CN114529174A - Electric automobile real-time scheduling method considering predicted load and user requirements - Google Patents

Electric automobile real-time scheduling method considering predicted load and user requirements Download PDF

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CN114529174A
CN114529174A CN202210109058.4A CN202210109058A CN114529174A CN 114529174 A CN114529174 A CN 114529174A CN 202210109058 A CN202210109058 A CN 202210109058A CN 114529174 A CN114529174 A CN 114529174A
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scheduling
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周星月
陈楚玥
张勇军
姚蓝霓
杨景旭
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South China University of Technology SCUT
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses an electric vehicle real-time scheduling method considering predicted load and user requirements. The method comprises the following steps: classifying electric vehicle users according to the contract signing with the electric vehicle aggregator and the charging demand difference, and establishing charging control models of various electric vehicle users according to the classifications; establishing a relation model of non-signed user response probability and compensation electricity price; establishing a subsidy mechanism of the power grid for responding to the demand participation of the aggregators and the aggregators for the electric vehicle users; calculating the response potential of various electric vehicle users, determining the power grid side scheduling requirement of each time period according to the predicted load, and formulating a real-time scheduling scheme of each time period according to the power grid side scheduling requirement and the electric vehicle user potential; and establishing a real-time optimization model, and solving the charging power of the electric automobile by adopting a particle swarm algorithm. The method can realize good load peak clipping effect on the basis of meeting different charging requirements of electric vehicle users, and can effectively improve the safety of a power grid.

Description

Electric automobile real-time scheduling method considering predicted load and user requirements
Technical Field
The invention belongs to the technical field of power system demand response, and particularly relates to a real-time electric vehicle dispatching method considering predicted load and user demand.
Background
Due to the fact that the grid connection quantity of the electric automobiles is continuously increased, the randomness and the fluctuation of charging loads of the electric automobiles bring huge operation burden to a power system, and the electric automobiles need to participate in power distribution network scheduling through ordered charging so as to reduce load pressure and eliminate power grid overload risks. At present, methods for researching the charging power optimization problem of the electric automobile are classified into day-ahead global optimization and real-time local optimization. Due to the attribute of the mobile load of the electric automobile, the arrival and departure time of the electric automobile is random, the state of charge of a battery during access has large uncertainty, and the day-ahead scheduling often cannot meet the charging requirements of part of electric automobiles, so that the electric automobile users need to be scheduled in real time. Meanwhile, the participation of the electric automobile in cooperative scheduling is a demand response process, the current demand response is mainly divided into price type demand response and excitation type demand response, a charging time period is selected by a user independently under the price type demand response, the charging power is not regulated and controlled, the demand response potential of the electric automobile cannot be fully utilized, and a peak-valley inversion phenomenon (cheng fir, Chen Nao, Xukang apparatus, and the like) can be generated possibly; the incentive type demand response can guide the user to participate in load adjustment by adopting incentive strategies such as economic compensation or electricity price discount and the like according to the characteristics of different users according to signing contracts or agreements, and the response potential of the electric automobile can be more fully utilized. In the current real-time optimization research, the electric vehicle can participate in scheduling as a premise or only signed users are used as demand response objects, the difference of the charging demands of the users and non-signed users (Chenlvpeng, penning, excessive waves, Qunkeying) who are unwilling to sign a long-term incentive agreement because of high flexibility of daily travel demands are rarely considered, the large-scale electric vehicle real-time optimization scheduling based on the dynamic non-cooperative game [ J ] power system automation, 2019,43(24):32-40+66 ], and the scheduling of predicted load is rarely considered in the real-time optimization by a technology, the real-time scheduling effect is not good enough, and the response potential of the electric vehicle is not fully utilized (Zhangxu, permission, electric vehicle grid-connected rolling time domain optimization of power system automation, 2020,44(13): 106-.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method for classifying electric vehicle users according to the difference between subscription and user charging requirements, establishes charging models of various electric vehicles according to the classification, introduces a relation model of non-subscription user response probability and compensation electricity price, establishes subsidy mechanisms of both demand response parties, and takes the profit-gain ratio of aggregators and the average profit-gain ratio of users as comprehensive targets according to scheduling requirements and predicted scheduling potentials of various users, thereby providing a real-time optimization method of electric vehicle charging power.
