An electric vehicle aggregated demand response method considering weather and compliance rate

Abstract

This invention provides an electric vehicle (EV) aggregated demand response method that considers weather and fulfillment rate, relating to the field of EV demand response. Compared with existing EV demand response control methods that do not consider weather and fulfillment differences, this invention introduces weather information to correct vehicle arrival characteristics and combines dynamic fulfillment rate constraints to generate control schemes, enabling the control capacity to match the actual service capacity. Experimental results show that under different weather conditions, this invention can avoid a significant drop in the fulfillment rate caused by excessive control capacity, keeping the overall fulfillment rate stable at approximately 60% or higher. Furthermore, under typical control scales, the aggregator's net profit exhibits better stability than a fixed fulfillment rate assumption, thereby effectively reducing the risk of demand response execution failure and improving the reliability of EV participation in demand response.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle demand response, and more specifically to an electric vehicle aggregated demand response method that takes into account weather and fulfillment rate. Background Technology

[0002] With the rapid growth in the number of electric vehicles, electric vehicles, as adjustable load resources, participate in grid demand response and have become an important means to improve the operational flexibility and regulation capabilities of the power system.

[0003] Existing electric vehicle demand response control methods are mostly based on fixed service capacity or empirical parameters for scheduling. Some schemes introduce queuing models to assess charging station congestion, but usually assume that the charging station service capacity remains unchanged during the control period.

[0004] However, the above methods do not fully consider the impact of weather conditions on the service efficiency of charging stations and vehicle arrival behavior, and are prone to overestimating controllable capacity under adverse weather conditions; control plans generated based on fixed service capacity are prone to problems such as queuing congestion and insufficient fulfillment during the execution phase.

[0005] Furthermore, some regulatory schemes neglect the differences in fulfillment among electric vehicle users and fail to differentiate between vehicles with varying levels of fulfillment stability, leading to increased uncertainty in the regulatory outcomes. Simultaneously, the lack of pre-optimization and reasonable lower bound constraints on incentive parameters may result in insufficient user participation and even further amplify the risks associated with regulatory implementation. Therefore, it is necessary to introduce regulatory modeling methods that consider weather impacts and fulfillment reliability before implementing demand response regulation, in order to reduce the risks of regulatory implementation caused by overestimation of service capacity or unstable fulfillment. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to propose an electric vehicle aggregated demand response method that considers weather and fulfillment rates, comprising:

[0007] The aggregator system receives demand response control instructions for electric vehicles issued by the power grid side. The demand response control instructions include the target control amount and the control time window.

[0008] Obtain the weather for the target area, and determine the weather impact coefficient for the target area based on the pre-set correspondence between weather and weather impact coefficients;

[0009] Calculate the arrival rate after taking weather impacts into account, based on the weather impact coefficient. Then calculate the maximum serviceable capacity. ;

[0010] Arrival rate after taking into account weather effects Calculate the driving time of each electric vehicle. Total stay time This allows for the calculation of the electric vehicle's travel time cost and total dwell time cost, as well as the fixed costs incurred by the electric vehicle in participating in demand response during its journey to the charging station. ;

[0011] Define the dynamic fulfillment rate, calculate the maximum responsive capacity, and calculate the comprehensive score of all electric vehicles in the target area based on the electric vehicle driving time cost, total stay time cost, maximum responsive capacity, and dynamic fulfillment rate. Then, filter the electric vehicles in the target area based on the comprehensive score to obtain a candidate electric vehicle set.

[0012] Define the base incentive unit price based on the comprehensive scores of all candidate electric vehicles. Therefore, the additional incentive unit price is defined. Calculate the total incentive amount per unit for candidate electric vehicle t. Define the planned regulation of the electric power of candidate electric vehicle t. And calculate the effective charge and discharge power. and actual contracted electricity volume Simultaneously calculate the net benefit of candidate electric vehicle t in this demand response. ;

[0013] Electricity is adjusted according to plan. Actual electricity volume fulfilled Total incentive amount per unit Fines for failure to fulfill contractual obligations Target-controlled power Effective charge and discharge power and maximum service capacity Construct the net profit target function and target constraints for aggregators;

[0014] Based on the aggregator's net profit objective function and objective constraints, the particle swarm optimization algorithm is used to solve for the additional incentive unit price. Additional incentives for the power grid's intraday level four response The basic penalty amount for a Level IV response within a power grid within a day Elasticity coefficient Historical performance rate influencing parameters Parameters affecting state of charge This allows for the calculation of the aggregator's net profit within the control time window.

[0015] Optionally, based on the weather impact coefficient, the arrival rate after taking into account the weather impact can be calculated. Specifically, it is calculated using the following formula:

[0016] ;

[0017] in, This characterizes the actual number of electric vehicles arriving at the j-th charging station per hour after taking into account weather conditions. Indicates the weather impact coefficient. This represents the average number of electric vehicles arriving at the j-th charging station per hour.

[0018] Optionally, calculate the maximum serviceable capacity. ,include:

[0019] Arrival rate after taking into account weather effects The maximum service capacity of a charging station within the control time window is calculated using a queuing model. Specifically, this is achieved through the following formula:

[0020] ;

[0021] in, This refers to the number of fast charging stations at the charging stations. The service rate of a single fast charging station is given by h, which represents the length of the controllable time window, and p represents the adjustable power that a single electric vehicle can provide per unit time.

[0022] Optionally, calculate the driving time for each electric vehicle. Total stay time ,include:

[0023] Arrival rate after taking into account weather effects Calculate the utilization rate of charging stations Specifically, this is achieved through the following formula:

[0024] ;

[0025] in, This refers to the number of fast charging stations at the charging stations. Service rate for a single fast charging station;

[0026] Arrival rate after taking into account weather effects and charging station utilization rate The total dwell time of electric vehicles is calculated, including:

[0027] The equivalent business volume 'a' is calculated using the following formula:

[0028] ;

[0029] Calculate the idle probability of a charging station Specifically, this is achieved through the following formula:

[0030] ;

[0031] Where n is the number of electric vehicles being served at the charging station;

[0032] Based on idle probability Calculate the waiting probability of electric vehicles Specifically, this is achieved through the following formula:

[0033] ;

[0034] Waiting probability based on electric vehicles Calculate the average waiting time Specifically, this is achieved through the following formula:

[0035] ;

[0036] Average waiting time Adding this to the service time gives the total dwell time of the electric vehicle. Specifically, it is expressed by the following formula:

[0037] ;

[0038] in, Indicates service hours;

[0039] The driving time of electric vehicles is calculated based on the weather impact coefficient, including:

[0040] The Underwood speed-density model is used to describe the road traffic conditions in the target area, specifically expressed by the following formula:

[0041] ;

[0042] in, In order to achieve a traffic density of Average driving speed at that time; For free flow velocity; Traffic density per unit road length; Congestion density;

[0043] The calculation takes weather conditions into account when calculating driving speed. Specifically, it is calculated using the following formula:

[0044] ;

[0045] Based on driving speed taking weather conditions into account Calculate the travel time from the current location of electric vehicle i to charging station j. Specifically, this is achieved through the following formula:

[0046] ;

[0047] in, For electric vehicles The driving distance between the vehicle and charging station j.

