CN114372606A - EV aggregator short-time scheduling and response excitation method considering road traffic model - Google Patents

EV aggregator short-time scheduling and response excitation method considering road traffic model Download PDF

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CN114372606A
CN114372606A CN202111479915.1A CN202111479915A CN114372606A CN 114372606 A CN114372606 A CN 114372606A CN 202111479915 A CN202111479915 A CN 202111479915A CN 114372606 A CN114372606 A CN 114372606A
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response
user
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scheduling
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朱兰
王坤
丁雨佳
杨鑫宇
陈子墨
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Shanghai Electric Power University
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Abstract

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

Description

EV aggregator short-time scheduling and response excitation method considering road traffic model
Technical Field
The invention relates to the field of optimized scheduling of electric vehicles, in particular to an EV aggregator short-time scheduling and response excitation method considering a road traffic model.
Background
In recent years, the number of electric automobile users in China is rapidly increased, the electric automobile has the characteristics of adjustable load and energy storage, and flexible demand response resources can be provided for a power system through ordered charging and discharging. Most of the existing electric vehicle dispatching and user response incentive methods aim at electric vehicle dispatching under the conventional conditions in the day-ahead and day-in, and mainly aim at slow charging and slow discharging of vehicles. When an electric power system faces a relatively urgent peak clipping and valley filling requirement and even a power failure accident caused by a fault, the electric vehicle is dispatched to a charging station to carry out orderly high-power charging and discharging, so that a quick, efficient, flexible and relatively stable short-time demand response resource can be provided for the electric power system.
The electric quantity and the time constraint of the short-time scheduling of the EV to the charging station in the running process are closely related to the real-time road traffic information and the running process of the EV, and all the costs generated by the running process and the charging and discharging process of the EV have influence on the benefits of aggregators and the response willingness of users. The influence of factors such as road conditions, distances, cost and income on the psychology of a user cannot be comprehensively considered by the existing model, so that an EV aggregator short-time scheduling strategy considering a road traffic model and a two-step pricing response incentive method need 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 an EV aggregator short-time scheduling and response incentive method considering a road traffic model.
The purpose of the invention can be realized by the following technical scheme: a
An EV aggregator short-time dispatch and response incentive method considering road traffic models, comprising the steps of:
1) constructing a single EV short-time demand response model;
2) considering various costs of a driving process and a charging and discharging process, constructing a short-time demand response cost function of a single EV;
3) establishing an EV target charging station selection model based on entropy weight method scoring, and determining a selected target charging station;
4) determining the EV user basic incentive price optimization constraint condition according to the EV scoring and improved user participation rate model, and constructing and solving an EV user basic incentive price optimization model according to the EV scoring and improved user participation rate model;
5) considering the uncertainty of user response caused by the distance, and constructing a user actual response quantity model;
6) and determining a short-time scheduling constraint condition of the EV aggregator, constructing a short-time scheduling decision model of the EV aggregator according to the short-time scheduling constraint condition, and solving 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:
Figure BDA0003394895290000021
wherein, Tmax,m,nFor scheduling period flexibility, Tarrive,m,nAnd Tleave,nThe earliest time interval that the nth EV can start charging and discharging at the charging station m and the latest time interval that the nth EV leaves are respectively, T is the total number of scheduling time intervals, delta T is the length of each scheduling time interval, TA,m,nTime of arrival of the nth EV at the mth charging station, t0And teRespectively a first scheduling period start time and a last scheduling period end time, tE,nResponse end time, symbol declared for user
Figure BDA0003394895290000022
Represents rounding up;
the maximum schedulable capacity model is specifically as follows:
Figure BDA0003394895290000023
wherein Q ismax,m,nMaximum schedulable capacity, Q, for the nth EV at charging station mmax1,m,n、Qmax2,m,nMaximum schedulable capacity, SOC, of the nth EV at charging station m taking into account the electric quantity constraint and taking into account the time constraint, respectivelyE,n、SOCS,nEnd of response SOC and initial SOC of EV, W, reported for nth EV usernIs the battery capacity, P, of the vehicle ncmax,n、Pdcmax,nMaximum charge and discharge power, η, of the vehicle nc、ηdCharge and discharge efficiencies, Q, of EV, respectivelyD,m,nFor the total power consumption of the vehicle n during its travel to the charging station m, qD,ijEnergy consumption per mileage for a section ij, dijIs the length of the section ij, lijRepresenting a link between nodes i, j, LmnThe nth EV is the shortest path to the charging station m.
In the step 2), the short-time demand response cost function of the single EV includes a fixed cost function generated in a driving process and a variable cost function generated in a charging and discharging process, and the fixed cost function specifically includes:
Figure BDA0003394895290000031
wherein, C1,m,nFixed cost of participating in scheduling for nth EV to charging station m, CQ,m,nCost of electricity consumption for EV driving process, CT,m,nFor travel time costs, Cwait,m,nCost of waiting time for arriving vehicles before the start of dispatch, α being the time value coefficient, ppAnd TpThe annual income and annual working time, t, of the laborersD,m,nFor the travel time of the vehicle n to the charging station m, twait,m,nFor the time it takes for the vehicle n to wait in advance at the charging station m, p0Is the daily average electricity price;
the variable cost function is specifically as follows:
Figure BDA0003394895290000032
wherein, C2,m,nFor the variable cost of EV participation in the schedule,
Figure BDA0003394895290000033
responding to EV user's charging electricity cost for participation in charging, CDOD,m,nTo participate in the cost of a single discharge of the discharge response EV versus the loss of battery life,
Figure BDA0003394895290000034
recharging the charge cost, T, for the user after the end of the EV discharge in the discharge responsestart,nAnd Tend,nActual scheduling of a vehicle n for a start and end period, Q, respectively, for an aggregatorm,n(t) the aggregator actually schedules capacity for the nth EV to charging station m at the t-th time period while participating in the scheduling.