The purpose of the invention is realized by at least one of the following technical solutions.
An electric automobile real-time scheduling method considering predicted load and user requirements comprises the following steps:
s1, classifying electric vehicle users according to whether the electric vehicle users are signed with an electric vehicle aggregator or not and the difference of charging requirements, and establishing charging control models of various electric vehicle users according to the classification;
s2, establishing a relation model of the response probability of the non-signed user and the compensation electricity price;
s3, establishing a subsidy mechanism of the power grid for the aggregator and the aggregator for the electric vehicle user participation demand response;
s4, calculating response potentials of various electric vehicle users, determining power grid side scheduling requirements in each time period according to the predicted loads, and making a real-time scheduling scheme in each time period according to the power grid side scheduling requirements and the electric vehicle user potentials;
s5, establishing a real-time optimization model, taking the power grid side control target power as constraint, taking the profit gain ratio of the electric automobile aggregator and the average profit gain ratio of the electric automobile users as comprehensive targets, and solving the charging power of the electric automobile by adopting a particle swarm algorithm.
Further, in step S1, regarding the lithium battery as an object, ignoring the battery self-discharge process and approximately considering that the battery keeps the charging power constant in each optimized time period, a charging model of the electric vehicle is established, which is specifically as follows:
Figure BDA0003494463620000021
wherein S isneThe charging capacity requirement of the electric automobile is met; s0The initial state of charge (SOC) of the electric automobile when the electric automobile is connected; sexThe expected state of charge when the electric vehicle leaves; c0Is the battery capacity; s (t) is the charge state of the electric automobile at the time t; eta is charging efficiency; pc(t) is the charging power of the electric automobile at the moment t; Δ t is the interval of time; pcNRated charging power for the electric vehicle; t is tarAnd texRespectively representing the arrival time and the predicted departure time of the electric automobile;
determining the electric quantity boundary of the electric automobile, specifically as follows:
Tcmin＝C0(Sex-S0)/(ηPcN) (35)
tml＝tar+Tcmin (36)
Figure BDA0003494463620000022
tex＝tar+Ttl (38)
tmc＝tex-Tcmin (39)
Figure BDA0003494463620000031
wherein, TcminThe shortest charging time of the electric automobile;tmlthe fastest leaving time of the electric automobile; smax(t) is the electric quantity upper bound of the electric automobile at the time t when the electric automobile is in the station; t istlThe stay time of the electric automobile in the station; t is tmcThe latest charging starting time of the electric automobile; sminAnd (t) is the lower limit of the electric quantity at the time t when the electric automobile is in the station.