[0048] Optionally, calculate the electric vehicle's travel time cost and total dwell time cost, and calculate the fixed costs incurred by the electric vehicle in participating in demand response during its journey to the charging station. ,include:

[0049] Introducing a time value factor v, the driving time of electric vehicles is considered. Multiplying this by the time value coefficient v yields the electric vehicle's driving time cost, which in turn represents the total dwell time of the electric vehicle. Multiplying the time value coefficient v by the total dwell time cost of the electric vehicle yields the total dwell time cost. Adding the driving time cost and the total dwell time cost of the electric vehicle gives the total dwell time cost of the electric vehicle. Heading to the charging station Time cost ;

[0050] Calculate the energy consumption cost of an electric vehicle during its operation. Specifically, this is achieved through the following formula:

[0051] ;

[0052] in, The unit price of electricity used for driving. Energy consumption per unit distance For electric vehicles The driving distance between the charging station j;

[0053] The sum of time cost and energy cost is calculated and used as the fixed cost of electric vehicles participating in demand response. .

[0054] Optionally, the dynamic fulfillment rate is expressed as:

[0055] ;

[0056] in, This represents the dynamic fulfillment rate of electric vehicle i within the target area. Indicates the parameters affecting historical performance rate. Indicates the parameters affecting the state of charge. This represents the actual historical response power. For historical planned response power, This represents the operating state function based on state of charge, available time window, and distance factors. Let represent the state of charge of the i-th electric vehicle. For electric vehicles The driving distance between the vehicle and charging station j.

[0057] Optionally, calculate the maximum responsive capacity, including:

[0058] When the electric vehicle i is in discharge mode, the maximum responsive capacity is calculated using the following formula:

[0059] ;

[0060] in, Indicates the maximum responsive capacity of the discharge mode. Indicates discharge efficiency. For the battery capacity of candidate electric vehicle i, This indicates the state of charge of electric vehicle i after it arrives at the charging station. Indicates the minimum permissible state of charge;

[0061] When the electric vehicle i is in charging mode, the maximum responsive capacity is calculated using the following formula:

[0062] ;

[0063] in, Indicates the maximum responsive capacity of the charging mode. Indicates charging efficiency. Indicates the highest permissible state of charge.

[0064] Optionally, based on the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate, a comprehensive score is calculated for all electric vehicles within the target area. The electric vehicles within the target area are then filtered based on this comprehensive score to obtain a candidate electric vehicle set, including:

[0065] For each electric vehicle, among the distances between the electric vehicle and each charging station, a first distance less than or equal to a distance threshold is obtained, and the charging station corresponding to the first distance is used as a candidate charging station.

[0066] For each candidate charging station, based on the entropy weight method, the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate are combined to obtain a score for the electric vehicle, and then multiple scores for the electric vehicle are obtained. The maximum value among all scores is selected as the comprehensive score of the electric vehicle, thus obtaining the comprehensive score for each electric vehicle.

[0067] Among all the comprehensive scores, the comprehensive scores that are greater than or equal to the scoring threshold are obtained, and the corresponding electric vehicles are used as candidate electric vehicles. All candidate electric vehicles are combined into a candidate electric vehicle set.

[0068] Optionally, a base incentive unit price can be defined based on the comprehensive score of all candidate electric vehicles. Therefore, the additional incentive unit price is defined. Calculate the total incentive amount per unit for candidate electric vehicle t. Define the planned regulation of the electric power of candidate electric vehicle t. And calculate the effective charge and discharge power. and actual contracted electricity volume Simultaneously calculate the net benefit of candidate electric vehicle t in this demand response. ,include:

[0069] Define the global minimum basic stimulus starting point, which is expressed as:

[0070] ;

[0071] in, Indicates the minimum basic incentive starting point globally. Let t represent the set of candidate electric vehicles. For the variable costs related to the electricity volume to be delivered, Fixed costs incurred during travel, waiting, charging, or discharging. The total penalty amount arising from failure to fully comply with the contract. Additional incentive unit price;

[0072] Based on the comprehensive scores of all candidate electric vehicles, they are sorted in descending order to obtain the sorting index for each candidate electric vehicle. Then, the basic incentive unit price is defined, wherein the basic incentive unit price Represented as:

[0073] ;

[0074] in, The minimum excitation difference between adjacent candidate electric vehicles;

[0075] The intensity factor is defined based on the urgency of the grid-side demand response. , represented as:

[0076] ;

[0077] in, Characterizes the urgency of demand response on the grid side;

[0078] According to the intensity factor Calculate the additional incentive unit price Specifically, this is achieved through the following formula:

[0079] ;

[0080] in, For the minimum additional incentive amount, The maximum additional incentive amount;

[0081] Based on additional incentive unit price Define the elasticity coefficient of punishment relative to additional incentives. , represented as:

[0082] ;

[0083] in, The additional penalty amount is represented as follows:

[0084] ;

[0085] in This is the base penalty amount for a Level 4 response within the power grid per day. This serves as an additional incentive for the power grid's intraday Level 4 response;

[0086] Additional penalty amount Basic penalty amount for the power grid's level four response within a day Added together, the amount received was the penalty for the company's failure to fulfill its contractual obligations. The additional incentive unit price and basic incentive unit price Adding them together, we get the total incentive amount per unit for candidate electric vehicle t. ;

[0087] Calculate the state of charge of the candidate electric vehicle after it arrives at the charging station , represented as:

[0088] ;

[0089] in, This represents the initial state of charge of the candidate electric vehicle t. For power consumption per unit distance, Let be the distance between the candidate electric vehicle t and the candidate charging station l. Let t be the battery capacity of the candidate electric vehicle;

[0090] Define the planned controlled power of candidate electric vehicle t. Specifically, when candidate electric vehicle t is in discharge mode, the planned controlled power... Represented as:

[0091] ;

[0092] in, Indicates the length of the control time window. Indicates discharge efficiency. Indicates the minimum permissible state of charge. Let t be the battery capacity of the candidate electric vehicle. The effective charging and discharging power, determined jointly by the candidate electric vehicle t and the connected charging station, can be calculated using the following formula:

[0093] ;

[0094] in, Let t be the maximum effective power of the candidate electric vehicle. The maximum effective power of the charging pile connected to the candidate electric vehicle t;

[0095] When the candidate electric vehicle t is in charging mode, the power supply is planned to be adjusted. Represented as:

[0096] ;

[0097] in, For charging efficiency, Indicates the highest permissible state of charge;

[0098] Planned power regulation and dynamic fulfillment rate Multiplying these together, we obtain the actual delivered electric power of the candidate electric vehicle. The actual volume of electricity delivered will be Total incentive amount per unit Multiply by each other to get the total incentive amount. ;

[0099] In the event that the candidate electric vehicle fails to fulfill its obligations, the electricity supply will be adjusted according to the plan. and the actual contracted electricity volume Determine the amount of electricity not fulfilled. Specifically, this is achieved through the following formula:

[0100] ;

[0101] Unfulfilled electricity Basic penalty amount for the power grid's level four response within a day Multiply by each other to get the total base penalty amount. Additional penalty amount and unfulfilled electricity volume Multiply by each other to get the total amount of additional penalties. The total amount of the basic penalty and the total amount of additional penalties Add them together to get the total penalty amount. ;

[0102] Calculate the net benefit of candidate electric vehicle t in this demand response. Specifically, this is achieved through the following formula:

[0103] .