In the step 3), the entropy weight method scoring-based EV target charging station selection model specifically includes:
Figure BDA0003394895290000041
Figure BDA0003394895290000042
wherein the content of the first and second substances,
Figure BDA0003394895290000043
and
Figure BDA0003394895290000044
respectively taking part in dispatching the normalized value, the initial value and the normalized result of the w index from the vehicle n to the charging station m, wherein the EV scoring indexes comprise dispatching time period flexibility, maximum dispatching capacity and user fixed cost,
Figure BDA0003394895290000045
as a weight of each index, em,nGrading the vehicles n to the charging stations M to participate in scheduling, wherein W is the total number of evaluation indexes, M is the number of the charging stations under management of the aggregator, and xim,nA charge station m is selected as a 0-1 variable for representing the target charge station for vehicle n.
In the step 4), the objective function expression of the basic incentive price optimization model of the EV user is as follows:
Figure BDA0003394895290000046
the constraint conditions include:
Figure BDA0003394895290000051
wherein the content of the first and second substances,
Figure BDA0003394895290000052
the upper limit value and the lower limit value of the user participation rate and the parameter r are respectively1、h1、r2、h2The charging and discharging are respectively the numerical values obtained by earlier investigation and historical data of the aggregators, c1,n1、c1,n2Are respectively vehicles n1And n2Basic incentive price of fnThe probability of the vehicle n participating in the response, the membership degree sigma is a parameter describing the influence of road condition differences on the user participation rate on different dates and different time periods, and c0Minimum difference, f, between two adjacent EV users' basic incentive prices set for the aggregatoravAverage participation rate of users, k, set for aggregatorsmFor the number of EVs with the mth charging station as the target charging station,
Figure BDA0003394895290000053
respectively sorting two adjacent electric vehicles n according to grades from small to large1And n2The score of (1).
In the step 5), the uncertainty of the user response caused by the distance is considered, and the expression of the user actual response quantity model is as follows:
Figure BDA0003394895290000054
wherein the content of the first and second substances,
Figure BDA0003394895290000061
the aggregator simulates the actual response quantity by considering the response uncertainty of the vehicle n when the vehicle n is actually selected to participate in the dispatching,
Figure BDA0003394895290000062
the actual response shortage of the nth EV influenced by the distance accounts for the proportion of the aggregator to the scheduling capacity of the nth EV, and the actual response shortage is obtained by Monte Carlo samplingLmax,nIs its upper limit, U [, ]]Denotes a uniform distribution, Qn(t) is the actual deployment amount of the aggregator for the nth EV during t, DnFor the shortest path distance for vehicle n to reach its target charging station, DmaxFor all EVs agreeing to participate in the scheduling to reach the maximum value of their target charging station shortest path distance, QMRThe maximum proportion of user response insufficiency.
In the step 6), an objective function of the short-time scheduling decision model of the EV aggregator is as follows:
Figure BDA0003394895290000063
Figure BDA0003394895290000064
wherein the content of the first and second substances,
Figure BDA0003394895290000065
and
Figure BDA0003394895290000066
revenue expectation, compensation user expense expectation and response tolerance for the aggregator k respectivelyThe expectation of compensation caused by the shortage of the amount beyond the specified margin,
Figure BDA0003394895290000067
and
Figure BDA0003394895290000068
respectively representing the income of an aggregator k, the expenditure of a compensation user and the compensation due to the fact that the response capacity is insufficient and exceeds a specified margin under the condition of the s-th simulation, wherein omega is the punishment of the power company on the response insufficiency of the aggregator, and xl∈{x1,x2,...,xLThe price of the L-th level in the L-level additional incentive prices set by the aggregators, beta is a penalty coefficient for insufficient user response, and F1,nFor the basic motivation of the user n,
Figure BDA0003394895290000071
and
Figure BDA0003394895290000072
respectively representing the penalty of responding to additional incentives and to insufficient responses for the nth EV when the price/is selected,
Figure BDA0003394895290000073
probability of selecting price l for user n when
Figure BDA0003394895290000078
The maximum selection probability of the user to the additional incentive price of the first file is obtained by the aggregator simulation, and the value is pmaxAt this time, the selection probability of the user for the rest of incentive prices is set to be equal, and the value is (1-p)max)/(L-1)。
In the step 6), the constraint conditions of the short-time scheduling decision model of the EV aggregator are as follows:
Figure BDA0003394895290000074
wherein Q is0,nFor the maximum responsive capacity, SOC, of the nth EV within a single time period Δ tn(tE,n) And SOCE,nRespectively responding to EV actual state of charge when the user leaves and responding to the end state of charge requirement reported by the user, wherein SOC (t) is the state of charge of the user at t time period, and SOCmin、SOCmaxRespectively the lower limit and the upper limit of the vehicle state of charge during charging and discharging, NselectThe number of calling users, N, is finally and practically selected for the aggregatoraNumber of users, mu, for eventual consent to participate in schedulingkThe price is quoted for the k-th aggregator,
Figure BDA0003394895290000075
respectively the highest and lowest quotes for the aggregator, whose value is related to the demand response level phi, epsilonnA variable of 0-1, Q, to indicate whether vehicle n is actually selected for invocationSCminTo avoid the user revenue being far below the expected minimum value of the actual scheduling capacity of the invoked EV due to a low actual scheduling capacity of a certain user,
Figure BDA0003394895290000076
reliability of response for the aggregation quotient k, PreminResponse reliability minimum requirement for the aggregators;
the response reliability of the aggregation quotient k
Figure BDA0003394895290000077
The expression of (a) is:
Figure BDA0003394895290000081
Figure BDA0003394895290000082
wherein the content of the first and second substances,
Figure BDA0003394895290000083
the actual total response capacity obtained is simulated for the aggregate quotient k,
Figure BDA0003394895290000084
for the bid amount of the aggregator k, δ is the margin specified by the utility that allows the aggregator to have an insufficient response, NsimRepresenting the number of simulations, NzFor in Monte Carlo simulations
Figure BDA0003394895290000085
The number of times.
The method further comprises the following steps:
7) and determining constraint conditions for clearing by a 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 electric power company scheduling optimization model is as follows:
Figure BDA0003394895290000086
Figure BDA0003394895290000087
the constraint conditions for clearing by the dispatching mechanism of the power company are as follows:
Figure BDA0003394895290000088
wherein, muMPFor the highest bid among the successful bidders,
Figure BDA0003394895290000089
bid capacity, Q, for aggregator kECK is the total number of aggregators participating in the bid for the total capacity demanded by the utility company.