Further, in step S1, it is assumed that the subscriber will actively participate in demand response without urgent travel demand, and a scheduling power difference limit S is setdmAs a limiting condition for freely scheduling the subscribers by the aggregator, the method is used for avoiding that the subscribers without emergency travel demands are always in a scheduled state and cannot be fully charged, namely, the difference between the current electric quantity of a certain subscriber and the electric quantity charged to the current moment according to rated power after the certain subscriber enters the station exceeds the scheduling electric quantity difference limit SdmIf so, the electric automobile does not participate in scheduling any more; the method comprises the following steps of recording a signed user without an emergency travel demand as a class A user, wherein a charging control model of the class A user is as follows:
Figure BDA0003494463620000032
wherein q (i) is the category of the electric automobile i; d (i) is the scheduling priority of the electric vehicle i, d (i) 1 means the highest scheduling priority, d (i) 2 and d (i) 3 means the scheduling priorities are sequentially lowered; sex(i) The expected electric quantity of the electric automobile i is obtained; sMThe maximum charging capacity of the electric automobile is obtained; pmin(i, t) and Pmax(i, t) a lower limit and an upper limit of the charging power of the electric vehicle i at the moment t, respectively; s (i, t) is a schedulable state of the electric vehicle i at time t, s (i, t) ═ 0 represents non-schedulable, s (i, t) ═ 1 represents schedulable, and subscribers with a demand of an emergency trip are always in a non-schedulable state, specifically as follows:
Figure BDA0003494463620000033
Sb(i,t)＝S0(i)+ηPcN(t-tar)/C0
Sd(i,t)＝S(i,t)-Sb(i,t) (43)
wherein S isb(i, t) is that the electric automobile i is charged according to the rated charging power PcNFrom arrival time tarCharging to the standard electric quantity at the moment t; sd(i, t) is the dispatching electric quantity difference of the electric automobile i at the time t; s (i, t) is the electric quantity of the electric automobile i at the moment t; s0(i) The initial charge state of the electric automobile when the electric automobile is connected;
for users who have no contract with the aggregator, namely ordinary users, the users often have no choice of long-term contract due to high flexibility of daily travel demands; if the travel demand of the ordinary users is low when the aggregator issues a certain demand response and wants to participate in the demand response for a certain profit, the ordinary users can participate in the demand response in a temporary signing manner and receive the scheduling of the aggregator, so that the ordinary users with demand response participation will participate in the demand response without reducing expected charging capacity if the stay time of the ordinary users in the charging station exceeds the shortest charging time, the users are marked as B-class users, the scheduling priority is next to A-class users, and when the A-class users cannot meet the power grid scheduling demand, the aggregator needs to guide the B-class users to participate in the demand response at the compensation electricity price of the B-class users; for the ordinary users who stay in the charging station for a time length shorter than the shortest charging time length, the lowest charging electric quantity is needed to participate in demand response as a charging demand, the ordinary users are marked as class C users, the scheduling priority of the class C users is lower than that of class A users and class B users, when the class A users and the class B users cannot meet the power grid scheduling demand, a aggregator needs to pay a compensation power price higher than that of the class B users to guide the class C users to participate in the demand response, and the charging control model of the ordinary users is as follows:
Figure BDA0003494463620000041
wherein S iszd(i) The lowest charging capacity provided for the electric vehicle i is usually lower than the maximum charging capacity S of the electric vehicleM；Sex(i) Is powered electricallyDesired state of charge at exit of car i; t iscmin(i) The shortest charging time of the electric automobile i is obtained; t istl(i) The stay time of the electric automobile i in the station is shown; smin(i, t) is the lower electric quantity bound of the electric automobile i at the time t when the electric automobile i is in the station, SmaxAnd (i, t) is the upper limit of the electric quantity of the electric automobile i at the time t when the electric automobile i is in the station.
Further, in step S2, a relation model between the non-subscriber response probability and the compensation electricity price is established, which is specifically as follows:
when the scheduling potential of the contracted user can not meet the power grid requirement, reasonable compensation power price needs to be formulated to guide the ordinary user to participate in response, and as the compensation power price is higher, the income of the user participating in the demand response is higher, and the response probability is higher, a relationship between the response probability of the ordinary user and the compensation power price, which is a non-contracted user, is established by adopting a direct proportion function, specifically as follows:
Figure BDA0003494463620000051
μ＝1/(cm-c0) (46)
wherein p isx(t) is the response probability of the ordinary user at the time t; c (t) is the compensation electricity price at the time t; mu is the response probability of the ordinary user improved by increasing the unit compensation electricity price; c. C0And cmRespectively, the lowest compensation electricity price and the highest compensation electricity price of the aggregator to the general users.