[0104] Optionally, power consumption can be adjusted according to a plan. Actual electricity volume fulfilled Total incentive amount per unit Fines for failure to fulfill contractual obligations Target-controlled power Effective charge and discharge power and maximum service capacity Construct the aggregator's net profit objective function and objective constraints, including:

[0105] Construct the net profit objective function for the aggregator, expressed as:

[0106] ;

[0107] in, This indicates the aggregator's net profit during the regulatory window. This indicates the price at which the power grid unit regulates and compensates. This indicates the unit price of the penalty paid by the aggregator to the power grid for breach of contract. Adjust capacity to meet targets;

[0108] Construct target constraints, which include:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] in, This represents the maximum adjustable capacity of the candidate electric vehicle t. This indicates whether the candidate electric vehicle t participates in demand response. This indicates the maximum penalty amount per unit capacity. The minimum level of completion acceptable to the power grid, where h represents the length of the control time window. Indicates discharge efficiency. Indicates charging efficiency. Let t be the battery capacity of the candidate electric vehicle. This represents the initial state of charge of the candidate electric vehicle t. Indicates the minimum permissible state of charge. Indicates the highest permissible state of charge. Energy consumption per unit distance For the minimum additional incentive amount, Let t be the distance between the candidate electric vehicle t and the candidate charging station l.

[0119] The beneficial effects of adopting the above technical solution are as follows:

[0120] Compared to existing electric vehicle demand response control methods that do not consider weather and fulfillment differences, this invention incorporates weather information to correct vehicle arrival characteristics and combines dynamic fulfillment rate constraints to generate control schemes, ensuring that the control capacity matches the actual service capacity. Experimental results show that under different weather conditions, this invention can avoid a significant drop in the fulfillment rate caused by excessive control capacity, maintaining an overall fulfillment rate stably above approximately 60%. Furthermore, under typical control scales, the aggregator's net profit exhibits better stability than under a fixed fulfillment rate assumption, thereby effectively reducing the risk of demand response execution failure and improving the reliability of electric vehicles participating in demand response. Attached Figure Description

[0121] Figure 1 This is a flowchart illustrating an electric vehicle aggregated demand response method that considers weather and fulfillment rate in an embodiment of the present invention.

[0122] Figure 2 This is a schematic diagram illustrating the aggregator's net profit and control capacity in an embodiment of the present invention;

[0123] Figure 3 This is a schematic diagram comparing dynamic contract fulfillment rates under different weather conditions in an embodiment of the present invention;

[0124] Figure 4 This is a schematic diagram comparing the net profit of aggregators under different fulfillment rate modeling methods in this embodiment of the invention;

[0125] Figure 5 This is a schematic diagram illustrating the trend of aggregator's net profit changing with control capacity under different weather conditions in an embodiment of the present invention. Detailed Implementation

[0126] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0127] To address the common problems in existing technologies, such as static assessment of regulatory service capabilities, coarse characterization of vehicle performance, reliance on experience for pre-setting incentive parameters, and difficulty in constraining the execution risks of regulatory schemes, this solution adopts the overall design concept of "pre-event risk constraint, in-event joint optimization, and post-event feedback update." It unifies and collaboratively models and applies weather influencing factors, traffic and queuing operation characteristics, dynamic performance rate modeling methods, and swarm intelligence optimization strategies to form a complete closed loop of demand response regulation algorithm.

[0128] Based on this, the present invention provides an electric vehicle aggregation demand response method that considers weather and fulfillment rate. By introducing weather information before demand response regulation, key parameters reflecting vehicle arrival characteristics in the charging station queuing model are dynamically corrected, constructing an adjustable service boundary reflecting the actual operating environment. Under the constraints of this service boundary, dynamic fulfillment rate parameters are calculated by combining historical demand response completion data and current operating status of electric vehicles. Under the premise of satisfying minimum basic incentive constraints and vehicle physical feasibility constraints, the scale of demand response regulation and incentive and penalty parameters are jointly optimized. This effectively reduces the risk of demand response failure due to uncertainty in vehicle arrival behavior or unstable fulfillment before regulation execution, improving the overall fulfillment reliability of electric vehicle aggregation in demand response and the operational stability of aggregators. Specifically, combined with... Figure 1 This may include the following steps:

[0129] Step 1: The aggregator system receives demand response control instructions for electric vehicles issued by the power grid side. The demand response control instructions include the target control amount and the control time window, for example, reducing 2000 kWh within 15 minutes.

[0130] This invention introduces an adjustable service boundary to address the issue that electric vehicles arrive at charging stations randomly, charging service capacity is limited, and weather and traffic conditions can cause the theoretical capacity (number of idle fast charging stations x charging / discharging power of fast charging stations) to differ from the actual serviceable capacity. Without restrictions, the adjustment algorithm might overestimate the scale of adjustment the system can withstand before execution.

[0131] Step 2: Obtain the weather for the target area, and determine the weather impact coefficient for the target area based on the pre-set correspondence between weather and weather impact coefficients;

[0132] The relationship between weather and weather impact coefficient is illustrated with an example. The specific formula for this relationship is as follows:

[0133] ;

[0134] The specific value of the weather impact coefficient can be adjusted based on historical operating data, regional climate characteristics, or engineering experience. The above example is a feasible way to correct the vehicle arrival characteristic parameters based on weather conditions.

[0135] Step 3: Calculate the arrival rate after taking into account the weather impact based on the weather impact coefficient. Then calculate the maximum serviceable capacity. ;

[0136] Calculate the arrival rate after taking weather impacts into account, based on the weather impact coefficient. Specifically, it is calculated using the following formula:

[0137] ;

[0138] in, This characterizes the actual number of electric vehicles arriving at the j-th charging station per hour after taking into account weather conditions. Indicates the weather impact coefficient. This represents the average number of electric vehicles arriving at the j-th charging station per hour.

[0139] Arrival rate after taking into account weather effects The maximum service capacity of a charging station within the control time window is calculated using a queuing model. The queuing model M / M / c outputs the maximum number of vehicles that can be served per unit time and the maximum amount of charge / discharge power that can be completed per unit time, assuming no severe congestion (maximum number of vehicles served per unit time x charge / discharge power). In other words, it represents the maximum responsive capacity the system can handle under current conditions, specifically achieved through the following formula:

[0140] ;

[0141] in, This refers to the number of fast charging stations at the charging stations. The service rate of a single fast charging station is given by h, which represents the length of the controllable time window; p represents the adjustable power that a single electric vehicle can provide per unit time.

[0142] Step 4: Based on the arrival rate after taking into account the impact of weather Calculate the driving time of each electric vehicle. Total stay time This allows for the calculation of the electric vehicle's travel time cost and total dwell time cost, as well as the fixed costs incurred by the electric vehicle in participating in demand response during its journey to the charging station. ;

[0143] Arrival rate after taking into account weather effects Calculate the utilization rate of charging stations Specifically, this is achieved through the following formula:

[0144] ;

[0145] in, This refers to the number of fast charging stations at the charging stations. For the service rate of a single fast charging pile, when the utilization rate ρ of the charging station approaches 1, the average waiting time increases exponentially. Therefore, this invention indirectly constrains the utilization rate of the charging station by limiting the upper limit of the waiting time, thereby preventing the control scheme from entering a high-risk operating range.

