CN114372606B - Short-term dispatch and response incentive method for EV aggregators considering road traffic model - Google Patents

Short-term dispatch and response incentive method for EV aggregators considering road traffic model Download PDF

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CN114372606B
CN114372606B CN202111479915.1A CN202111479915A CN114372606B CN 114372606 B CN114372606 B CN 114372606B CN 202111479915 A CN202111479915 A CN 202111479915A CN 114372606 B CN114372606 B CN 114372606B
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朱兰
王坤
丁雨佳
杨鑫宇
陈子墨
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Shanghai University of Electric Power
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Abstract

The invention relates to an EV aggregator short-time scheduling and response excitation method considering a road traffic model, which comprises the following steps: 1) Constructing a single EV short-time demand response model; 2) Considering various costs in a driving process and a charging and discharging process, and constructing a short-time demand response cost function of a single EV; 3) Constructing an EV target charging station selection model based on entropy weight scoring, and determining a selected target charging station; 4) Determining EV user basic incentive price optimization constraint conditions according to the EV scoring and improved user participation rate model, constructing an EV user basic incentive price optimization model according to the EV user basic incentive price optimization constraint conditions, and solving; 5) Taking uncertainty of the user response caused by the distance into consideration, and constructing an actual response quantity model of the user; 6) And determining short-time scheduling constraint conditions of the EV aggregators, constructing a short-time scheduling decision model of the EV aggregators according to the short-time scheduling constraint conditions, and solving the short-time scheduling decision model to obtain an optimal short-time scheduling decision scheme. Compared with the prior art, the invention has the advantages of comprehensive consideration, more fitting the actual situation and the like.

Description

EV (electric vehicle) aggregator short-time scheduling and response excitation method considering road traffic model
Technical Field
The invention relates to the field of electric vehicle optimized dispatching, in particular to an EV aggregator short-time dispatching and response excitation method considering a road traffic model.
Background
In recent years, the number of users of electric vehicles in China is rapidly increased, the electric vehicles have the characteristics of adjustable load and energy storage, and the electric vehicles can provide flexible demand response resources for an electric power system through ordered charge and discharge. The existing electric automobile dispatching and user response excitation method is mainly used for dispatching electric automobiles in the conventional conditions of the day before and the day in the past, and is mainly used for slowly charging and discharging the vehicles. When the electric power system faces emergency peak clipping and valley filling demands or even power failure accidents caused by faults, the electric vehicle is scheduled to the charging station to orderly perform high-power charging and discharging, so that quick, efficient, flexible and stable short-time demand response resources can be provided for the electric power system.
The electric quantity and time constraint of the EV to the charging station in the running process to participate in short-time scheduling are closely related to real-time road traffic information and the EV running process, and various costs generated in the running process and the charging and discharging process of the EV influence the benefits of an aggregate and the response willingness of a user. The existing model fails to comprehensively account for the influence of factors such as road conditions, distance, cost and income on the user psychology, so that an EV (electric vehicle) aggregator short-time scheduling strategy and a response excitation method of two-step pricing considering a road traffic model are required to be provided, the participation enthusiasm of the user is fully mobilized, and abundant and flexible short-time demand response resources are provided for a power grid.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a short-time scheduling and response incentive method for EV aggregators considering road traffic models.
The aim of the invention can be achieved by the following technical scheme: a step of
An EV aggregator short-time scheduling and response incentive method considering a road traffic model comprises the following steps:
1) Constructing a single EV short-time demand response model;
2) Considering various costs in a driving process and a charging and discharging process, and constructing a short-time demand response cost function of a single EV;
3) Constructing an EV target charging station selection model based on entropy weight scoring, and determining a selected target charging station;
4) Determining EV user basic incentive price optimization constraint conditions according to the EV scoring and improved user participation rate model, constructing an EV user basic incentive price optimization model according to the EV user basic incentive price optimization constraint conditions, and solving;
5) Taking uncertainty of the user response caused by the distance into consideration, and constructing an actual response quantity model of the user;
6) And determining short-time scheduling constraint conditions of the EV aggregators, constructing a short-time scheduling decision model of the EV aggregators according to the short-time scheduling constraint conditions, and solving the short-time scheduling decision model to obtain an optimal short-time scheduling decision scheme.
In the step 1), the single EV short-time demand response model includes a schedulable period model and a maximum schedulable capacity model, and the schedulable period model specifically includes:
Wherein T max,m,n is the flexibility of the scheduling period, T arrive,m,n and T leave,n are the earliest period and the latest period of departure of the nth EV in the charging station m, T is the total number of the scheduling periods, deltat is the length of each scheduling period, T A,m,n is the time of the nth EV to the mth charging station, T 0 and T e are the starting time of the first scheduling period and the ending time of the last scheduling period, T E,n is the response ending time of the user declaration, and the symbol Representing an upward rounding;
the maximum schedulable capacity model specifically comprises the following steps:
Wherein, Q max,m,n is the maximum schedulable capacity of the nth EV at the charging station m, Q max1,m,n、Qmax2,m,n is the maximum schedulable capacity of the nth EV at the charging station m in consideration of electric quantity constraint and in consideration of time constraint, SOC E,n、SOCS,n is the response end state of charge and the initial state of charge of the EV declared by the nth EV user, W n is the battery capacity of the vehicle n, P cmax,n、Pdcmax,n is the maximum charge and discharge power of the vehicle n, η c、ηd is the charge and discharge efficiency of the EV, Q D,m,n is the total electric power consumption in the process of driving the vehicle n to the charging station m, Q D,ij is the unit mileage driving energy consumption of the road section ij, d ij is the length of the road section ij, L ij represents the road section between the nodes i and j, and L mn is the shortest path of the nth EV to the charging station m.
In the step 2), the single EV short-time demand response cost function includes a fixed cost function generated in the driving process and a variable cost function generated in the charging and discharging process, and the fixed cost function specifically includes:
Wherein, C 1,m,n is the fixed cost of the nth EV to the charging station m to participate in the dispatching, C Q,m,n is the electricity consumption cost of the EV driving process, C T,m,n is the driving time cost, C wait,m,n is the waiting time cost of the arriving vehicle before the dispatching starts, α is the time value coefficient, p p and T p are the annual income and annual working time of the laborer, T D,m,n is the driving time of the vehicle n to the charging station m, T wait,m,n is the time spent by the vehicle n waiting when arriving at the charging station m in advance, and p 0 is the daily average electricity price;
the variable cost function specifically comprises the following steps:
Wherein C 2,m,n is the variable cost of EV participation scheduling, To participate in charging the charge rate costs of the EV users in response to charging, C DOD,m,n is the cost of battery life lost by a single discharge of the EV in response to discharging,To supplement the cost of the charged electric charge to the user after the EV discharge participating in the discharge response is finished, T start,n and T end,n are the actual schedule start and end periods of the vehicle n by the aggregator, respectively, and Q m,n (T) is the actual schedule capacity of the aggregator for the nth EV to the charging station m at the nth period when participating in the schedule.