Further, step S3 includes the steps of:
s3.1, establishing a compensation mechanism of the power grid for the aggregator, and when the aggregator participates in demand response, compensating the aggregator by the power grid side according to the load reduction degree, wherein the compensation mechanism specifically comprises the following steps:
Bg(t)＝Wy(t)·bEVA·a (47)
Figure BDA0003494463620000052
Df(t)＝W(t)/Pne(t) (49)
Figure BDA0003494463620000053
wherein, Bg(t) the compensation cost of the power grid to the aggregator at time t; wy(t) is the effective response electric quantity of the aggregator at the time t; bEVAGenerally taking 0-5 (yuan/kW.h) as a subsidy standard for participating in response; a is a response coefficient, and the response coefficient under the real-time peak clipping demand response is 3; df(t) response completion of the aggregator at time t; n is a radical ofs(t) is the sum of the number of arrived electric vehicles and predicted arrived electric vehicles participating in demand response at time t; p (i, t) is the charging power of the electric automobile i at the moment t;
s3.2, establishing a compensation mechanism of the aggregator for the electric vehicle users, and subsidizing the electric vehicle users according to the electric quantity reduction of the electric vehicle, which is specifically as follows:
Figure BDA0003494463620000054
Bev(i,t)＝c(t)·W(i,t) (52)
W(i,t)＝Δt·(PcN-P(i,t)) (53)
wherein, BEV(t) total subsidy costs of the aggregator to all electric vehicles at time t; b isev(i, t) is the compensation cost of the aggregator to the electric vehicle i at the moment t; and W (i, t) is the response electric quantity of the electric automobile i at the time t.
Further, step S4 includes the following steps:
s4.1, the scheduling potential of various electric vehicles and the scheduling requirement of each time period are calculated as follows:
Pne(t)＝Ppev(t)+Ppb(t)-Paim (54)
Ppev(t)＝PcN(Nn(t)+Np(t)) (55)
Figure BDA0003494463620000061
wherein, Pne(t) scheduling requirement at time t, Ppev(t) predicting the electric vehicle load to be connected before the next moment, P, for time tpb(t) predicted normal load at time t, PaimA control target power for the grid side; n is a radical ofn(t) the number of electric vehicles at the charging station at time t; n is a radical ofp(t) the number of electric vehicles predicted to arrive at the charging station before the next time instant; pcap(q, t) is the scheduling potential of the q-type electric vehicle at the t moment; n is a radical ofs(q, t) is the number of q types of electric vehicles at the time t, and q is A, B or C;
s4.2, making a real-time scheduling scheme of each time interval according to the scheduling requirements of the power grid side and the potential of users, and when a certain time interval has a scheduling requirement PneAnd (t), scheduling various electric vehicles according to the sequence of the scheduling priorities from high to low, and scheduling the electric vehicle with the lower level when the scheduling potential of the user with the high scheduling priority cannot meet the scheduling requirement of the power grid.
Further, in step S4.2, the specific scheduling scheme is as follows:
1) when P is presentne(t)<Pcap(A, t), scheduling part of the class A electric vehicles within the time t;
2) when P iscap(A,t)≤Pne(t)<Pcap(A,t)+Pcap(B, t), scheduling all the A-type electric vehicles and part of the B-type electric vehicles within the time t;
3) when P is presentcap(A,t)+Pcap(B,t)≤Pne(t)<Pcap(A,t)+Pcap(B,t)+Pcap(C, t), scheduling all A, B electric vehicles and part of C electric vehicles within the time t;
4) when P is presentcap(A,t)+Pcap(B,t)+Pcap(C,t)≤PneAnd (t), scheduling all the electric vehicles within the time t.