[0146] Arrival rate after taking into account weather effects and charging station utilization rate The total dwell time of electric vehicles is calculated, including:

[0147] The equivalent workload 'a' (the average number of fast charging stations required to operate simultaneously under steady-state conditions) is calculated using the following formula:

[0148] ;

[0149] Calculate the idle probability of a charging station Specifically, this is achieved through the following formula:

[0150] ;

[0151] Where n is the number of electric vehicles being served at the charging station;

[0152] Based on idle probability Calculate the waiting probability of electric vehicles Specifically, this is achieved through the following formula:

[0153] ;

[0154] Waiting probability based on electric vehicles Calculate the average waiting time Specifically, this is achieved through the following formula:

[0155] ;

[0156] Average waiting time Adding this to the service time gives the total dwell time of the electric vehicle. This includes waiting time and charging / discharging time, specifically expressed by the following formula:

[0157] ;

[0158] in, Indicates service time (charging / discharging time);

[0159] When arrival rate The shorter the time, the higher the utilization rate of the charging station. The smaller the equivalent business volume The smaller the value, the lower the probability of the charging station being idle. The larger the probability of electric vehicles waiting. The smaller the value, the shorter the average waiting time for electric vehicles. The smaller the value, the shorter the time electric vehicles spend at charging stations. The smaller the time spent at charging stations, the worse the weather. This means that while vehicle travel time increases under adverse weather conditions, the risk of queuing and congestion at charging stations actually decreases. From a demand response perspective, charging stations offer more stable service capabilities, which helps reduce the risk of control failures caused by internal issues.

[0160] The driving time of electric vehicles is calculated based on the weather impact coefficient, including:

[0161] The road traffic model uses a real-world map model composed of longitude and latitude. Charging stations are required to report their longitude and latitude, and electric vehicles are required to upload their longitude and latitude in real time using map tools (such as Amap). The distance between electric vehicles and charging stations is calculated using the GEO data structure in Redis.

[0162] The Underwood speed-density model is used to describe the road traffic conditions in the target area, specifically expressed by the following formula:

[0163] ;

[0164] in, In order to achieve a traffic density of Average driving speed at that time; For free flow velocity; Traffic density per unit road length; The congestion density is used as the basis for PSO optimization, which yields the optimal free-flow velocity. =60km / h; congestion density is determined based on fixed parameters such as the number of lanes, lane width, and lane class, according to road physical conditions. =80 (This can be an empirical value or calibrated offline). Current "Equivalent Vehicle Density" Congestion index provided by a third-party map platform Mapped to obtain, take This mapping is used to incorporate real-time road congestion levels into the vehicle speed model without directly obtaining vehicle density. This is an indicator of real-time congestion levels.

[0165] The calculation takes weather conditions into account when calculating driving speed. Specifically, it is calculated using the following formula:

[0166] ;

[0167] Based on driving speed taking weather conditions into account Calculate the travel time of electric vehicle i from its current location to charging station j. Specifically, this is achieved through the following formula:

[0168] ;

[0169] in, For electric vehicles The driving distance between the charging station j;

[0170] The above calculations allow us to obtain the estimated arrival time of vehicles under current traffic conditions before demand response adjustments are implemented, which can then be used to assess the feasibility of fulfilling obligations. The more congested the traffic, the slower the vehicles travel.

[0171] When weather conditions worsen, road speeds decrease, significantly increasing the travel time for electric vehicles to reach charging stations, thereby increasing uncertainty in the demand response execution phase.

[0172] Introducing a time value coefficient v, representing the monetary value of a car owner's loss per unit of time, and factoring in the driving time of electric vehicles... Multiplying this by the time value coefficient v yields the electric vehicle's driving time cost, which in turn represents the total dwell time of the electric vehicle. Multiplying the time value coefficient v by the total dwell time cost of the electric vehicle yields the total dwell time cost. Adding the driving time cost and the total dwell time cost of the electric vehicle gives the total dwell time cost of the electric vehicle. Heading to the charging station Time cost Specifically, this is achieved through the following formula:

[0173] ;

[0174] Calculate the energy consumption cost of an electric vehicle during its operation. Specifically, this is achieved through the following formula:

[0175] ;

[0176] in, The unit price of electricity used for driving. Energy consumption per unit distance For electric vehicles The driving distance between the charging station j;

[0177] The sum of time cost and energy cost is calculated and used as the fixed cost of electric vehicles participating in demand response. Specifically, it is expressed by the following formula:

[0178] ;

[0179] This fixed cost is used for subsequent electric vehicle scoring, vehicle-to-charging station matching, and incentive parameter optimization processes.

[0180] In the process of demand response regulation, the dynamic fulfillment rate is mainly used to reduce the allocation weight of vehicles with unstable fulfillment during the optimization process, and to gradually eliminate vehicles with long-term low fulfillment rates in multiple rounds of regulation.

[0181] Step 5: Define the dynamic fulfillment rate, which is expressed as:

[0182] ;

[0183] in, This represents the dynamic fulfillment rate of electric vehicle i within the target area. Indicates the parameters affecting historical performance rate. This represents the parameter affecting the state of charge, β1+β2=1, where β1 and β2 are parameters to be calculated later; This represents the actual historical response power. For historical planned response power, This represents the operating state function based on state of charge, available time window, and distance factors. Let represent the state of charge of the i-th electric vehicle. For electric vehicles The driving distance to charging station j Calculated based on Redis's GEO data structure; where, , Stored in a database.

[0184] The maximum responsive capacity is calculated specifically when electric vehicle i is in discharge mode using the following formula:

[0185] ;

[0186] in, Indicates the maximum responsive capacity of the discharge mode. Indicates discharge efficiency. For the battery capacity of candidate electric vehicle i, This indicates the state of charge of electric vehicle i after it arrives at the charging station. Indicates the minimum permissible state of charge;

[0187] When the electric vehicle i is in charging mode, the maximum responsive capacity is calculated using the following formula:

[0188] ;

[0189] in, Indicates the maximum responsive capacity of the charging mode. Indicates charging efficiency. Indicates the highest permissible state of charge.

[0190] Based on the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate, a comprehensive score is calculated for all electric vehicles in the target area. The electric vehicles in the target area are then screened based on the comprehensive score to obtain a candidate electric vehicle set.

[0191] Dimensionless processing is applied to the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate. Extreme value normalization is used for the maximum responsive capacity and historical fulfillment rate, while inverse normalization is used for driving time cost and waiting time cost. Then, the entropy weight method is used to score electric vehicles and charging stations. The highest score among all scores for each electric vehicle and charging station is selected as the final score for that electric vehicle, achieving vehicle-to-charging-station matching. The score obtained by the entropy weight method is more like assigning a "recommendation level" to the vehicle than directly issuing a task.

[0192] Not all electric vehicles are included in the consideration level. For example, electric vehicles with a vehicle-to-charging distance of more than 15km are not considered, as are electric vehicles with scores below the threshold. If it is calculated that the owner of the electric vehicle would suffer a loss after participating in demand response, it is also not considered.