In the step 3), the EV target charging station selection model based on the entropy weight scoring is specifically:
wherein, AndRespectively taking part in the standardization value, the initial value and the normalization result of the w index of the dispatching for the vehicle n to the charging station m, wherein the EV scoring index comprises the flexibility of the dispatching time period, the maximum schedulable capacity and the fixed cost of the user,For the weight of each index, e m,n is the score of the participation of vehicle n to charging station M in the dispatch, W is the total number of evaluation indexes, M is the number of charging stations under the management of the aggregator, and ζ m,n is a 0-1 variable representing that vehicle n selects charging station M as the target charging station.
In the step 4), the objective function expression of the EV user basic incentive price optimization model is as follows:
The constraint conditions include:
wherein, The upper and lower limit values of the user participation rate are respectively used for charging and discharging, the parameter r 1、h1、r2、h2 is a value obtained by the previous investigation and history data of the aggregator, c 1,n1、c1,n2 is the basic incentive price of vehicles n 1 and n 2, f n is the probability of the vehicle n participation response, the membership degree sigma is a parameter describing the influence of road condition differences of different dates and different time periods on the user participation rate, c 0 is the minimum difference value of basic incentive prices of two adjacent EV users set by the aggregator, f av is the average user participation rate set by the aggregator, k m is the EV number taking the m charging station as a target charging station,And the scores of the adjacent two electric vehicles n 1 and n 2 are respectively obtained after the electric vehicles are ranked from small to large.
In the step 5), the uncertainty of the user response caused by the distance is considered, and the expression of the actual response quantity model of the user is as follows:
wherein, The aggregator considers the actual response volume resulting from its response uncertainty simulation for when the vehicle n is actually selected to participate in the dispatch,The ratio of the actual insufficient response of the nth EV to the scheduling capacity of the aggregator is obtained by Monte Carlo sampling, Q Lmax,n is the upper limit, U [ ] represents uniform distribution, Q n (t) is the actual scheduling amount of the nth EV in the period t by the aggregator, D n is the shortest path distance of the vehicle n to the target charging station, D max is the maximum value of the shortest path distance of all the EVs which agree to participate in scheduling to the target charging station, and Q MR is the maximum ratio of the insufficient response of the user.
In the step 6), the objective function of the short-time scheduling decision model of the EV aggregator is:
wherein, AndThe profit expectations of the aggregator k, the expenditure expectations of the compensating users and the compensation expectations caused by the insufficient response capacity exceeding the prescribed margin are respectively,AndRespectively representing the income of the aggregation manufacturer k, the expenditure of the compensation user and the compensation caused by the insufficient response capacity exceeding the specified margin under the s-th simulation condition, wherein omega is the punishment of the power company to the insufficient response of the aggregation manufacturer, x l∈{x1,x2,...,xL is the first price in the L-grade additional incentive price set by the aggregation manufacturer, beta is the punishment coefficient of the insufficient response capacity of the user, F 1,n is the basic incentive of the user n,AndIndicating the penalty of responding to additional incentives and insufficient responses of the nth EV when price l is selected,Probability of selecting price/for user n whenThe probability of selecting the additional incentive price of the first file by the user obtained by the simulation of the aggregator is maximum, the value of the probability is p max, at the moment, the probability of selecting the rest incentive prices by the user is set to be equal, and the value of the probability is (1-p max)/(L-1).
In the step 6), constraint conditions of the short-time scheduling decision model of the EV aggregator are as follows:
wherein Q 0,n is the maximum response capacity in the nth EV single period Deltat, SOC n(tE,n) and SOC E,n are respectively the EV actual state of charge when the user response is ended and the response end state of charge requirement reported by the user, SOC (t) is the state of charge in the user t period, SOC min、SOCmax is the lower limit and the upper limit of the vehicle state of charge when charging and discharging respectively, N select is the final actual selection call user number of the aggregator, N a is the final user number who agrees to participate in the scheduling, mu k is the kth aggregator offer, The highest and lowest quotes for the aggregate, respectively, the values of which relate to the demand response level Φ, epsilon n is a 0-1 variable representing whether vehicle n is actually selected for invocation, Q SCmin is the minimum of actual dispatch capacity for invoked EVs specified to avoid that a user's actual dispatch capacity is low resulting in user profits well below expectations,For the response reliability of the aggregator k, P remin is the minimum requirement of the response reliability of the aggregator;
Reliability of response of the aggregator k The expression of (2) is:
wherein, The actual total response capacity obtained for the aggregator k simulation,For the winning capacity of the aggregator k, δ is the margin specified by the electric power company for allowing the aggregator to have insufficient response, N sim represents the simulation times, and N z is the Monte Carlo simulationIs a number of times (1).
The method further comprises the steps of:
7) And determining constraint conditions for clearing by the power company dispatching mechanism, constructing a power company dispatching optimization model and solving to obtain a power company dispatching optimization scheme.
In the step 7), the objective function of the power company dispatching optimization model is as follows:
The constraint conditions for clearing by the dispatching mechanism of the power company are as follows:
wherein mu MP is the highest bid among the bid successers, Q EC is the total capacity required by the electric company, and K is the total number of the aggregators participating in bidding.
Compared with the prior art, the invention has the following advantages:
the method comprehensively considers the influence of factors such as road conditions, distance, cost, income and the like on users in the design of the scheduling process and response incentive, accords with actual conditions and fully respects user wish, is favorable for mobilizing the participation enthusiasm of EV users and selecting and guiding better users to participate in scheduling, and can provide more abundant and flexible short-time demand response resources for the power grid while realizing the benefits of aggregators and users.
Drawings
FIG. 1 is a short demand response scheduling framework.
Fig. 2 is a road network architecture diagram of the aggregator C.
FIG. 3 is a graph of aggregate C net gain expectations and reliability as a function of bid capacity.
Fig. 4 shows the trend of call volume, response volume, and call EV volume with bidding capacity.
Fig. 5 is a graph of the schedulable EV number versus time period under three schemes.