Further, S5 includes the steps of:
s5.1, the real-time optimization model takes the power grid side control target power as constraint:
Figure BDA0003494463620000062
p (i, t) is the charging power of the electric automobile i after scheduling at the time t; pu(t) the sum of the unscheduled loads at the time t, including the electric vehicle load in the unscheduled state and the conventional load; paimA control target power for the grid side;
s5.2, the real-time optimization model takes the profit-gain ratio of an electric vehicle aggregator and the average profit-gain ratio of electric vehicle users as comprehensive targets, and the real-time optimization model specifically comprises the following steps:
the cost of the aggregator includes the service fee revenue loss Δ B at time tser(t) and a compensation fee B for the userEV(t) a compensation fee B from the grid company at the time of the benefit tg(t), namely:
Figure BDA0003494463620000071
BEVA(t)＝Bg(t)-ΔBser(t)-BEV(t) (59)
ηEVA(t)＝BEVA(t)/Bser(t) (60)
wherein, Bser(t) and B'ser(t) service fee revenues predicted before and after the optimization at time t, respectively; p (t) is the estimated total charging load after the optimization at the time t; b isEVA(t) estimated profit of the aggregator after optimization at time t; c. Cser(t) charging service price at time t; etaEVA(t) estimated profitability gain of the aggregator after optimization at time t;
for the electric vehicle users, the cost mainly includes the response cost, and generally, the user response cost has the characteristics of monotonous non-reduction and concave about the reduction electric quantity, so that the cost is characterized by a quadratic function:
Bx(i,t)＝ax[W(i,t)]2+bxW(i,t) (61)
wherein, Bx(i, t) is the response cost of the electric automobile user i at the moment t; w (i, t) is the reduction electric quantity of the electric automobile user i at the time t; a isxAnd bxAre coefficients, all constants greater than 0;
the user benefits comprise the electric quantity cost Delta B reduced by the user i of the electric automobile at the moment tc(i, t) and the compensation charge B of the aggregator to the electric vehicle user i at the time tev(i, t), namely:
Figure BDA0003494463620000072
Bevs(i,t)＝Bev(i,t)+ΔBc(i,t)-Bx(i,t) (63)
ηevs(i,t)＝Bevs(i,t)/Bc(i,t) (64)
wherein, Bc(i, t) is the electric quantity cost of the electric automobile user i at the moment t, cch(t) is the charge price at time t; b isevs(i,t)、ηevs(i, t) respectively representing the income and income increasing ratio of the electric vehicle user i participating in demand response at the moment t;
integrating all users participating in demand response at time t to investigate average profit B of usersevm(t) and average profitability ηevm(t) is:
Figure BDA0003494463620000073
in order to give consideration to the requirements and benefits of all parties, the power grid side control target power is taken as scheduling constraint, and the profit-to-profit ratio increase of the aggregator and the average profit-to-gain ratio increase of the users are maximized to form a comprehensive optimization target, namely:
maxF(t)＝β1ηEVA(t)+β2ηevm(t) (66)
wherein, beta1And beta2The weight coefficients are the profit-increase ratio of the aggregator and the average profit-to-average ratio of the user respectively.
The invention has the beneficial effects that:
(1) the user can obtain the benefit by participating in the demand response; the peak load of the power distribution network is obviously improved; the power grid company realizes the improvement of the safety of the power grid at a certain economic cost, and the requirement of implementing demand response is met.
(2) The electric vehicle user classification method is based on whether the user signs an incentive agreement with an aggregator or not and the retention time adequacy of the user in a charging station is classified, so that the travel demand and the charging demand of the user can be ensured, the response potential of the electric vehicle user is fully utilized, and the demand response with good peak clipping effect can be realized.
Drawings
Fig. 1 is a flowchart of an electric vehicle real-time scheduling method considering predicted load and user demand according to an embodiment of the present invention.
FIG. 2 is a graph of a conventional load of a commercial district and a predicted load of an electric vehicle in an embodiment of the present invention.
Fig. 3 is a response effect diagram of real-time scheduling in an embodiment of the present invention, where fig. 3a is a schematic diagram of a total load curve before and after response, fig. 3b is a schematic diagram of an electric vehicle power curve before and after response, and fig. 3c is a schematic diagram of the number of responding vehicles and an average power.
Fig. 4 is a load response graph showing whether a general user responds or not in the embodiment of the present invention.