[0193] Specifically, for each electric vehicle, among the distances between the electric vehicle and each charging station, a first distance less than or equal to a distance threshold is obtained, and the charging station corresponding to the first distance is used as a candidate charging station;

[0194] For each candidate charging station, based on the entropy weight method, the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate are combined to obtain a score for the electric vehicle, and then multiple scores for the electric vehicle are obtained. The maximum value among all scores is selected as the comprehensive score of the electric vehicle, thus obtaining the comprehensive score for each electric vehicle.

[0195] Among all comprehensive scores, the comprehensive scores that are greater than or equal to the scoring threshold are obtained, and the corresponding electric vehicles are used as candidate electric vehicles. All candidate electric vehicles are combined into a candidate electric vehicle set.

[0196] Therefore, this invention uses the entropy weight method to comprehensively judge whether electric vehicles are suitable for participating in demand response. It focuses on evaluating factors such as whether the electric vehicle can reach the destination under the current conditions, whether it has enough power to adjust, whether it has stable performance capability, and whether the charging station has sufficient adjustability, so as to avoid the risk of control failure due to vehicles not being able to reach the station, excessively long queues at the station, or unstable performance during the implementation of the control plan.

[0197] The total time an electric vehicle spends at a charging station consists of two parts: queuing time and charging service time. The queuing time is determined by the real-time queuing status of the charging station (including the number of vehicles waiting to be charged, queue sorting rules, etc.); the charging service time depends on the number of charging piles configured at the charging station and the service rate of a single charging pile.

[0198] To ensure the economic feasibility of electric vehicle users participating in the demand response process, while balancing grid-side control objectives and aggregator revenue, this invention constructs an incentive pricing model based on a no-loss constraint, taking into account vehicle travel costs, performance uncertainty, and the level of control demand. This model divides the unit electricity incentive price into two parts: a basic incentive and an additional incentive, and determines a reasonable lower bound for the basic incentive through constraints.

[0199] The basic incentives, additional incentives, and corresponding basic and additional penalties described in this invention are all based on "unit electricity (yuan / kWh)" as the pricing basis, and are settled based on the actual fulfilled or unfulfilled electricity of a single electric vehicle, ultimately forming the total amount of incentives or penalties for that electric vehicle in a demand response.

[0200] Step 6: Define the basic incentive unit price based on the comprehensive scores of all candidate electric vehicles. Therefore, the additional incentive unit price is defined. Calculate the total incentive amount per unit for candidate electric vehicle t. Define the planned regulation of the electric power of candidate electric vehicle t. And calculate the effective charge and discharge power. and actual contracted electricity volume Simultaneously calculate the net benefit of candidate electric vehicle t in this demand response. ;

[0201] To ensure that electric vehicles participating in demand response do not lose money, a user economic feasibility constraint is introduced when determining the basic incentive unit price.

[0202] The basic incentive unit price At least the following inequality relations must be satisfied:

[0203] ;

[0204] in, For the variable costs related to the electricity volume to be delivered, Fixed costs incurred during travel, waiting, charging, or discharging. The total penalty amount arising from failure to fully comply with the contract. Additional incentive unit price;

[0205] When the basic incentive unit price is lower than the above threshold, the corresponding control scheme will be judged as not meeting the minimum basic incentive constraint and will not participate in subsequent control parameter optimization or will be automatically corrected.

[0206] The "minimum basic incentive starting point" is derived by working backward from the above formula. Since the costs and fulfillment volumes of different vehicles vary, the above formula will give a minimum feasible threshold for each vehicle. In order to ensure that all vehicles in the participating set meet the "no loss" requirement, a global minimum basic incentive starting point is defined, which will be explained in detail in step 6.1.

[0207] Step 6.1: Define the global minimum basic stimulus starting point, which is expressed as:

[0208] ;

[0209] in, Indicates the minimum basic incentive starting point globally. Let t represent the set of candidate electric vehicles. For the variable costs related to the electricity volume to be delivered, Fixed costs incurred during travel, waiting, charging, or discharging. The total penalty amount arising from failure to fully comply with the contract. Additional incentive unit price;

[0210] in, It is a uniform minimum starting point required for "all candidate electric vehicles to not lose money", not a fixed value that is finally given to candidate electric vehicles.

[0211] Based on this, to reflect the differentiated incentive mechanism of "one price per vehicle," this invention uses a linearly increasing method to allocate a basic incentive unit price to different vehicles. According to the comprehensive scores of all candidate electric vehicles, they are sorted in descending order to obtain a ranking index for each candidate electric vehicle. Then, the basic incentive unit price is defined, wherein the basic incentive unit price Represented as:

[0212] ;

[0213] in, This is the minimum incentive difference between adjacent candidate electric vehicles, used to reflect the difference in control priority. Its value can be set according to the actual platform rules or empirical parameters.

[0214] Under the premise of meeting the minimum basic incentive constraints, relying solely on the basic incentive unit price is insufficient to fully characterize the differentiated requirements of the power grid side for response reliability and performance strength under different control scenarios. Therefore, this invention introduces additional incentives and additional penalty mechanisms in addition to the basic incentives to further guide and constrain the behavior of electric vehicles participating in the demand response process.

[0215] Step 6.2: Define the intensity factor based on the urgency of the grid-side demand response. , represented as:

[0216] ;

[0217] in, Characterizes the urgency of demand response on the grid side. The most serious (most urgent) situation. The lightest (most relaxed), that is .

[0218] According to the intensity factor Calculate the additional incentive unit price Specifically, this is achieved through the following formula:

[0219] ;

[0220] in, For the minimum additional incentive amount, The maximum additional incentive amount;

[0221] Based on additional incentive unit price Define the elasticity coefficient of punishment relative to additional incentives. , represented as:

[0222] ;

[0223] Among them, when When, it indicates that the penalty grows faster than the incentive (stronger constraint); when When this occurs, it indicates that the punishment increases more slowly than the incentive (more lenient).

[0224] in, The additional penalty amount is represented as follows:

[0225] ;

[0226] in This is the base penalty amount for a Level 4 response within the power grid per day. This serves as an additional incentive for the power grid's intraday Level 4 response;

[0227] Additional penalty amount Basic penalty amount for the power grid's level four response within a day Added together, the amount received was the penalty for the company's failure to fulfill its contractual obligations. Specifically, it is expressed by the following formula:

[0228] ;

[0229] Step 6.3: Add the additional incentive unit price and basic incentive unit price Adding them together, we get the total incentive amount per unit for candidate electric vehicle t. , represented as:

[0230] ;

[0231] Define the planned controlled power of candidate electric vehicle t. Specifically, when candidate electric vehicle t is in discharge mode, the planned controlled power... Represented as:

[0232] ;

[0233] in, Indicates the length of the control time window. Indicates discharge efficiency. Indicates the minimum permissible state of charge. The effective charging and discharging power, determined jointly by the candidate electric vehicle t and the connected charging station, can be calculated using the following formula:

[0234] ;

[0235] in, Let t be the maximum effective power of the candidate electric vehicle. The maximum effective power of the charging pile connected to the candidate electric vehicle t;

[0236] When the candidate electric vehicle t is in charging mode, the power supply is planned to be adjusted. Represented as:

[0237] ;

[0238] in, For charging efficiency, Indicates the highest permissible state of charge; where, Use the default value of 0.9. Use the default value of 0.1.