Compared with the prior art, the invention has the following advantages:
the method comprehensively considers the influence of road conditions, distances, cost, benefits and other factors on users in the design of the scheduling process and response incentive, better accords with the actual situation, fully respects the user desire, is beneficial to mobilizing the participation enthusiasm of EV users and selecting and guiding better users to participate in scheduling, and can provide 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-term demand response scheduling framework.
Fig. 2 is a road network structure diagram of the aggregator C.
FIG. 3 is a graph of aggregator C net profit expectancy and reliability as a function of bid capacity.
FIG. 4 is a graph of the variation of the amount of modulation, the amount of response, and the number of invoked EVs with bid capacity.
Fig. 5 shows the comparison of the number of scheduled EVs in each period under three schemes.
Fig. 6 shows a trend of a user incentive income expectation and a charging electricity fee expenditure variation.
Fig. 7 is a schematic diagram of the EV scheduling process numbered 27.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention provides an EV aggregator short-time scheduling and response excitation method considering a road traffic model, which comprises the steps of establishing an electric automobile short-time demand response model considering the road traffic model; considering the cost of a user in a driving process and a charging and discharging process, comprehensively considering the response uncertainty caused by the electric vehicle score, a user participation rate model and a distance, providing a short-time scheduling strategy of an electric vehicle aggregator, and designing a two-step pricing response incentive method corresponding to the short-time scheduling strategy, wherein the two-step pricing response incentive method specifically comprises the following steps of:
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 of a driving process and a charging and discharging process, establishing a short-time demand response cost function of a single EV;
3) grading the process of scheduling from the 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 users according to the EV scoring and improved user participation rate model;
5) establishing an EV user basic incentive price optimization model, solving by adopting matlab + cplex software, sending the optimized basic incentive price to a user, and guiding the user to report a response intention again;
6) considering the uncertainty of user response caused by the distance, establishing a user actual response quantity model;
7) establishing a short-time scheduling constraint condition of an EV aggregator;
8) simulating the condition of selecting additional incentive prices of users, 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 bidding decision scheme of the aggregator;
9) establishing a constraint condition for clearing by a dispatching mechanism of an electric power company;
10) and establishing a scheduling optimization model of the power company, and solving bidding results of a plurality of aggregators according to the information of each aggregator.
The method comprises the steps of considering the influence of a road traffic model on user cost and aggregator profit in the process of optimizing scheduling, acquiring real-time road traffic information, EV (electric vehicle) position information and vehicle information reported by users who intentionally participate in scheduling, establishing an EV-driving road resistance model, and taking driving time as road resistance; and establishing a single EV short-time demand response model on the basis. In step (1), the EV traveling speed v in the link ijijCalculating as shown in formula (1):
Figure BDA0003394895290000101
wherein v is0The urban road in China is mainly divided into four levels of an express way, a main road, a secondary road and a branch for the zero flow speed of the road level corresponding to the road section; k is a radical ofijFor the traffic density of the section, NijThe number of vehicles running on the road section at the moment.
The road resistance model of EV driving is shown as the formula (2):
Figure BDA0003394895290000102
wherein, tA,m,nTime of arrival of the nth EV at the mth charging station, tS,m,nTime t for completing contract signing for nth EV and starting to drive to charging station mD,m,nFor the travel time from the nth EV to the charging station m, lijRepresenting the road section between nodes i, j, tij、dijAnd vijThe average travel time, the length of the link and the average travel speed, L, of the link ijmnThe shortest path for the nth EV to reach charging station m may be derived by Dijkstra's 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 from each EV to each charging station and the maximum schedulable capacity.
The schedulable period model is shown as equation (3):
Figure BDA0003394895290000103
wherein, Tmax,m,nFor scheduling period flexibility, Tarrive,m,nAnd Tleave,nThe earliest time interval that the nth EV can start charging and discharging at the charging station m and the latest time interval that the nth EV leaves are respectively, T is the total number of scheduling time intervals, delta T is the length of each scheduling time interval, T0And teRespectively a first scheduling period start time and a last scheduling period end time, tE,nResponse end time, symbol declared for user
Figure BDA0003394895290000104
Indicating rounding up. If EV is at t0If the current time reaches the preset time, the charging and discharging can be started at the beginning of the first scheduling period; if EV is at t0And when the charge reaches later, the charge and the discharge can be started in the next period after the charge and the discharge reach later. Similarly, if the user declares the response ending time tE,nAt tePreviously, it was considered to beThe time period can be left at the beginning; response end time t if user declaresE,nAt teAnd later, the user is considered to leave at the beginning of the next period after the end of the scheduling, namely the end of the last scheduling period.
The maximum schedulable capacity model is shown as equation (4):
Figure BDA0003394895290000111
wherein Q ismax,m,nMaximum schedulable capacity, Q, for the nth EV at charging station mmax1,m,n、Qmax2,m,nMaximum schedulable capacity, SOC, of the nth EV at the charging station m calculated considering the electric quantity constraint and considering the time constraint respectivelyE,n、SOCS,nEnd of response SOC and initial SOC of EV, W, reported for nth EV usernIs the battery capacity, P, of the vehicle ncmax,n、Pdcmax,nMaximum charge and discharge power, η, of the vehicle nc、ηdCharge and discharge efficiencies, Q, of EV, respectivelyD,m,nFor the total power consumption of the vehicle n during its travel to the charging station m, qD,ijThe unit mileage driving energy consumption of the road section ij.
In the step (2), a cost function model of each EV participating in the short-time demand response to each charging station is established, wherein the cost function model comprises a fixed cost function generated in the driving process and a variable cost function generated in the charging and discharging process.