Fig. 5 is a comparison graph of load curves of subscribers with different proportions according to the embodiment of the present invention.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
Example 1:
an electric vehicle real-time dispatching method considering predicted load and user demand is shown in fig. 1, and comprises the following steps:
s1, classifying electric vehicle users according to whether the electric vehicle users are signed with an electric vehicle aggregator or not and the difference of charging requirements, and establishing charging control models of various electric vehicle users according to the classification;
the method comprises the following steps of taking a lithium battery as an object, neglecting the self-discharge process of the battery, approximately considering that the battery keeps constant charging power in each optimized time interval, and establishing a charging model of the electric automobile, wherein the specific steps are as follows:
Figure BDA0003494463620000091
wherein S isneThe charging capacity requirement of the electric automobile is met; s0The initial state of charge (SOC) when the electric automobile is connected; sexThe expected state of charge when the electric vehicle leaves; c0Is the battery capacity; s (t) is the charge state of the electric automobile at the time t; eta is charging efficiency; pc(t) is the charging power of the electric automobile at the moment t; Δ t is the interval of time; pcNRated charging power for the electric vehicle; t is tarAnd texRespectively representing the arrival time and the predicted departure time of the electric automobile;
determining the electric quantity boundary of the electric automobile, specifically as follows:
Tcmin＝C0(Sex-S0)/(ηPcN) (68)
tml＝tar+Tcmin (69)
Figure BDA0003494463620000092
tex＝tar+Ttl (71)
tmc＝tex-Tcmin (72)
Figure BDA0003494463620000093
wherein, TcminThe shortest charging time of the electric automobile; t is tmlThe fastest departure time for the electric vehicle; smax(t) is the electric quantity upper bound of the electric automobile at the time t when the electric automobile is in the station; t istlThe stay time of the electric automobile in the station; t is tmcThe latest charging starting time of the electric automobile; sminAnd (t) is the lower limit of the electric quantity at the time t when the electric automobile is in the station.
Assuming that a subscriber will actively participate in demand response without an emergency travel demand, and setting a scheduling electric quantity difference limit SdmAs a limiting condition for freely scheduling the subscribers by the aggregator, the method is used for avoiding that the subscribers without emergency travel demands are always in a scheduled state and cannot be fully charged, namely, the difference between the current electric quantity of a certain subscriber and the electric quantity charged to the current moment according to rated power after the certain subscriber enters the station exceeds the scheduling electric quantity difference limit SdmIf so, the electric automobile does not participate in scheduling any more; the method comprises the following steps of recording a signed user without an emergency travel demand as a class A user, wherein a charging control model of the class A user is as follows:
Figure BDA0003494463620000101
wherein q (i) is the category of the electric automobile i; d (i) is the scheduling priority of the electric vehicle i, d (i) 1 means the highest scheduling priority, d (i) 2 and d (i) 3 means the scheduling priorities are sequentially lowered; sex(i) The expected electric quantity of the electric automobile i is obtained; sMThe maximum charging capacity of the electric automobile is obtained; pmin(i, t) and Pmax(i, t) a lower limit and an upper limit of the charging power of the electric vehicle i at the moment t, respectively; s (i, t) is a schedulable state of the electric vehicle i at the time t, s (i, t) ═ 0 represents non-schedulable, s (i, t) ═ 1 represents schedulable, and subscribers with the demand of emergency trip are always in non-schedulable stateThe scheduling state is specifically as follows:
Figure BDA0003494463620000102
Figure BDA0003494463620000103
wherein S isb(i, t) according to the rated charging power P for the electric automobile icNFrom arrival time tarCharging to the standard electric quantity at the moment t; s. thed(i, t) is the dispatching electric quantity difference of the electric automobile i at the time t; s (i, t) is the electric quantity of the electric automobile i at the moment t; s0(i) The initial charge state of the electric automobile when the electric automobile is connected;