[0239] Planned power regulation and dynamic fulfillment rate Multiplying these together, we obtain the actual delivered electric power of the candidate electric vehicle. Specifically, it is expressed by the following formula:

[0240] ;

[0241] The actual volume of electricity delivered is Total incentive amount per unit Multiply by each other to get the total incentive amount. , represented as:

[0242] ;

[0243] The incentive amount is used to compensate electric vehicle users for charging and discharging costs, travel time costs, and performance uncertainty risks incurred during the demand response process.

[0244] Calculate the state of charge of the candidate electric vehicle after it arrives at the charging station , represented as:

[0245] ;

[0246] in, This represents the initial state of charge of the candidate electric vehicle t. For power consumption per unit distance, The distance between candidate electric vehicles and charging stations. Let t be the battery capacity of the candidate electric vehicle;

[0247] In cases of incomplete fulfillment of obligations, an additional penalty mechanism will be introduced to constrain the performance of electric vehicles. Specifically, if a candidate electric vehicle t fails to fulfill its obligations, the power consumption will be adjusted according to the plan. and the actual contracted electricity volume Determine the amount of electricity not fulfilled. Specifically, this is achieved through the following formula:

[0248] ;

[0249] Unfulfilled electricity Basic penalty amount for the power grid's level four response within a day Multiply by each other to get the total base penalty amount. Specifically, it is expressed by the following formula:

[0250] ;

[0251] Additional penalty amount and unfulfilled electricity volume Multiply by each other to get the total amount of additional penalties. Specifically, it is expressed by the following formula:

[0252] ;

[0253] Total amount of basic penalty and the total amount of additional penalties Add them together to get the total penalty amount. Specifically, it is expressed by the following formula:

[0254] ;

[0255] Calculate the net benefit of candidate electric vehicle t in this demand response. Specifically, this is achieved through the following formula:

[0256] ;

[0257] This net profit expression serves two purposes: firstly, it constrains the "no-loss condition" during the incentive pricing phase; secondly, it provides a consistent mathematical basis for the incentive and penalty terms in the subsequent aggregator profit function.

[0258] After obtaining the candidate demand response vehicle set and the corresponding dynamic fulfillment rate parameters, a demand response control parameter optimization model is constructed. The parameters to be optimized include at least the number of electric vehicles participating in demand response, the control power allocation ratio of each electric vehicle, and the corresponding incentive and penalty parameters. The optimization process is carried out under the premise of satisfying the controllable service boundary constraints, dynamic fulfillment rate constraints, and minimum basic incentive constraints, so as to avoid generating control schemes that are unexecutable or have excessively high fulfillment risks. In this embodiment, the parameter optimization process is implemented using the particle swarm optimization algorithm. The combination of control parameters that meet the constraints is obtained through iterative search, and the final demand response control scheme is generated accordingly. This is explained in detail in step 7.

[0259] Step 7: Adjust power consumption according to plan Actual electricity volume fulfilled Total incentive amount per unit Fines for failure to fulfill contractual obligations Target-controlled power Effective charge and discharge power and maximum service capacity Construct the net profit target function and target constraints for aggregators;

[0260] Construct the net profit objective function for the aggregator, expressed as:

[0261] ;

[0262] in, This indicates the aggregator's net profit during the regulatory window. This indicates the price at which the power grid unit regulates and compensates. This indicates the unit price of the penalty paid by the aggregator to the power grid for breach of contract. Adjust capacity to meet targets;

[0263] In the optimization phase of the control scheme, the actual response power is calculated using an estimate based on the dynamic compliance rate; in the evaluation or simulation settlement phase of the control implementation results, the actual response power is based on the simulation or metering results.

[0264] Construct target constraints, which include:

[0265] ;

[0266] ;

[0267] ;

[0268] ;

[0269] ;

[0270] ;

[0271] ;

[0272] ;

[0273] ;

[0274] in, This represents the maximum adjustable capacity of the candidate electric vehicle t. This indicates whether the candidate electric vehicle t participates in demand response. This indicates the maximum penalty amount per unit capacity. The minimum level of completion acceptable to the power grid;

[0275] Step 8: Based on the aggregator's net profit objective function and objective constraints, use the particle swarm optimization algorithm to solve for the additional incentive unit price. Additional incentives for the power grid's intraday level four response The basic penalty amount for a Level IV response within a power grid within a day Elasticity coefficient Historical performance rate influencing parameters Parameters affecting state of charge Then, calculate the aggregator's net profit within the control time window;

[0276] The core parameters of the particle swarm optimization algorithm are set as follows: population size 50, number of iterations 100, inertia weight ω∈[0.4,0.9], learning factor c1=c2=2, and maximum speed Vmax is set to 15% of the variable value range to ensure that the optimization process takes into account both global search and convergence efficiency.

[0277] After the demand response control scheme is executed, the aggregator system collects and writes the actual response power of each participating electric vehicle into the database, as well as the planned response power of each participating electric vehicle into the database. The actual response power is compared with the target response power in the control command to determine the performance of the corresponding electric vehicle and the overall control task. The performance result is recorded as control feedback data, used to update the dynamic performance rate parameters of the electric vehicles or as a reference for subsequent demand response control.

[0278] The net profit of aggregators under different control capacities differs between snowy and sunny days, for example. Figure 2 As shown. By Figure 2 It can be seen that the relationship between aggregator net profit and regulatory capacity is not monotonic, but rather exhibits an inverted U-shaped characteristic of "increasing first and then decreasing," indicating that there is an optimal range for regulatory scale. Excessive expansion of regulatory capacity will lead to a decrease in profits due to increased performance risks and penalty costs. β1=0.5, β2=0.5. The dynamic performance rates for snowy and sunny days under different regulatory capacities are compared as follows: Figure 3 As shown. By Figure 3 It can be seen that under different weather conditions, the dynamic compliance rate exhibits a "non-monotonic" characteristic with changes in regulatory capacity, exhibiting an "optimal range." Severe weather does not necessarily reduce the compliance rate; its impact is more reflected in changes to the "sensitivity to regulatory scale." Comparison of aggregator net profit under different compliance rate modeling methods is shown below. Figure 4 As shown. By Figure 4 It is evident that adopting a fixed fulfillment rate assumption significantly impacts the aggregator's revenue assessment results. A higher fulfillment rate leads to higher net profit for the aggregator. In contrast, a dynamic fulfillment rate model based on adaptive scenarios and control scale yields higher and more reliable aggregator net profit. β1=0.5, β2=0.5. From Figure 5 It can be seen that the net profit of aggregators shows a significant downward trend as the control capacity increases, and under the same control capacity, the net profit under adverse weather conditions is always lower than that under sunny conditions; this indicates that blindly expanding the scale of control will significantly amplify the risk and cost of fulfillment, and weather factors will further amplify the sensitivity of profits to the scale of control.

[0279] This invention introduces a weather impact and dynamic fulfillment rate modeling mechanism in the pre-demand response regulation stage. By correcting vehicle arrival characteristics and constructing an adjustable service boundary, the scale of demand response regulation and incentive / penalty parameters are jointly optimized to reduce the fulfillment risk in the regulation execution stage from the source.

[0280] This invention obtains weather information such as temperature and humidity, and introduces a weather influence coefficient to dynamically correct the vehicle arrival rate parameter in the charging station queuing model. This breaks through the assumption in the prior art that the arrival rate and service capacity are fixed and makes the assessment of controllable capacity closer to the real operating environment.