Fig. 6 shows the trend of consumer incentive income expectations and charge electricity fee spending.
Fig. 7 is a schematic diagram of an EV schedule procedure numbered 27.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
Examples
The invention provides an EV (electric vehicle) aggregator short-time scheduling and response excitation method considering a road traffic model, which is characterized in that an electric vehicle short-time demand response model considering the road traffic model is established; considering the cost of a user driving process and a charging and discharging process, comprehensively considering response uncertainty caused by an electric automobile score, a user participation rate model and a distance, providing a short-time scheduling strategy of an electric automobile aggregator and designing a corresponding response excitation method of two-step pricing, and specifically comprising the following steps:
1) Acquiring road traffic information and EV position information, acquiring initial response willingness and vehicle information declared by EV users, and establishing a single EV short-time demand response model;
2) Considering various costs in a driving process and a charging and discharging process, and establishing a short-time demand response cost function of a single EV;
3) Scoring the process of participating in scheduling from EV to each charging station by adopting an entropy weight method and selecting a target charging station for the vehicle;
4) Establishing constraint conditions for basic incentive price optimization of the user according to the EV scoring and the improved user participation rate model;
5) Establishing an EV user basic incentive price optimization model, solving by adopting matlab+ cplex software, transmitting the basic incentive price obtained by optimization to a user, and guiding the user to re-declare a response intention;
6) Taking uncertainty of the user response caused by the distance into consideration, and establishing an actual response quantity model of the user;
7) Establishing short-time scheduling constraint conditions of EV aggregators;
8) Simulating the additional incentive price selection condition of a user, establishing a short-time scheduling decision model of the EV aggregator, and solving by adopting matlab+ cplex software to obtain a concrete scheduling plan and a bidding decision scheme of the aggregator;
9) Establishing constraint conditions for clearing by a dispatching mechanism of an electric company;
10 And (3) establishing a power company dispatching optimization model, and solving bidding results of a plurality of aggregators according to the information of each aggregator.
In the invention, the influence of a road traffic model on the cost of a user and the income of an aggregator is considered in the process of optimizing and dispatching, real-time road traffic information, EV position information and vehicle information reported by users willing to participate in dispatching are obtained, and a road resistance model of EV running is established, wherein the running time is taken as the road resistance; and establishing a single EV short-time demand response model on the basis. In step (1), EV travel speed v ij in link ij is calculated as shown in formula (1):
Wherein v 0 is zero flow speed of road grade corresponding to the road section, and the urban road in China is mainly divided into four grades of expressway, main road, secondary main road and branch road; k ij is the traffic density of the road section, and N ij is the number of vehicles traveling on the road section at the moment.
The road resistance model of EV driving is shown as (2):
Wherein t A,m,n is the time for the nth EV to reach the mth charging station, t S,m,n is the time for the nth EV to complete the contract and start driving to the charging station m, t D,m,n is the driving time for the nth EV to reach the charging station m, L ij represents the road section between the nodes i and j, t ij、dij and v ij are the average driving time for the road section ij, the road section length and the average driving speed of the road section EV, respectively, and L mn is the shortest path for the nth EV to reach the charging station m, which can be obtained by Dijkstra algorithm.
And establishing an EV short-time demand response model according to the initial response willingness declared by the EV user and the vehicle information, and calculating the schedulable time period and the maximum schedulable capacity from each EV to each charging station.
The schedulable time period model is shown in formula (3):
Wherein T max,m,n is the flexibility of the scheduling period, T arrive,m,n and T leave,n are the earliest period and the latest period of departure of the nth EV in charging station m, T is the total number of scheduling periods, deltat is the length of each scheduling period, T 0 and T e are the starting time of the first scheduling period and the ending time of the last scheduling period, T E,n is the response ending time of the user declaration, and the symbol is that Representing an upward rounding. If EV arrives before t 0, it can start charging and discharging at the beginning of the first scheduling period; if EV arrives after t 0, it can start charging and discharging at the beginning of the next period after arrival. Similarly, if the response ending time t E,n declared by the user is before t e, it is considered that the user can leave at the beginning of the period; if the response ending time t E,n declared by the user is after t e, the user is considered to leave at the beginning of the next period after the end of the scheduling, i.e. at the end of the last scheduling period.
The maximum schedulable capacity model is shown as (4):
Wherein, Q max,m,n is the maximum schedulable capacity of the nth EV at the charging station m, Q max1,m,n、Qmax2,m,n is the maximum schedulable capacity of the nth EV at the charging station m calculated by considering the electric quantity constraint and considering the time constraint, SOC E,n、SOCS,n is the response ending state of charge declared by the nth EV user and the initial state of charge of the EV, W n is the battery capacity of the vehicle n, P cmax,n、Pdcmax,n is the maximum charge and discharge power of the vehicle n, η c、ηd is the charge and discharge efficiency of the EV, Q D,m,n is the total electric consumption in the process of the vehicle n traveling to the charging station m, and Q D,ij is the unit mileage traveling energy consumption of the road section ij.
In step (2), a cost function model of each EV to each charging station participating in short-time demand response is established, including a fixed cost function generated during driving and a variable cost function generated during charging and discharging.
The single EV fixed cost function is shown as (5):
Wherein, C 1,m,n is the fixed cost of EV participation in the schedule, C Q,m,n is the electricity consumption cost of EV driving, C T,m,n is the driving time cost, C wait,m,n is the waiting time cost of arriving at the vehicle before the start of the schedule, α is the time value coefficient, p p and T p are the annual income and annual working time of the laborer, T wait,m,n is the time spent waiting when the vehicle n arrives at the charging station m in advance, and p 0 is the daily average electricity price.
The single EV variable cost function is shown in formula (6):
Wherein C 2,m,n is the variable cost of EV participation scheduling, To participate in charging the charge rate costs of the EV users in response to charging, C DOD,m,n is the cost of battery life lost by a single discharge of the EV in response to discharging,To supplement the cost of the charged electric charge to the user after the EV discharge participating in the discharge response is finished, T start,n and T end,n are the actual schedule start and end periods of the vehicle n by the aggregator, respectively, and Q m,n (T) is the actual schedule capacity of the aggregator for the nth EV to the charging station m at the nth period when participating in the schedule.
