let be the set of all invited time periods for electric vehicle i in V2G aggregator j after time period t. This refers to the k-th time period within the set.

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