[0281] The weather-corrected vehicle arrival characteristic parameters are introduced into the M / M / c queuing model to calculate the maximum service capacity of charging stations within the control time window. This capacity is then used as the upper limit constraint on the scale of demand response control, avoiding queuing congestion and insufficient response during the implementation phase due to overestimation of service capacity.

[0282] By comprehensively considering the historical demand response completion status of electric vehicles and their current operating status (including factors such as state of charge, overlap between available time windows and control time windows, and distance between vehicles and charging stations), a dynamic fulfillment rate model is constructed to characterize the fulfillment reliability of different vehicles under the current control scenario, thereby achieving a quantitative expression of the differences in fulfillment stability.

[0283] Under the premise of satisfying the constraints of adjustable service boundary, dynamic performance rate, and minimum basic incentive, the electric vehicle set participating in demand response, the proportion of controlled power allocation, and incentive and penalty parameters are jointly optimized to avoid generating control schemes that are economically infeasible or have excessively high performance risks.

[0284] After the regulation is implemented, the actual performance results will be used as feedback data to update the dynamic performance rate parameters of electric vehicles or as a reference for subsequent regulation, so as to realize the continuous correction and adaptive optimization of the demand response regulation strategy.

[0285] Compared to existing electric vehicle demand response control methods that do not consider weather and fulfillment differences, this invention incorporates weather information to correct vehicle arrival characteristics and combines dynamic fulfillment rate constraints to generate control schemes, ensuring that the control capacity matches the actual service capacity. Experimental results show that under different weather conditions, this invention can avoid a significant drop in the fulfillment rate caused by excessive control capacity, maintaining an overall fulfillment rate stably above approximately 60%. Furthermore, under typical control scales, the aggregator's net profit exhibits better stability than a fixed fulfillment rate assumption, thereby effectively reducing the risk of demand response execution failure and improving the reliability of electric vehicles participating in demand response. (Reference) Figure 4 As shown, the net profit of aggregators is compared under different performance assumptions when the control amount is 2000 kWh.

[0286] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for aggregated demand response of electric vehicles considering weather and fulfillment rate, characterized in that, include: The aggregator system receives demand response control instructions for electric vehicles issued by the power grid side. The demand response control instructions include the target control amount and the control time window. Obtain the weather for the target area, and determine the weather impact coefficient for the target area based on the pre-set correspondence between weather and weather impact coefficients; Calculate the arrival rate after taking weather impacts into account, based on the weather impact coefficient. Then calculate the maximum serviceable capacity. ; Arrival rate after taking into account weather effects Calculate the driving time of each electric vehicle. Total stay time This allows for the calculation of the electric vehicle's travel time cost and total dwell time cost, as well as the fixed costs incurred by the electric vehicle in participating in demand response during its journey to the charging station. ; Define the dynamic fulfillment rate, calculate the maximum responsive capacity, and calculate the comprehensive score of all electric vehicles in the target area based on the electric vehicle driving time cost, total stay time cost, maximum responsive capacity, and dynamic fulfillment rate. Then, filter the electric vehicles in the target area based on the comprehensive score to obtain a candidate electric vehicle set. Define the base incentive unit price based on the comprehensive scores of all candidate electric vehicles. Therefore, the additional incentive unit price is defined. Calculate the total incentive amount per unit for candidate electric vehicle t. Define the planned regulation of the electric power of candidate electric vehicle t. And calculate the effective charge and discharge power. and actual contracted electricity volume Simultaneously calculate the net benefit of candidate electric vehicle t in this demand response. ; Electricity is adjusted according to plan. Actual electricity volume fulfilled Total incentive amount per unit Fines for failure to fulfill contractual obligations Target-controlled power Effective charge and discharge power and maximum service capacity Construct the net profit target function and target constraints for aggregators; Based on the aggregator's net profit objective function and objective constraints, the particle swarm optimization algorithm is used to solve for the additional incentive unit price. Additional incentives for the power grid's intraday level four response The basic penalty amount for a Level IV response within a power grid within a day Elasticity coefficient Historical performance rate influencing parameters Parameters affecting state of charge This allows for the calculation of the aggregator's net profit within the control time window.

2. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Calculate the arrival rate after taking weather impacts into account, based on the weather impact coefficient. Specifically, it is calculated using the following formula: ； in, This characterizes the actual number of electric vehicles arriving at the j-th charging station per hour after taking into account weather conditions. Indicates the weather impact coefficient. This represents the average number of electric vehicles arriving at the j-th charging station per hour.

3. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Calculate the maximum serviceable capacity ,include: Arrival rate after taking into account weather effects The maximum service capacity of a charging station within the control time window is calculated using a queuing model. Specifically, this is achieved through the following formula: ； in, This refers to the number of fast charging stations at the charging stations. The service rate of a single fast charging station is given by h, which represents the length of the controllable time window; p represents the adjustable power that a single electric vehicle can provide per unit time.

4. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Calculate the driving time of each electric vehicle Total stay time ,include: Arrival rate after taking into account weather effects Calculate the utilization rate of charging stations Specifically, this is achieved through the following formula: ； in, This refers to the number of fast charging stations at the charging stations. Service rate for a single fast charging station; Arrival rate after taking into account weather effects and charging station utilization rate The total dwell time of electric vehicles is calculated, including: The equivalent business volume 'a' is calculated using the following formula: ； Calculate the idle probability of a charging station Specifically, this is achieved through the following formula: ； Where n is the number of electric vehicles being served at the charging station; Based on idle probability Calculate the waiting probability of electric vehicles Specifically, this is achieved through the following formula: ； Waiting probability based on electric vehicles Calculate the average waiting time Specifically, this is achieved through the following formula: ； Average waiting time Adding this to the service time gives the total dwell time of the electric vehicle. Specifically, it is expressed by the following formula: ； in, Indicates service hours; The driving time of electric vehicles is calculated based on the weather impact coefficient, including: The Underwood speed-density model is used to describe the road traffic conditions in the target area, specifically expressed by the following formula: ； in, In order to achieve a traffic density of Average driving speed at that time; For free flow velocity; Traffic density per unit road length; Congestion density; The calculation takes weather conditions into account for driving speed. Specifically, it is calculated using the following formula: ； Based on driving speed taking weather conditions into account Calculate the travel time of electric vehicle i from its current location to charging station j. Specifically, this is achieved through the following formula: ； in, For electric vehicles The driving distance between the charging station j and the charging station j.

5. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Calculate the driving time cost and total dwell time cost of the electric vehicle, and calculate the fixed costs incurred by the electric vehicle in participating in demand response during its journey to the charging station. ,include: Introducing a time value factor v, the driving time of electric vehicles is considered. Multiplying this by the time value coefficient v yields the electric vehicle's driving time cost, which in turn represents the total dwell time of the electric vehicle. Multiplying the time value coefficient v by the total dwell time cost of the electric vehicle yields the total dwell time cost. Adding the driving time cost and the total dwell time cost of the electric vehicle gives the total dwell time cost of the electric vehicle. Heading to the charging station Time cost ; Calculate the energy consumption cost of an electric vehicle during its operation. Specifically, this is achieved through the following formula: ； in, The unit price of electricity used for driving. Energy consumption per unit distance For electric vehicles The driving distance between the charging station j; The sum of time cost and energy cost is calculated and used as the fixed cost of electric vehicles participating in demand response. .

6. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, The dynamic fulfillment rate is expressed as: ； in, This represents the dynamic fulfillment rate of electric vehicle i within the target area. Indicates the parameters affecting historical performance rate. Indicates the parameters affecting the state of charge. This represents the actual historical response power. For historical planned response power, This represents the operating state function based on state of charge, available time window, and distance factors. Let represent the state of charge of the i-th electric vehicle. For electric vehicles The driving distance between the charging station j and the charging station j.

7. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Calculating the maximum responsive capacity includes: When the electric vehicle i is in discharge mode, the maximum responsive capacity is calculated using the following formula: ； in, Indicates the maximum responsive capacity of the discharge mode. Indicates discharge efficiency. For the battery capacity of candidate electric vehicle i, This indicates the state of charge of electric vehicle i after it arrives at the charging station. Indicates the minimum permissible state of charge; When the electric vehicle i is in charging mode, the maximum responsive capacity is calculated using the following formula: ； in, Indicates the maximum responsive capacity of the charging mode. Indicates charging efficiency. Indicates the highest permissible state of charge.

8. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Based on the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate, a comprehensive score is calculated for all electric vehicles within the target area. The electric vehicles within the target area are then filtered based on this comprehensive score to obtain a candidate electric vehicle set, including: For each electric vehicle, among the distances between the electric vehicle and each charging station, a first distance less than or equal to a distance threshold is obtained, and the charging station corresponding to the first distance is used as a candidate charging station. For each candidate charging station, based on the entropy weight method, the electric vehicle's driving time cost, total dwell time cost, maximum responsive capacity, and dynamic fulfillment rate are combined to obtain a score for the electric vehicle, and then multiple scores for the electric vehicle are obtained. The maximum value among all scores is selected as the comprehensive score of the electric vehicle, thus obtaining the comprehensive score for each electric vehicle. Among all the comprehensive scores, the comprehensive scores that are greater than or equal to the scoring threshold are obtained, and the corresponding electric vehicles are used as candidate electric vehicles. All candidate electric vehicles are combined into a candidate electric vehicle set.

9. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Define the base incentive unit price based on the comprehensive scores of all candidate electric vehicles. Therefore, the additional incentive unit price is defined. Calculate the total incentive amount per unit for candidate electric vehicle t. Define the planned regulation of the electric power of candidate electric vehicle t. And calculate the effective charge and discharge power. and actual contracted electricity volume Simultaneously calculate the net benefit of candidate electric vehicle t in this demand response. ,include: Define the global minimum basic stimulus starting point, which is expressed as: ； in, Indicates the minimum basic incentive starting point globally. Let t represent the set of candidate electric vehicles. For the variable costs related to the amount of electricity to be delivered, Fixed costs incurred during travel, waiting, charging, or discharging. The total penalty amount arising from failure to fully comply with the contract. Additional incentive unit price; Based on the comprehensive scores of all candidate electric vehicles, they are sorted in descending order to obtain the sorting index for each candidate electric vehicle. Then, the basic incentive unit price is defined, wherein the basic incentive unit price Represented as: ； in, The minimum excitation difference between adjacent candidate electric vehicles; The intensity factor is defined based on the urgency of the grid-side demand response. , is represented as: ； in, Characterizes the urgency of demand response on the grid side; According to the intensity factor Calculate the additional incentive unit price Specifically, this is achieved through the following formula: ； in, For the minimum additional incentive amount, The maximum additional incentive amount; Based on additional incentive unit price Define the elasticity coefficient of punishment relative to additional incentives. , is represented as: ； in, The additional penalty amount is represented as follows: ； in This is the base penalty amount for a Level 4 response within the power grid per day. This serves as an additional incentive for the power grid's intraday Level 4 response. Additional penalty amount Basic penalty amount for the power grid's level four response within a day Added together, the amount received was the penalty for the company's failure to fulfill its contractual obligations. The additional incentive unit price and basic incentive unit price Adding them together, we get the total incentive amount per unit for candidate electric vehicle t. ; Calculate the state of charge of the candidate electric vehicle after it arrives at the charging station , is represented as: ； in, This represents the initial state of charge of the candidate electric vehicle t. For power consumption per unit distance, Let be the distance between the candidate electric vehicle t and the candidate charging station l. Let t be the battery capacity of the candidate electric vehicle; Define the planned controlled power of candidate electric vehicle t. Specifically, when candidate electric vehicle t is in discharge mode, the planned controlled power... Represented as: ； in, Indicates the length of the control time window. Indicates discharge efficiency. Indicates the minimum permissible state of charge. Let t be the battery capacity of the candidate electric vehicle. The effective charging and discharging power, determined jointly by the candidate electric vehicle t and the connected charging station, can be calculated using the following formula: ； in, Let t be the maximum effective power of the candidate electric vehicle. The maximum effective power of the charging pile connected to the candidate electric vehicle t; When the candidate electric vehicle t is in charging mode, the power supply is planned to be adjusted. Represented as: ； in, For charging efficiency, Indicates the highest permissible state of charge; Planned power regulation and dynamic performance rate Multiplying these together, we obtain the actual delivered electric power of the candidate electric vehicle. The actual volume of electricity delivered will be Total incentive amount per unit Multiply by each other to get the total incentive amount. ; In the event that the candidate electric vehicle fails to fulfill its obligations, the electricity supply will be adjusted according to the plan. and the actual contracted electricity volume Determine the amount of electricity not fulfilled. Specifically, this is achieved through the following formula: ； Unfulfilled electricity Basic penalty amount for the power grid's level four response within a day Multiply by each other to get the total base penalty amount. Additional penalty amount and unfulfilled electricity volume Multiply by each other to get the total amount of additional penalties. The total amount of the basic penalty and the total amount of additional penalties Add them together to get the total penalty amount. ; Calculate the net benefit of candidate electric vehicle t in this demand response. Specifically, this is achieved through the following formula: 。 10. The electric vehicle aggregated demand response method considering weather and fulfillment rate according to claim 1, characterized in that, Electricity is adjusted according to plan. Actual electricity volume fulfilled Total incentive amount per unit Fines for failure to fulfill contractual obligations Target-controlled power Effective charge and discharge power and maximum service capacity Construct the aggregator's net profit objective function and objective constraints, including: Construct the net profit objective function for the aggregator, expressed as: ； in, This indicates the aggregator's net profit during the regulatory window. This indicates the price at which the power grid unit regulates and compensates. This indicates the unit price of the penalty paid by the aggregator to the power grid for breach of contract. Adjust capacity to meet targets; Construct target constraints, which include: ； ； ； ； ； ； ； ； ； in, This represents the maximum adjustable capacity of the candidate electric vehicle t. This indicates whether the candidate electric vehicle t participates in demand response. This indicates the maximum penalty amount per unit capacity. The minimum level of completion acceptable to the power grid, where h represents the length of the control time window. Indicates discharge efficiency. Indicates charging efficiency. Let t be the battery capacity of the candidate electric vehicle. This represents the initial state of charge of the candidate electric vehicle t. Indicates the minimum permissible state of charge. Indicates the highest permissible state of charge. Energy consumption per unit distance For the minimum additional incentive amount, Let t be the distance between the candidate electric vehicle t and the candidate charging station l.

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