The fixed cost function for a single EV is shown in equation (5):
Figure BDA0003394895290000112
wherein, C1,m,nFixed cost for EV participation scheduling, CQ,m,nCost of electricity consumption for EV driving process, CT,m,nFor travel time costs, Cwait,m,nCost of waiting time for arriving vehicles before the start of dispatch, α being the time value coefficient, ppAnd TpRespectively the annual income and the annual working time of the laborers，twait,m,nFor the time it takes for the vehicle n to wait in advance at the charging station m, p0The daily average electricity price.
The single EV variable cost function is shown in equation (6):
Figure BDA0003394895290000121
wherein, C2,m,nFor the variable cost of EV participation in the schedule,
Figure BDA0003394895290000122
responding to EV user's charging electricity cost for participation in charging, CDOD,m,nTo participate in the cost of a single discharge of the discharge response EV versus the loss of battery life,
Figure BDA0003394895290000123
recharging the charge cost, T, for the user after the end of the EV discharge in the discharge responsestart,nAnd Tend,nActual scheduling of a vehicle n for a start and end period, Q, respectively, for an aggregatorm,n(t) the aggregator actually schedules capacity for the nth EV to charging station m at the t-th time period while participating in the scheduling.
In the step (3), the scheduling process from the EV to each charging station is subjected to entropy weight method scoring by taking the scheduling time period flexibility, the maximum scheduling capacity and the user fixed cost as scoring indexes, and a target charging station is selected for the vehicle according to a scoring result. The entropy weight method-based EV scoring model is shown as a formula (7):
Figure BDA0003394895290000124
wherein the content of the first and second substances,
Figure BDA0003394895290000125
and
Figure BDA0003394895290000126
participating in scheduling w-th indexes from vehicle n to charging station m respectivelyThe normalized value, the initial value and the normalized result,
Figure BDA0003394895290000127
as a weight of each index, em,nAnd scoring the vehicles n to the charging stations M to participate in scheduling, wherein W is the total number of the evaluation indexes, and M is the number of the charging stations managed by the aggregator.
And selecting the maximum nonzero value from the scores of each EV to each charging station as the final score of the EV, wherein the corresponding charging station is the target charging station. The EV target charging station selection model is shown in equation (8):
Figure BDA0003394895290000131
wherein ξm,nA charge station m is selected as a 0-1 variable for representing the target charge station for vehicle n.
In the step (4), an improved user participation rate model is established, and the user basic incentive price optimization constraint conditions are written according to the EV score, the improved user participation rate model and the user average participation rate, wherein the constraint conditions are shown as the formula (9):
Figure BDA0003394895290000132
wherein the content of the first and second substances,
Figure BDA0003394895290000133
the upper limit value and the lower limit value of the user participation rate and the parameter r are respectively1、h1、r2、h2Respectively taking different values of charge and discharge, and obtaining the values through earlier-stage investigation and historical data of a aggregator; c. C1,nFor the base incentive price of vehicle n, fnThe probability of the vehicle n participating in the response, the membership degree sigma is a parameter describing the influence of road condition differences on the user participation rate on different dates and different time periods, and c0Minimum difference, f, between two adjacent EV users' basic incentive prices set for the aggregatoravAverage participation rate of users, k, set for aggregatorsmTo the eyesThe target charging station is the EV number of the mth charging station,
Figure BDA0003394895290000134
respectively sorting two adjacent electric vehicles n according to grades from small to large1And n2The score of (1).
In the step (5), the target function expression of the basic incentive price optimization model of the EV user is solved as shown in the formula (10). And sending the solved basic incentive price of each user to the EV user to guide the user to finally declare whether to participate in scheduling.
Figure BDA0003394895290000141
In step (6), considering uncertainty of user response caused by distance, establishing a user actual response quantity model as shown in formula (11):
Figure BDA0003394895290000142
wherein the content of the first and second substances,
Figure BDA0003394895290000143
the method comprises the steps that an aggregator considers actual response quantity obtained by simulating response uncertainty of the aggregator when a vehicle n is actually selected to participate in scheduling;
Figure BDA0003394895290000144
the actual response shortage of the nth EV influenced by the distance accounts for the proportion of the aggregator to the scheduling capacity of the nth EV, and the actual response shortage is obtained by Monte Carlo samplingLmax,nIs the upper limit thereof; "U" denotes uniform distribution, Qn(t) is the final actual deployment amount of the aggregator for the nth EV over time t, DnFor the shortest path distance for vehicle n to reach its target charging station, DmaxFor all EVs agreeing to participate in the scheduling to reach the maximum value of their target charging station shortest path distance, QMRThe maximum proportion of user response insufficiency.
In step (7), considering schedulable electric quantity and schedulable time period constraints, user electric quantity demand constraints, battery state of charge constraints, user quantity selection constraints, aggregator reporting price constraints, scheduling capacity constraints considering user profits and response reliability constraints, and establishing a short-time scheduling constraint condition of the EV aggregator as shown in formula (12):
Figure BDA0003394895290000145
wherein Q is0,nFor the maximum responsive capacity, SOC, of the nth EV within a single time period Δ tn(tE,n) And SOCE,nRespectively responding to EV actual state of charge when the user leaves and responding to the end state of charge requirement reported by the user, wherein SOC (t) is the state of charge of the user at t time period, and SOCmin、SOCmaxRespectively the lower limit and the upper limit of the vehicle state of charge during charging and discharging, NselectThe number of calling users, N, is finally and practically selected for the aggregatoraThe number of users who finally agree to participate in scheduling; mu.skQuote for the kth aggregator (clearing price when updating the actual decision);
Figure BDA0003394895290000151
Figure BDA0003394895290000152
the highest and lowest quotes for the aggregator, respectively, whose values are related to the demand response level Φ, specified by the electric company; epsilonnIs a 0-1 variable, Q, indicating whether vehicle n is actually selected for invocationSCminTo avoid the user revenue being far below the expected minimum value of the actual scheduling capacity of the invoked EV due to a low actual scheduling capacity of a certain user,
Figure BDA0003394895290000153
reliability of response for the aggregation quotient k, PreminThe response reliability of the aggregators is the minimum requirement.
Maximum responsive capacity Q within a single period Δ t of EV in equation (12)0nCalculation is as shown in equation (13)：
Figure BDA0003394895290000154
The final actual selection of the number of calling users N by the aggregator in equation (12)selectIs calculated as shown in equation (14):
Figure BDA0003394895290000155
where N is the total number of EV users that initially declared the information.