In the step (3), entropy weight scoring is carried out on the process of participating in scheduling from the EV to each charging station by taking the scheduling period flexibility, the maximum schedulable capacity and the user fixed cost as scoring indexes, and a target charging station is selected for the vehicle according to the scoring result. The EV scoring model based on the entropy weight method is shown in formula (7):
wherein, AndRespectively taking part in the standardization value, the initial value and the normalization result of the w index of the dispatching for the vehicle n to the charging station m,E m,n is the score of the participation schedule from the vehicle n to the charging station M for the weight of each index, W is the total number of evaluation indexes, and M is the number of charging stations under the management of an aggregator.
And selecting the largest non-zero value in the scores from each EV to each charging station as the final score of the EV, and taking the corresponding charging station as the target charging station. The EV target charging station selection model is shown in formula (8):
Where ζ m,n is a 0-1 variable representing the vehicle n selecting charging station m as the target charging station.
In step (4), an improved user participation rate model is established, and the basic incentive price optimization constraint conditions of the user are written according to the EV score, the improved user participation rate model and the average user participation rate, wherein the basic incentive price optimization constraint conditions are shown in a formula (9):
wherein, Respectively taking different values for charging and discharging by a parameter r 1、h1、r2、h2 according to the upper and lower limit values of the user participation rate, and obtaining the values through earlier investigation and historical data of an aggregator; c 1,n is the basic incentive price of the vehicle n, f n is the probability of participation response of the vehicle n, the membership sigma is a parameter describing the influence of road condition differences of different dates and different time periods on the user participation rate, c 0 is the minimum difference value of basic incentive prices of two adjacent EV users set by an aggregator, f av is the average user participation rate set by the aggregator, k m is the EV number of the m charging stations of the target charging station,And the scores of the adjacent two electric vehicles n 1 and n 2 are respectively obtained after the electric vehicles are ranked from small to large.
In step (5), solving an objective function expression of the EV user basic incentive price optimization model is shown as a formula (10). And sending the solved basic incentive price of each user to the EV user, and guiding the user to finally declare whether to participate in scheduling.
In step (6), taking the uncertainty of the user response caused by the distance into consideration, and building a user actual response quantity model as shown in a formula (11):
wherein, The actual response quantity obtained by simulating the response uncertainty of the vehicle n is considered by the aggregator when the vehicle n is actually selected to participate in scheduling; For the n-th EV to be affected by distance by the ratio of the actual insufficient response to the capacity scheduled by the aggregator, obtained by monte carlo sampling, Q Lmax,n is its upper limit; "U" represents a uniform distribution, Q n (t) is the final actual amount of scheduling by the aggregator for the nth EV in period t, D n is the shortest path distance for vehicle n to reach its target charging station, D max is the maximum of the shortest path distances for all EVs agreeing to participate in scheduling to reach their target charging station, and Q MR is the maximum proportion of insufficient user response.
In step (7), considering schedulable electric quantity and schedulable time period constraint, user electric quantity demand constraint, battery charge state constraint, user quantity constraint selection, price quotation constraint on an aggregator, scheduling capacity constraint considering user income and response reliability constraint, and establishing short-time scheduling constraint conditions of the EV aggregator as shown in formula (12):
Wherein, Q 0,n is the maximum response capacity in the nth EV single period Δt, SOC n(tE,n) and SOC E,n are respectively the EV actual state of charge when the user response is finished leaving and the response finishing state of charge requirement reported by the user, SOC (t) is the state of charge of the user t period, SOC min、SOCmax is the lower limit and upper limit of the vehicle state of charge when charging and discharging, N select is the final actual selection invoking user number of the aggregator, and N a is the final user number who agrees to participate in the scheduling; mu k offers (clearing prices in updating the actual decisions) for the kth aggregator; The highest and lowest quotations of the aggregate, respectively, the values of which are related to the demand response class Φ, are specified by the electric company; epsilon n is a 0-1 variable indicating whether vehicle n is actually selected to be invoked, Q SCmin specifies the minimum actual dispatch capacity of the invoked EV to avoid that a user's actual dispatch capacity is low, resulting in user profits well below expectations, For the response reliability of the aggregator k, P remin is the minimum requirement for the response reliability of the aggregator.
The maximum responsibilities Q 0n within the EV single period Δt in equation (12) are calculated as shown in equation (13):
The final actual selection of the number of call users N select by the aggregator in equation (12) is shown in equation (14):
where N is the total number of EV users of the initial declaration information.
Reliability of response of the aggregator k in equation (12)The definition is shown as a formula (15):
Wherein the method comprises the steps of The resulting total response capacity was simulated for the aggregator k,For the winning capacity of the aggregator k, δ is the margin specified by the electric power company for allowing the aggregator to have insufficient response, N sim represents the simulation times, and N z is the Monte Carlo simulationIs a number of times (1).
The aggregate quotient k in the formula (15) is simulated to obtain the total response capacityThe specific calculation of (2) is shown in the formula (16):
In step (8), the probability of the user to add incentive price selection to each file is calculated through simulation, a short-time scheduling decision model of the EV aggregator is built with the aim of the aggregate net benefit expectation maximization, and the objective function is shown as a formula (17):
wherein, AndThe profit expectations of the aggregator k, the expenditure expectations of the compensation users, and the compensation expectations due to insufficient response capacity beyond the prescribed margin, respectively.
The cost and benefit of each term in the objective function is shown in the formula (18):
And Respectively representing the income of the aggregation manufacturer k, the expenditure of the compensation user and the compensation caused by the insufficient response capacity exceeding the specified margin under the s-th simulation condition, wherein omega is the punishment of the power company to the insufficient response of the aggregation manufacturer, x l∈{x1,x2,...,xL is the first price in the L-grade additional incentive price set by the aggregation manufacturer, beta is the punishment coefficient of the insufficient response capacity of the user, F 1,n is the basic incentive of the user n,AndIndicating the penalty of responding to additional incentives and insufficient responses, respectively, for the nth EV when price l is selected.Probability of selecting price/for user n whenThe probability of selecting the additional incentive price of the first file by the user obtained by the simulation of the aggregator is maximum, and the value of the probability is p max; at this time, the user is set to have equal selection probability of the remaining incentive price, which is (1-p max)/(L-1).
In step (9), the constraint condition of the power company dispatching mechanism for clearing is as shown in formula (19):
wherein, Q EC is the total capacity required by the electric company, and K is the total number of the aggregators participating in bidding.
In the step (10), an electric power company dispatching optimization model is established, a plurality of aggregate bidding results are solved, and an objective function is shown as a formula (20):
Application instance
The short-time demand response scheduling framework is shown in fig. 1. Assuming that 5 EV aggregators A, B, C, D, E participate in bidding in the jurisdiction of the electric power company, the road nodes in the region where the EV aggregators are located are 36, 30, 34, 30 and 27, the charging stations are 5, 4 and 3, and the EVs reporting initial response will are 260, 240, 230, 215 and 200. Wherein the road network structure of the aggregator C is shown in fig. 2. Taking the charge response as an example for analysis, EV parameters set the reference bidi E6, the total schedule time is 1h, and Δt=5 min is one schedule period. The initial SOC of the EV, the SOC expected value at the departure time, and the response end time are all reported by the user. For the calculation of time cost, the alpha is 50%, the annual income of laborers is 2020, the incomes of all urban residents in the whole country can be 43834 yuan, and the annual working time of laborers is calculated according to 22 days of monthly work and 8 hours of daily work. The setting of the third gear additional incentive price is shown in table 1. The selection probability of the first price with the highest selection probability is set to p max =0.5. The remaining main parameters required for the calculation are shown in Table 2.
TABLE 1 additional incentive price setting
Table 2 main parameter description
The average participation rate of users of each aggregator was 0.8, and the membership σ=0.8. The respective aggregate bid schemes are shown in Table 3. According to the scale effect, the price of EV aggregators gradually decreases with the increase of the maximum schedulable capacity, and the ratio of the bidding capacity to the maximum schedulable capacity gradually decreases.
Table 3 EV Convergence bid schemes
Assuming that the demand capacity of the electric power company is 11.8 MW.h in this period, the actual winning bid results of each aggregator are shown in Table 4. It can be seen that the aggregator D, E is at risk of partial bid-winning and bid-unbiasing, respectively. When the aggregate is bid, the net gain of the actual decision is expected to be greatly improved compared with the bid decision, and the total scheduling capacity and EV number of the actual decision of the aggregate are slightly improved compared with the bid decision. This is because, for all winning aggregators A, B, C, since the bid price is higher than the bid price, the revenue enhancement per kW.h. of electricity is invoked, while the incentive pay per kW.h for each EV user is unchanged, the aggregators tend to increase the number of EV users who are not selected at bid decision making and are able to boost their own revenue at actual decision making. For some winning aggregators D, the net benefit of the actual decision is expected to be lower than the value at the time of bid decision, and the volume and EV number invoked at the time of actual decision are also significantly reduced. Therefore, it is necessary for the aggregator to adjust the scheduling plan based on the clearing results to obtain higher revenue.
Table 4 EV Convergence actual decision scheme
Take aggregator C as an example to analyze bid decisions for individual aggregators. As the bid amount of the aggregator increases from 2800kW.h to 3800 kW.h, the net revenue expectations and response reliability are shown in FIG. 3. It can be seen that as bid capacity increases, the reliability of the response of the aggregator tends to stay the same and then decrease rapidly, with the net profit expectations rising and decreasing. When the bid capacity reaches 3300kW.h, the net profit of the aggregator C is expected to be maximum and the response reliability is 1, which can be used as a reference value of the bid decision of the aggregator C.
As can be seen from fig. 4, the increase in the number of EV calls by the aggregator becomes gradually slower as the bidding capacity increases. When the aggregate bid capacity is lower than 3300kW.h, the aggregate total scheduling capacity, the expected value of the user actual response capacity and the bid capacity difference are basically unchanged with the increase of the bid capacity. When the bidding capacity exceeds 3300 kW.h, the difference between the total scheduling capacity of the aggregate and the bidding capacity is obviously reduced until the bidding capacity is reduced to a negative value; and the expected value of the actual response capacity of the user gradually becomes gradually smaller in increasing trend compared with the bidding capacity. This is because as bidding capacity increases, the aggregator has to choose some EVs with higher cost per capacity scheduling and lower schedulable capacity to participate in scheduling to avoid penalties of insufficient response beyond a prescribed margin; while the bid and penalty prices for the aggregate unit capacity are fixed, as bid capacity increases, the newly selected EVs increase revenue for the aggregate less and less compared to the increased payout costs, and thus the aggregate tends to increase the newly smaller call capacity and EV quantity for higher benefit.
In order to analyze the influence of charging station division and basic incentive price formulation on the dispatching result of the aggregation business, three schemes are designed for comparison analysis by taking the aggregation business C as an example.
Scheme one: and selecting target charging stations of each EV according to the principle of distance nearest, wherein the basic incentive prices of all users are the same, and adopting the average value of the basic incentive prices of all EV users in the third scheme.
Scheme II: the target charging stations for each EV are selected according to the distance-to-nearest principle, and a basic incentive price is formulated according to the vehicle-to-station distance and the improved user engagement rate model.
Scheme III: and selecting target charging stations of each EV by adopting the mode mentioned in section 3.1, namely scoring and selecting the target charging stations of each EV by adopting an entropy weight method, and formulating a basic incentive price according to each EV scoring result and an improved user participation rate model.
As can be seen from table 5, after the user is guided to declare the final response will through the basic incentive price, the total fixed cost spent by the scheme three is higher than that of the scheme two, but the schedulable capacity provided by the user who agrees to participate in scheduling is maximum, and the net income expected value and the corresponding bidding capacity which are finally obtained by the aggregator under the same quotation are also maximum; as can be seen from fig. 5, the scheme three has more schedulable EVs than the scheme one and two, and the distribution is more uniform in the whole scheduling time. This is because the adoption of entropy weight scoring for charging station division and the guidance of basic incentive prices allows EVs with larger schedulable capacity and more flexible schedulable time periods to have a greater probability of participation. In summary, the third scheme can provide better capacity and time flexibility for the subsequent optimized scheduling of the aggregator, so that the aggregator can obtain greater benefit finally, and is also beneficial to providing greater response capacity for the power grid.
Table 5 scheduling results under three schemes
Analysis was performed taking the aggregate C scheduling EV participation charge demand response as an example, and the response results of invoking 171 EV users under the optimal scheduling plan are shown in table 6. It can be seen that the average incentive income of each user is expected to be 105.34 yuan, which is much higher than the average charging expense of the user by 11.98 yuan; and the average value of the charge adjustment quantity of the aggregation business adjustment plan for each user is 19.96 kW.h, and the average value of the running energy consumption of the user is only 1.06 kW.h. Therefore, the incentive mechanism and the optimized scheduling strategy adopted in the method can better ensure the benefits of the users, promote the enthusiasm of the users, and also can fully utilize the response potential of the EV battery.