Reliability of response of the aggregation quotient k in the formula (12)
Figure BDA0003394895290000156
The definition is shown in formula (15):
Figure BDA0003394895290000157
wherein
Figure BDA0003394895290000158
The resulting total response capacity is modeled for the aggregate quotient k,
Figure BDA0003394895290000159
for the bid amount of the aggregator k, δ is the margin specified by the utility that allows the aggregator to have an insufficient response, NsimRepresenting the number of simulations, NzFor in Monte Carlo simulations
Figure BDA00033948952900001510
The number of times.
The aggregate quotient k in the formula (15) is simulated to obtain the total response capacity
Figure BDA00033948952900001515
Is as shown in equation (16):
Figure BDA00033948952900001511
in step (8), the probability of adding incentive price selection to each gear by the user is calculated in a simulation mode, a short-time scheduling decision model of the EV aggregator is established with the maximum aggregator net profit expectation target, and the objective function is shown as the formula (17):
Figure BDA00033948952900001512
wherein the content of the first and second substances,
Figure BDA00033948952900001513
and
Figure BDA00033948952900001514
respectively for the revenue expectation of the aggregator k, the expenditure expectation of the compensation user and the compensation expectation due to the response capacity shortage exceeding the specified margin.
The cost and yield of each term in the objective function is shown as equation (18):
Figure BDA0003394895290000161
Figure BDA0003394895290000162
and
Figure BDA0003394895290000163
respectively representing the income of an aggregator k, the expenditure of a compensation user and the compensation due to the fact that the response capacity is insufficient and exceeds a specified margin under the condition of the s-th simulation, wherein omega is the punishment of the power company on the response insufficiency of the aggregator, and xl∈{x1,x2,...,xLThe price of the L-th level in the L-level additional incentive prices set by the aggregators, beta is a penalty coefficient for insufficient user response, and F1,nFor the basic motivation of the user n,
Figure BDA0003394895290000164
and
Figure BDA0003394895290000165
respectively representing the penalty for responding to additional incentives and for responding insufficiently to the nth EV when the price/is selected.
Figure BDA0003394895290000166
Probability of selecting price l for user n when
Figure BDA0003394895290000169
The maximum selection probability of the user to the additional incentive price of the first file is obtained by the aggregator simulation, and the value is pmax(ii) a At this time, the selection probability of the user to the rest incentive prices is set to be equal and is (1-p)max)/(L-1)。
In step (9), the constraint condition for clearing by the dispatching organization of the electric power company is shown as formula (19):
Figure BDA0003394895290000167
wherein the content of the first and second substances,
Figure BDA0003394895290000168
bid capacity, Q, for aggregator kECK is the total number of aggregators participating in the bid for the total capacity demanded by the utility company.
In step (10), a power company scheduling optimization model is established, and a plurality of aggregator bidding results are solved, wherein an objective function of the aggregator bidding results is shown as formula (20):
Figure BDA0003394895290000171
examples of the applications
The short-term demand response scheduling framework of the present invention is shown in FIG. 1. Suppose that 5 EV aggregators A, B, C, D, E participate in the bidding in the jurisdiction of the electric power company, and the number of road nodes in the district where the EV aggregators are located is 36, 30, 34, 30 and 27 respectivelyThe number of charging stations is 5, 4, and 3, and the number of EVs reporting the initial willingness to respond is 260, 240, 230, 215, and 200, respectively. The road network structure of the aggregator C is shown in fig. 2. Taking the charging response as an example for analysis, the EV parameter setting refers to biddie 6, the total scheduling time is 1h, and Δ t ═ 5min is used as a scheduling period. The initial SOC of the EV, the expected SOC value at the time of departure, and the response end time are obtained by user declaration. For the calculation of time cost, alpha is 50%, the annual income of workers is 43834 yuan which can be dominated by urban residents all over the country in 2020, and the annual working time of the workers is calculated according to 22 days of work per month and 8 hours of work per day. The additional incentive price for setting third gear is shown in table 1. The selection probability of the first-gear price with the highest user selection probability is set as pmax0.5. The other main parameters required for the calculation are shown in Table 2.
TABLE 1 additional incentive price settings
Figure BDA0003394895290000173
Table 2 description of main parameters
Figure BDA0003394895290000172
The average participation rate of the users of each aggregator was 0.8, and the membership degree σ was 0.8. The bidding scenarios for each aggregator are shown in table 3. According to the scale effect, the price of the EV aggregator is gradually reduced along with the increase of the maximum schedulable capacity, and the ratio of the bid capacity to the maximum schedulable capacity is also gradually reduced.
TABLE 3 EV aggregator bidding scenario
Figure BDA0003394895290000181
Assuming that the power company has a required capacity of 11.8MW · h in this period, the actual winning results of each aggregator are shown in Table 4. It can be seen that aggregator D, E is at risk for partial winning and losing bids, respectively. When the aggregator makes full bid, the net income expectation of the actual decision is greatly improved compared with that of the bidding decision, and the total scheduling capacity and the number of EVs of the aggregator are slightly improved compared with that of the bidding decision. This is because, for all winning bid aggregators A, B, C, the earnings are increased by calling the electricity amount per kW · h because the clearing price is higher than the quote, and the incentive fee per kW · h for each EV user is not changed, so that the aggregators tend to add some EV users who are not selected for bidding decision but can increase their own earnings for actual decision. And for the partially winning aggregator D, the net income expectation in the actual decision is lower than the value in the bidding decision, and the calling capacity and the number of EVs in the actual decision are also obviously reduced. Therefore, it is necessary for the aggregator to adjust the scheduling plan according to the clearing result to obtain higher profit.
TABLE 4 EV aggregator actual decision scheme
Figure BDA0003394895290000182
The bidding decisions of a single aggregator are analyzed, taking aggregator C as an example. When the bidding capacity of the aggregator was increased from 2800 kW. multidot.h to 3800 kW. multidot.h, the net profit expectancy and the response reliability are shown in FIG. 3. It can be seen that as bid capacity increases, the aggregator response reliability shows a tendency to stay the same first and then decline rapidly, with the net revenue expectation increasing first and then declining. When the bidding capacity reaches 3300kW · h, the net profit expectation of the aggregator C is maximum and the response reliability is 1, which can be used as a reference value for the aggregator C bidding decision.
As can be seen from fig. 4, the number of aggregator-invoked EVs rises gradually and slowly as bid capacity increases. When the bidding capacity of the aggregator is lower than 3300kW · h, the difference between the total scheduling capacity of the aggregator and the expected value of the actual response capacity of the user and the bidding capacity is substantially constant as the bidding capacity increases. When the bidding capacity exceeds 3300kW & h, the difference between the total scheduling capacity of the aggregator and the bidding capacity is obviously reduced until the value is reduced to a negative value; and the expected value of the user's actual response capacity gradually becomes more gradual as compared with the bid capacity. This is because as bid capacity increases, the aggregator has to choose some EVs to participate in scheduling that have higher scheduling cost per unit capacity and lower schedulable capacity to avoid responding with a penalty of insufficient excess of the specified margin; while the quote and penalty prices per unit capacity of the aggregator remain the same, as bid capacity increases, the new entry of EVs increases the revenue to the aggregator less and less than the increased expenditure cost, and therefore the aggregator tends to add less call capacity and number of EVs to pursue higher profitability.
In order to analyze the influence of charging station division and basic incentive price formulation on the adjustment result of the aggregator, taking the aggregator C as an example, three schemes are designed for comparative analysis.
The first scheme is as follows: and selecting target charging stations of all EVs according to a nearest principle, wherein the basic incentive prices of all users are the same, and the average value of the basic incentive prices of all the EV users in the third scheme is adopted.
Scheme II: and selecting target charging stations of each EV according to a distance nearest principle, and making a basic incentive price according to the vehicle-station distance and the improved user participation rate model.
The third scheme is as follows: and (3) selecting a target charging station of each EV by adopting a mode mentioned in the section 3.1, namely grading by adopting an entropy weight method, and formulating a basic incentive price according to a grading result of each EV and an improved user participation rate model.
As can be seen from table 5, after the user is guided to declare the final willingness to respond by the basic incentive price, the total fixed cost spent by the user in the third scheme is higher than that of the second scheme, but the schedulable capacity provided by the user who agrees to participate in the scheduling in the third scheme is the maximum, and the net revenue expectation value finally obtained by the aggregator and the corresponding bid capacity under the same quotation are also the maximum; as can be seen from fig. 5, the scheme three has more scheduled EVs than the scheme one and the scheme two in each time period, and the scheduled EVs are distributed more evenly in the whole scheduling time. This is because charging station division and guidance of the basic incentive price using entropy weight scoring allows for a greater probability of participation for EVs with greater schedulable capacity and more flexible schedulable period. In conclusion, the third scheme can provide better capacity and time flexibility for subsequent optimization scheduling of the aggregator, so that the aggregator obtains higher benefits, and the third scheme is also beneficial to providing higher response capacity for the power grid.
TABLE 5 scheduling results under three schemes
Figure BDA0003394895290000191
Analysis is performed by taking the example that the aggregator C schedules EVs to participate in charging demand response, and the response result of calling 171 EV users under the optimal scheduling plan is shown in table 6. It can be seen that the average incentive revenue for each user is expected to be 105.34 dollars, which is much higher than the average charging cost expenditure of 11.98 dollars for the user; and the average value of the charge scheduling amount of the aggregator scheduling plan for each user is 19.96 kW.h, while the average value of the user driving energy consumption is only 1.06 kW.h. Therefore, the incentive mechanism and the optimized scheduling strategy adopted by the method can better ensure the user income, improve the user participation enthusiasm and more fully utilize the EV battery response potential.
TABLE 6 EV user-Angle response results
Figure BDA0003394895290000201
The 171 EVs are rearranged according to the descending order of the dispatching capacity, the charging electricity expense and the incentive income expectation of each EV user are sorted according to the same EV order, and the change trend and the relationship among the EVs are analyzed. As can be seen from fig. 6, the maximum and minimum values of the expected user incentive income are 183.19 yuan and 52.71 yuan, respectively, and the maximum and minimum values of the user charging electricity fee expenditure are 20.7 yuan and 6.05 yuan, respectively; at this time, the charging electricity charge expenditure of each EV user increases as the scheduling capacity increases, and the general trend of change of the incentive income expectation of each user also increases, but fluctuates locally. This is because the charging electricity price is fixed in the calculation example, the user charging electricity price is only in proportion to the scheduling capacity, and the user incentive income expectation is influenced not only by the scheduling capacity but also by 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 revenue obtained by the participation of the user in the scheduling is characterized in that the expected value rises as the scheduling capacity increases and fluctuates locally due to the difference of incentive unit prices.
Taking an EV with the original number of 27 in the jurisdiction of aggregator C as an example, the EV participation scheduling process is analyzed, as shown in fig. 7. Setting the first schedule period to begin at 8:00 am requires the utility to reach the EV aggregator with a schedule notice before 7:35 am and be notified by the aggregator to EV users traveling within the jurisdiction.
In the period of 7:35-7:45, the user reports EV information, whether the user is confirmed to participate in scheduling is selected according to the basic incentive price given by the aggregator after optimization, the aggregator simulates the additional incentive price selection condition and the actual response uncertainty influence of the user to optimize a scheduling strategy and sends a specific scheduling plan to the EV user, and the user selects the additional incentive price and completes contract signing with the aggregator. EV 27 initial position at node No. 1, initial state of charge 0.35, end of user response time 8:43 (i.e. user request to leave before 8: 43), aggregator gives a base incentive price of 2.78 yuan/kW · h, dispatch capacity 26.07kW · h, target charging station at node No. 7. The EV starts from a 7:45 ratio and drives to a target charging station, the driving path is 1-2-7, the whole process is 4.95km totally, the driving energy consumption is 1.05 kW.h, and the driving time is 8.36 min. Since the user arrives in advance, it is necessary to wait 7 min.