TABLE 6 EV response results to user angle
171 EVs are rearranged in order of small scheduling capacity, charging electric charge expenditure and incentive income of each EV user are expected to be ordered in the same EV order, and change trends and mutual relations are analyzed. As can be seen from FIG. 6, the maximum and minimum values expected by the user in the motivation income are 183.19 yuan and 52.71 yuan respectively, and the maximum and minimum values of the user in the charging electricity fee expenditure are 20.7 yuan and 6.05 yuan respectively; and at this time, the charge and electricity fee expenditure of each EV user increases with the rise of the dispatching capacity, and the overall change trend of the motivation income expectation of each user also rises with the rise, but locally fluctuates. This is because in the example the charge price is fixed, the user charge price expenditure is only proportional to the scheduling capacity, and the user incentive income expectations are influenced not only by the scheduling capacity, but also by the different incentive prices per unit capacity of each user, including different basic incentive prices and additional incentive prices. Therefore, the expected value of the incentive income obtained by the user participating in the scheduling is characterized in that the whole of the incentive income rises with the increase of the scheduling capacity and the local part fluctuates due to the difference of the incentive unit price.
Taking the original EV number 27 in the jurisdiction of the aggregator C as an example, the EV participation scheduling procedure is analyzed, as shown in FIG. 7. Setting the first scheduling period to start at 8:00 am, the utility company needs to reach the EV aggregator before 7:35 with scheduling notification, and the aggregator notifies EV users traveling within the jurisdiction.
And in the period of 7:35-7:45, reporting EV information by the user, selecting whether to confirm participation in scheduling according to the basic incentive price given after the optimization of the aggregator, simulating the additional incentive price selection condition of the user and the actual response uncertainty influence by the aggregator to perform scheduling policy optimization, transmitting a specific scheduling plan to the EV user, and enabling the user to select the additional incentive price and contract with the aggregator. The EV initial position of No. 27 is in node No. 1, the initial charge state is 0.35, the user response end time is 8:43 (namely, the user requests to leave before 8:43), the basic incentive price given by the aggregator is 2.78 yuan/kW.h, the scheduling capacity is 26.07 kW.h, and the target charging station is positioned in node No. 7. The EV starts at 7:45 and drives to a target charging station, the driving path is 1-2-7, the total distance is 4.95km, the driving energy consumption is 1.05 kW.h, and the driving time is 8.36min. Since the user arrives in advance, it is necessary to wait 7 minutes.
The user starts charging at 8:00 am, if the charging is carried out according to the schedule of the aggregator, the user can complete charging and leave at 8:40, the charge electricity fee expenditure is 15.64 yuan, and the obtained excitation income expected value is 135.36 yuan; if the user does not charge for the scheduled time and response capacity, the revenue obtained may be lower than expected.

Claims (4)

1.一种考虑道路交通模型的EV聚合商短时调度和响应激励方法，其特征在于，包括以下步骤：1. A short-term scheduling and response incentive method for EV aggregators considering a road traffic model, characterized by comprising the following steps: 1)构建单辆EV短时需求响应模型；1) Construct a short-term demand response model for a single EV; 2)考虑行驶过程及充放电过程的各类成本，构建单辆EV的短时需求响应成本函数；2) Considering various costs during the driving process and the charging and discharging process, a short-term demand response cost function for a single EV is constructed; 3)构建基于熵权法评分的EV目标充电站选择模型，确定选择的目标充电站；3) Construct an EV target charging station selection model based on entropy weight method scoring to determine the selected target charging station; 4)根据EV评分、改进用户参与率模型确定EV用户基本激励价格优化约束条件，并以此构建EV用户基本激励价格优化模型并求解；4) Determine the optimization constraints of the basic incentive price for EV users based on the EV score and the improved user participation rate model, and use this to build and solve the optimization model for the basic incentive price for EV users; 5)考虑距离引起用户响应不确定性，构建用户实际响应量模型；5) Considering the uncertainty of user response caused by distance, a model of the user's actual response quantity is constructed; 6)确定EV聚合商的短时调度约束条件，并以此构建EV聚合商的短时调度决策模型并求解，得到最优的短时调度决策方案；6) Determine the short-term dispatch constraints of EV aggregators, and use them to build and solve the short-term dispatch decision model of EV aggregators to obtain the optimal short-term dispatch decision plan; 所述的步骤1)中，单辆EV短时需求响应模型包括可调度时段模型与最大可调度容量模型，所述的可调度时段模型具体为：In the step 1), the short-term demand response model of a single EV includes a dispatchable period model and a maximum dispatchable capacity model. The dispatchable period model is specifically: 其中，Tmax,m,n为调度时段灵活度，Tarrive,m,n和Tleave,n分别为第n辆EV在充电站m能够开始充放电的最早时段和离开的最晚时段，T为调度时段总数，Δt为每个调度时段长度，tA,m,n为第n辆EV到达第m个充电站的时间，t0和te分别为第一个调度时段开始时间与最后一个调度时段结束时间，tE,n为用户申报的响应结束时间，符号表示向上取整；Where T max,m,n is the scheduling period flexibility, T arrive,m,n and T leave,n are the earliest time when the nth EV can start charging and discharging at charging station m and the latest time when it leaves, respectively, T is the total number of scheduling periods, Δt is the length of each scheduling period, t A,m,n is the time when the nth EV arrives at the mth charging station, t 0 and t e are the start time of the first scheduling period and the end time of the last scheduling period, respectively, t E,n is the response end time reported by the user, and the symbol Indicates rounding up; 所述的最大可调度容量模型具体为：The maximum dispatchable capacity model is specifically: 