The user starts charging at 8:00 am, if the charging is carried out according to the scheduling plan of the aggregation quotient, the user can finish charging at 8:40 and leave, the charging electricity fee is paid as 15.64 yuan, and the expected value of the obtained incentive income is 135.36 yuan; if the user does not charge according to the scheduled time and response capacity, the revenue obtained may be lower than expected.

Claims (10)

1. An EV aggregator short-time dispatch and response incentive method considering road traffic models, characterized by comprising the steps of:
1) constructing a single EV short-time demand response model;
2) considering various costs of a driving process and a charging and discharging process, constructing a short-time demand response cost function of a single EV;
3) establishing an EV target charging station selection model based on entropy weight method scoring, and determining a selected target charging station;
4) determining the EV user basic incentive price optimization constraint condition according to the EV scoring and improved user participation rate model, and constructing and solving an EV user basic incentive price optimization model according to the EV scoring and improved user participation rate model;
5) considering the uncertainty of user response caused by the distance, and constructing a user actual response quantity model;
6) and determining a short-time scheduling constraint condition of the EV aggregator, constructing a short-time scheduling decision model of the EV aggregator according to the short-time scheduling constraint condition, and solving to obtain an optimal short-time scheduling decision scheme.
2. The EV aggregator short-time dispatching and response incentive method considering road traffic models as claimed in claim 1, wherein in the step 1), the single EV short-time demand response model comprises a dispatchable period model and a maximum dispatchable capacity model, and the dispatchable period model is specifically as follows:
Figure FDA0003394895280000011
wherein, Tmax,m,nFor scheduling period flexibility, Tarrive,m,nAnd Tleave,nThe earliest time interval that the nth EV can start charging and discharging at the charging station m and the latest time interval that the nth EV leaves are respectively, T is the total number of scheduling time intervals, delta T is the length of each scheduling time interval, TA,m,nTime of arrival of the nth EV at the mth charging station, t0And teRespectively a first scheduling period start time and a last scheduling period end time, tE,nResponse end time, symbol declared for user
Figure FDA0003394895280000012
Represents rounding up;
the maximum schedulable capacity model is specifically as follows:
Figure FDA0003394895280000021
wherein Q ismax,m,nMaximum schedulable capacity, Q, for the nth EV at charging station mmax1,m,n、Qmax2,m,nMaximum schedulable capacity, SOC, of the nth EV at charging station m taking into account the electric quantity constraint and taking into account the time constraint, respectivelyE,n、SOCS,nEnd of response SOC and initial SOC of EV, W, reported for nth EV usernIs the battery capacity, P, of the vehicle ncmax,n、Pdcmax,nMaximum charge and discharge power, η, of the vehicle nc、ηdCharge and discharge efficiencies, Q, of EV, respectivelyD,m,nFor the total power consumption of the vehicle n during its travel to the charging station m, qD,ijEnergy consumption per mileage for a section ij, dijIs the length of the section ij, lijRepresenting a link between nodes i, j, LmnThe nth EV is the shortest path to the charging station m.
3. The EV aggregator short-time dispatching and response incentive method considering road traffic models as claimed in claim 2, wherein in the step 2), the single EV short-time demand response cost function comprises a fixed cost function generated in a driving process and a variable cost function generated in a charging and discharging process, and the fixed cost function is specifically as follows:
Figure FDA0003394895280000022
wherein, C1,m,nFixed cost of participating in scheduling for nth EV to charging station m, CQ,m,nCost of electricity consumption for EV driving process, CT,m,nFor travel time costs, Cwait,m,nCost of waiting time for arriving vehicles before the start of dispatch, α being the time value coefficient, ppAnd TpThe annual income and annual working time, t, of the laborersD,m,nFor the travel time of the vehicle n to the charging station m, twait,m,nFor vehicle n to arrive ahead of timeTime spent waiting at charging station m, p0Is the daily average electricity price;
the variable cost function is specifically as follows:
Figure FDA0003394895280000031
wherein, C2,m,nFor the variable cost of EV participation in the schedule,
Figure FDA0003394895280000032
responding to EV user's charging electricity cost for participation in charging, CDOD,m,nTo participate in the cost of a single discharge of the discharge response EV versus the loss of battery life,
Figure FDA0003394895280000033
recharging the charge cost, T, for the user after the end of the EV discharge in the discharge responsestart,nAnd Tend,nActual scheduling of a vehicle n for a start and end period, Q, respectively, for an aggregatorm,n(t) the aggregator actually schedules capacity for the nth EV to charging station m at the t-th time period while participating in the scheduling.
4. The EV aggregator short-time dispatching and response incentive method considering road traffic models as claimed in claim 3, wherein in the step 3), the EV target charging station selection model based on entropy weight method scoring is specifically as follows:
Figure FDA0003394895280000034
Figure FDA0003394895280000041
wherein the content of the first and second substances,
Figure FDA0003394895280000042
and
Figure FDA0003394895280000043
respectively taking part in dispatching the normalized value, the initial value and the normalized result of the w index from the vehicle n to the charging station m, wherein the EV scoring indexes comprise dispatching time period flexibility, maximum dispatching capacity and user fixed cost,
Figure FDA0003394895280000044
as a weight of each index, em,nGrading the vehicles n to the charging stations M to participate in scheduling, wherein W is the total number of evaluation indexes, M is the number of the charging stations under management of the aggregator, and xim,nA charge station m is selected as a 0-1 variable for representing the target charge station for vehicle n.
5. The EV aggregator short-time dispatching and response incentive method considering road traffic models as claimed in claim 4, wherein in the step 4), the objective function expression of the EV user basic incentive price optimization model is as follows:
Figure FDA0003394895280000045
the constraint conditions include:
Figure FDA0003394895280000046
wherein the content of the first and second substances,
Figure FDA0003394895280000051
the upper limit value and the lower limit value of the user participation rate and the parameter r are respectively1、h1、r2、h2The charging and discharging are respectively the numerical values obtained by earlier investigation and historical data of the aggregators, c1,n1、c1,n2Are respectively vehicles n1And n2Basic incentive price of fnFor vehicles nProbability of participating in response, membership degree sigma is a parameter describing influence of road condition difference on user participation rate on different dates and different time periods, and c0Minimum difference, f, between two adjacent EV users' basic incentive prices set for the aggregatoravAverage participation rate of users, k, set for aggregatorsmFor the number of EVs with the mth charging station as the target charging station,
Figure FDA0003394895280000052
respectively sorting two adjacent electric vehicles n according to grades from small to large1And n2The score of (1).
6. The EV aggregator short-time dispatching and response incentive method considering road traffic models as claimed in claim 5, wherein in the step 5), considering the uncertainty of user response caused by distance, the expression of the user actual response quantity model is as follows:
Figure FDA0003394895280000053
wherein the content of the first and second substances,
Figure FDA0003394895280000054
the aggregator simulates the actual response quantity by considering the response uncertainty of the vehicle n when the vehicle n is actually selected to participate in the dispatching,
Figure FDA0003394895280000055
the actual response shortage of the nth EV influenced by the distance accounts for the proportion of the aggregator to the scheduling capacity of the nth EV, and the actual response shortage is obtained by Monte Carlo samplingLmax,nIs its upper limit, U [, ]]Denotes a uniform distribution, Qn(t) is the actual deployment amount of the aggregator for the nth EV during t, DnFor the shortest path distance for vehicle n to reach its target charging station, DmaxFor all EVs agreeing to participate in the scheduling to reach the maximum value of their target charging station shortest path distance, QMRResponding insufficiently for usersTo the maximum ratio of (c).
7. The EV aggregator RTD and response incentive method considering road traffic models as in claim 6, wherein in the step 6), the objective function of the EV aggregator RTD decision model is as follows:
Figure FDA0003394895280000056
Figure FDA0003394895280000061
wherein the content of the first and second substances,
Figure FDA0003394895280000062
and
Figure FDA0003394895280000068
respectively for the revenue expectation of the aggregator k, the expenditure expectation of the compensation user and the compensation expectation due to the response capacity shortage exceeding the specified margin,
Figure FDA0003394895280000063
and
Figure FDA0003394895280000064
respectively representing the income of an aggregator k, the expenditure of a compensation user and the compensation due to the fact that the response capacity is insufficient and exceeds a specified margin under the condition of the s-th simulation, wherein omega is the punishment of the power company on the response insufficiency of the aggregator, and xl∈{x1,x2,...,xLThe price of the L-th level in the L-level additional incentive prices set by the aggregators, beta is a penalty coefficient for insufficient user response, and F1,nFor the basic motivation of the user n,
Figure FDA0003394895280000065
and
Figure FDA0003394895280000066
respectively representing the penalty of responding to additional incentives and to insufficient responses for the nth EV when the price/is selected,
Figure FDA0003394895280000067
probability of selecting price l for user n when
Figure FDA0003394895280000069
The maximum selection probability of the user to the additional incentive price of the first file is obtained by the aggregator simulation, and the value is pmaxAt this time, the selection probability of the user for the rest of incentive prices is set to be equal, and the value is (1-p)max)/(L-1)。
8. The EV aggregator RTV method considering road traffic model as claimed in claim 7, wherein in step 6), constraint conditions of the EV aggregator RTV decision model are as follows:
Figure FDA0003394895280000071
wherein Q is0,nFor the maximum responsive capacity, SOC, of the nth EV within a single time period Δ tn(tE,n) And SOCE,nRespectively responding to EV actual state of charge when the user leaves and responding to the end state of charge requirement reported by the user, wherein SOC (t) is the state of charge of the user at t time period, and SOCmin、SOCmaxRespectively the lower limit and the upper limit of the vehicle state of charge during charging and discharging, NselectThe number of calling users, N, is finally and practically selected for the aggregatoraNumber of users, mu, for eventual consent to participate in schedulingkThe price is quoted for the k-th aggregator,
Figure FDA0003394895280000072
respectively the highest and lowest quotes of the aggregatorThe value being related to the demand response level phi, epsilonnA variable of 0-1, Q, to indicate whether vehicle n is actually selected for invocationSCminTo avoid the user revenue being far below the expected minimum value of the actual scheduling capacity of the invoked EV due to a low actual scheduling capacity of a certain user,
Figure FDA0003394895280000073
reliability of response for the aggregation quotient k, PreminResponse reliability minimum requirement for the aggregators;
the response reliability of the aggregation quotient k
Figure FDA0003394895280000074
The expression of (a) is:
Figure FDA0003394895280000075
Figure FDA0003394895280000076
wherein the content of the first and second substances,
Figure FDA0003394895280000077
the actual total response capacity obtained is simulated for the aggregate quotient k,
Figure FDA0003394895280000078
for the bid amount of the aggregator k, δ is the margin specified by the utility that allows the aggregator to have an insufficient response, NsimRepresenting the number of simulations, NzFor in Monte Carlo simulations
Figure FDA0003394895280000081
The number of times.
9. An EV aggregator short-time dispatch and response incentive method in view of road traffic models according to claim 8, characterized in that it further comprises the steps of:
7) and determining constraint conditions for clearing by a power company dispatching mechanism, constructing a power company dispatching optimization model and solving to obtain a power company dispatching optimization scheme.
10. The EV aggregator short-time dispatching and response incentive method considering road traffic model as claimed in claim 9, wherein in the step 7), the objective function of the electric power company dispatching optimization model is:
Figure FDA0003394895280000082
μMP＝max{μ1，μ2，...，μK}
the constraint conditions for clearing by the dispatching mechanism of the power company are as follows:
Figure FDA0003394895280000083
wherein, muMPFor the highest bid among the successful bidders,
Figure FDA0003394895280000084
bid capacity, Q, for aggregator kECK is the total number of aggregators participating in the bid for the total capacity demanded by the utility company.
CN202111479915.1A 2021-12-06 2021-12-06 EV aggregator short-time scheduling and response excitation method considering road traffic model Pending CN114372606A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115409294A (en) * 2022-11-01 2022-11-29 江西江投电力技术与试验研究有限公司 Robust optimization method for power distribution network scheduling and charging cooperation

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