其中，Qmax,m,n为第n辆EV在充电站m的最大可调度容量，Qmax1,m,n、Qmax2,m,n分别为考虑电量约束和考虑时间约束的第n辆EV在充电站m的最大可调度容量，SOCE,n、SOCS,n分别为第n辆EV用户申报的响应结束荷电状态和EV的初始荷电状态，Wn为车辆n的电池容量，Pcmax,n、Pdcmax,n分别为车辆n的最大充、放电功率，ηc、ηd分别为EV的充、放电效率，QD,m,n为车辆n行驶至充电站m过程中的总耗电量，qD,ij为路段ij的单位里程行驶能耗，dij为路段ij的长度，lij表示节点i、j之间的路段，Lmn为第n辆EV到达充电站m的最短路径；Wherein, Q max,m,n is the maximum dispatchable capacity of the nth EV at charging station m, Q max1,m,n and Q max2,m,n are the maximum dispatchable capacities of the nth EV at charging station m considering power constraints and time constraints respectively, SOC E,n and SOC S,n are the response end state of charge declared by the user of the nth EV and the initial state of charge of the EV respectively, W n is the battery capacity of vehicle n, P cmax,n and P dcmax,n are the maximum charging and discharging powers of vehicle n respectively, η c and η d are the charging and discharging efficiencies of EV respectively, Q D,m,n is the total power consumption of vehicle n during its travel to charging station m, q D,ij is the energy consumption per unit mileage of section ij, d ij is the length of section ij, l ij represents the section between nodes i and j, and L mn is the shortest path for the nth EV to reach charging station m; 所述的步骤2)中，单辆EV短时需求响应成本函数包括行驶过程产生的固定成本函数和充放电过程产生的可变成本函数，所述的固定成本函数具体为：In the step 2), the short-term demand response cost function of a single EV includes a fixed cost function generated during the driving process and a variable cost function generated during the charging and discharging process. The fixed cost function is specifically: 其中，C1,m,n为第n辆EV到充电站m参与调度的固定成本，CQ,m,n为EV行驶过程的耗电成本，CT,m,n为行驶时间成本，Cwait,m,n为调度开始前到达车辆的等待时间成本，α为时间价值系数，pp与Tp分别为劳动者的年收入与年工作时间，tD,m,n为车辆n到充电站m的行驶时间，twait,m,n为车辆n提前到达充电站m时等待所花费的时间，p0为日平均电价；Where C 1,m,n is the fixed cost of the nth EV to participate in the dispatch at charging station m, C Q,m,n is the electricity consumption cost of the EV during driving, C T,m,n is the driving time cost, C wait,m,n is the waiting time cost of the vehicle arriving before the start of the dispatch, α is the time value coefficient, p p and T p are the annual income and annual working time of the worker respectively, t D,m,n is the driving time of vehicle n to charging station m, t wait,m,n is the waiting time spent by vehicle n when it arrives at charging station m in advance, and p 0 is the daily average electricity price; 所述的可变成本函数具体为：The variable cost function is specifically: 其中，C2,m,n为EV参与调度的可变成本，为参与充电响应EV用户的充电电费成本，CDOD,m,n为参与放电响应EV单次放电对电池寿命的损耗成本，为参与放电响应的EV放电结束后用户重新补充充电的电费成本，Tstart,n与Tend,n分别为聚合商对车辆n实际调度开始和结束时段，Qm,n(t)为第n辆EV到充电站m参与调度时聚合商在第t个时段对其实际调度容量；Among them, C 2,m,n is the variable cost of EV participating in scheduling, is the charging electricity cost of the EV users participating in the charging response, C DOD,m,n is the loss cost of the battery life caused by a single discharge of the EV participating in the discharge response, is the electricity cost of the user to recharge the EV after the EV participating in the discharge response has finished discharging. T start,n and T end,n are the actual start and end time periods of the aggregator's dispatch of vehicle n, respectively. Q m,n (t) is the actual dispatch capacity of the aggregator for the nth EV in the tth time period when the nth EV participates in the dispatch at the charging station m. 所述的步骤3)中，基于熵权法评分的EV目标充电站选择模型具体为：In the step 3), the EV target charging station selection model based on the entropy weight method scoring is specifically: 其中，和分别为车辆n到充电站m参与调度第w个指标的标准化值、初始值和归一化结果，EV评分指标包括调度时段灵活度、最大可调度容量和用户固定成本，为各指标的权重，em,n为车辆n到充电站m参与调度的评分，W为评价指标总数量，M为聚合商管理下的充电站数量，ξm,n为表示车辆n选择充电站m作为目标充电站的0-1变量；in, and are the standardized value, initial value and normalized result of the wth indicator of the vehicle n to the charging station m. The EV scoring indicators include the flexibility of the scheduling period, the maximum dispatchable capacity and the user's fixed cost. is the weight of each indicator, e m,n is the score of vehicle n participating in the dispatch to charging station m, W is the total number of evaluation indicators, M is the number of charging stations managed by the aggregator, ξ m,n is a 0-1 variable indicating that vehicle n chooses charging station m as the target charging station; 所述的步骤4)中，EV用户基本激励价格优化模型的目标函数表达式为：In the step 4), the objective function expression of the EV user basic incentive price optimization model is: 约束条件包括：The constraints include: 其中，分别为用户参与率的上、下限数值，参数r1、h1、r2、h2对充、放电分别为通过聚合商前期调研和历史数据得出的数值，c1,n1、c1,n2分别为车辆n1和n2的基本激励价格，fn为车辆n参与响应的概率，隶属度σ为描述不同日期、不同时段的路况差异对用户参与率的影响的参数，c0为聚合商设置的相邻两个EV用户基本激励价格的最小差值，fav为聚合商设置的用户平均参与率，km为以第m个充电站作为目标充电站的EV数量，分别为将电动汽车按评分由小到大排序后相邻两辆电动汽车n1和n2的评分；in, are the upper and lower limits of user participation rate, respectively; parameters r 1 , h 1 , r 2 , and h 2 are the values obtained through the aggregator's preliminary research and historical data for charging and discharging, respectively; c 1,n1 , c 1,n2 are the basic incentive prices of vehicles n 1 and n 2 , respectively; f n is the probability of vehicle n participating in the response; the membership degree σ is a parameter that describes the impact of road conditions on different dates and time periods on the user participation rate; c 0 is the minimum difference between the basic incentive prices of two adjacent EV users set by the aggregator; f av is the average user participation rate set by the aggregator; km is the number of EVs that use the mth charging station as the target charging station; are the scores of two adjacent electric vehicles n1 and n2 after sorting the electric vehicles in ascending order; 所述的步骤5)中，考虑距离引起用户响应不确定性，用户实际响应量模型的表达式为：In the step 5), considering the uncertainty of user response caused by distance, the expression of the user's actual response model is: 其中，为车辆n实际被选择参与调度时聚合商考虑其响应不确定性模拟得到的实际响应量，为第n辆EV受距离影响实际响应不足的量占聚合商对其调度容量的比例，通过蒙特卡洛抽样获得，QLmax,n是其上限，U[]表示均匀分布，Qn(t)为聚合商对于第n辆EV在t时段的实际调度量，Dn为车辆n到达其目标充电站的最短路径距离，Dmax为所有同意参与调度的EV到达其目标充电站最短路径距离的最大值，QMR为用户响应不足的最大比例；in, is the actual response amount obtained by the aggregator considering the uncertainty of its response when vehicle n is actually selected to participate in the dispatch, is the proportion of the actual under-response of the nth EV affected by distance to the aggregator's dispatch capacity, obtained by Monte Carlo sampling, Q Lmax,n is its upper limit, U[] represents uniform distribution, Q n (t) is the actual dispatch volume of the nth EV by the aggregator in time period t, D n is the shortest path distance of vehicle n to its target charging station, D max is the maximum value of the shortest path distance of all EVs that agree to participate in the dispatch to their target charging station, and Q MR is the maximum proportion of user under-response; 所述的步骤6)中，EV聚合商的短时调度决策模型的目标函数为：In step 6), the objective function of the short-term scheduling decision model of the EV aggregator is: 其中，和分别为聚合商k的收益期望、补偿用户的支出期望以及因响应容量不足超出规定裕度而带来的赔偿期望，和分别表示第s次模拟情况下聚合商k的收益、补偿用户的支出和因响应容量不足超出规定裕度而带来的赔偿，ω为电力公司对聚合商响应不足的惩罚，xl∈{x1,x2,...,xL}为聚合商设置的L档附加激励价格中的第l档价格，β为用户响应量不足的惩罚系数，F1,n为用户n的基本激励，和分别表示当选择价格l时第n辆EV的响应附加激励和响应不足的惩罚，为用户n选择价格l的概率，当聚合商模拟得到的用户对第l档附加激励价格的选择概率最大，其值为pmax，此时设定用户对其余激励价格的选择概率相等，其值为(1-pmax)/(L-1)。in, and are the expected revenue of aggregator k, the expected expenditure of compensating users, and the expected compensation due to insufficient response capacity exceeding the specified margin, and denote the revenue of aggregator k, the expenditure on compensating users and the compensation caused by insufficient response capacity exceeding the specified margin in the sth simulation case, ω is the penalty imposed by the power company on the insufficient response of the aggregator, x l ∈{x 1 ,x 2 ,...,x L } is the l-th price in the L-level additional incentive price set by the aggregator, β is the penalty coefficient for insufficient user response, F 1,n is the basic incentive for user n, and They represent the additional response incentive and insufficient response penalty of the nth EV when the price l is selected, The probability that user n chooses price l is The probability of users choosing the l-th additional incentive price obtained by the aggregator simulation is the largest, and its value is p max . At this time, the probability of users choosing the remaining incentive prices is set to be equal, and its value is (1-p max )/(L-1). 2.根据权利要求1所述的一种考虑道路交通模型的EV聚合商短时调度和响应激励方法，其特征在于，所述的步骤6)中，EV聚合商的短时调度决策模型的约束条件为：2. According to the method of short-term scheduling and response incentive of EV aggregators considering road traffic model in claim 1, it is characterized in that in the step 6), the constraint conditions of the short-term scheduling decision model of EV aggregators are: 其中，Q0,n为第n辆EV单个时段Δt内的最大可响应容量，SOCn(tE,n)与SOCE,n分别为用户响应结束离开时的EV实际荷电状态以及用户上报的响应结束荷电状态要求，SOC(t)为用户t时段的荷电状态，SOCmin、SOCmax分别为充放电时车辆荷电状态的下限与上限值，Nselect为聚合商最终实际选择调用用户数，Na为最终同意参与调度的用户数，μk为第k个聚合商报价，分别为聚合商的最高和最低报价，其值与需求响应等级Φ有关，εn为表示车辆n是否实际被选择调用的0-1变量，QSCmin为避免某一用户实际调度容量较低导致用户收益远低于预期而规定的被调用EV的实际调度容量最小值，为聚合商k的响应可靠度，Premin为聚合商的响应可靠度最低要求；Where Q 0,n is the maximum responsive capacity of the nth EV in a single period Δt, SOC n (t E,n ) and SOC E,n are the actual state of charge of the EV when the user leaves after responding and the state of charge requirement reported by the user at the end of the response, respectively, SOC(t) is the state of charge of the user in period t, SOC min and SOC max are the lower and upper limits of the vehicle state of charge during charging and discharging, respectively, N select is the number of users actually selected by the aggregator, Na is the number of users who finally agree to participate in the scheduling, μ k is the price quoted by the kth aggregator, are the highest and lowest bids of the aggregator, respectively, and their values are related to the demand response level Φ. εn is a 0-1 variable indicating whether vehicle n is actually selected for call. QSCmin is the minimum value of the actual dispatch capacity of the called EV to avoid a user's actual dispatch capacity being too low, resulting in the user's revenue being far lower than expected. is the response reliability of aggregator k, Premin is the minimum requirement for the response reliability of the aggregator; 所述的聚合商k的响应可靠度的表达式为：The response reliability of the aggregator k The expression is: 其中，为聚合商k模拟得到的实际总响应容量，为聚合商k的中标容量，δ为电力公司规定的允许聚合商存在响应不足的裕度，Nsim表示模拟次数，Nz为蒙特卡洛模拟中的次数。in, is the actual total response capacity simulated by aggregator k, is the winning bid capacity of aggregator k, δ is the margin allowed by the power company for insufficient response of aggregators, N sim is the number of simulations, and N z is the number of The number of times. 3.根据权利要求2所述的一种考虑道路交通模型的EV聚合商短时调度和响应激励方法，其特征在于，该方法还包括以下步骤：3. The method for short-term scheduling and response incentive of EV aggregators considering road traffic models according to claim 2, characterized in that the method further comprises the following steps: 7)确定电力公司调度机构进行出清的约束条件，并构建电力公司调度优化模型并求解，得到电力公司调度优化方案。7) Determine the constraints for the clearing of the power company's dispatching organization, build and solve the power company's dispatching optimization model, and obtain the power company's dispatching optimization plan. 4.根据权利要求3所述的一种考虑道路交通模型的EV聚合商短时调度和响应激励方法，其特征在于，所述的步骤7)中，电力公司调度优化模型的目标函数为：4. The method for short-term dispatch and response incentive of EV aggregators considering road traffic model according to claim 3 is characterized in that, in the step 7), the objective function of the power company dispatch optimization model is: μMP＝max{μ1，μ2，...，μK}μ MP =max{μ 1 , μ 2 ,..., μ K } 电力公司调度机构进行出清的约束条件为：The constraints for the power company dispatching agency to clear are: 其中，μMP为竞价成功者中的最高报价，为聚合商k的投标容量，QEC为电力公司的需求总容量，K为参与投标竞价的聚合商总数。Among them, μ MP is the highest bid among the successful bidders. is the bidding capacity of aggregator k, QEC is the total demand capacity of the power company, and K is the total number of aggregators participating in the bidding.
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