for users who have no contract with the aggregator, namely ordinary users, the users often have no choice of long-term contract due to high flexibility of daily travel demands; if the travel demand of the ordinary users is low when the aggregator issues a certain demand response and wants to participate in the demand response for a certain profit, the ordinary users can participate in the demand response in a temporary signing manner and receive the scheduling of the aggregator, so that the ordinary users with demand response participation will participate in the demand response without reducing expected charging capacity if the stay time of the ordinary users in the charging station exceeds the shortest charging time, the users are marked as B-class users, the scheduling priority is next to A-class users, and when the A-class users cannot meet the power grid scheduling demand, the aggregator needs to guide the B-class users to participate in the demand response at the compensation electricity price of the B-class users; for the ordinary users who stay in the charging station for a time length shorter than the shortest charging time length, the lowest charging electric quantity is needed to participate in demand response as a charging demand, the ordinary users are marked as class C users, the scheduling priority of the class C users is lower than that of class A users and class B users, when the class A users and the class B users cannot meet the power grid scheduling demand, a aggregator needs to pay a compensation power price higher than that of the class B users to guide the class C users to participate in the demand response, and the charging control model of the ordinary users is as follows:
Figure BDA0003494463620000111
wherein S iszd(i) The lowest charging capacity provided for the electric vehicle i is usually lower than the maximum charging capacity S of the electric vehicleM；Sex(i) The expected state of charge when the electric vehicle i leaves; t iscmin(i) The shortest charging time of the electric automobile i is obtained; t istl(i) The stay time of the electric automobile i in the station is shown; s. themin(i, t) is the lower electric quantity bound of the electric automobile i at the time t when the electric automobile i is in the station, SmaxAnd (i, t) is the upper limit of the electric quantity of the electric automobile i at the time t when the electric automobile i is in the station.
S2, establishing a relation model of the response probability of the non-signed user and the compensation electricity price, which is as follows:
when the scheduling potential of the contracted user can not meet the power grid requirement, reasonable compensation power price needs to be formulated to guide the ordinary user to participate in response, and as the compensation power price is higher, the income of the user participating in the demand response is higher, and the response probability is higher, a relationship between the response probability of the ordinary user and the compensation power price, which is a non-contracted user, is established by adopting a direct proportion function, specifically as follows:
Figure BDA0003494463620000112
μ＝1/(cm-c0) (79)
wherein p isx(t) is the response probability of the ordinary user at the moment t; c (t) is the compensation electricity price at the time t; mu is the response probability of the ordinary user improved by increasing the unit compensation electricity price; c. C0And cmRespectively, the lowest compensation electricity price and the highest compensation electricity price of the aggregator to the general users.
S3, establishing a subsidy mechanism of the power grid for the aggregator and the aggregator for the electric vehicle user participation demand response, comprising the following steps:
s3.1, establishing a compensation mechanism of the power grid for the aggregator, and when the aggregator participates in demand response, compensating the aggregator by the power grid side according to the load reduction degree, wherein the compensation mechanism specifically comprises the following steps:
Bg(t)＝Wy(t)·bEVA·a (80)
Figure BDA0003494463620000121
Df(t)＝W(t)/Pne(t) (82)
Figure BDA0003494463620000122
wherein, Bg(t) the compensation cost of the power grid to the aggregator at time t; w is a group ofy(t) is the effective response electric quantity of the aggregator at the time t; bEVAGenerally taking 0-5 (yuan/kW.h) as a subsidy standard for participating in response; a is a response coefficient, and the response coefficient under the real-time peak clipping demand response is 3; df(t) response completion of the aggregator at time t; n is a radical of hydrogens(t) is the sum of the number of arrived electric vehicles and predicted arrived electric vehicles participating in demand response at the time t; p (i, t) is the charging power of the electric automobile i at the moment t;
s3.2, establishing a compensation mechanism of the aggregator for the electric vehicle users, and subsidizing the electric vehicle users according to the electric quantity reduction of the electric vehicle, which is specifically as follows:
Figure BDA0003494463620000123
Bev(i,t)＝c(t)·W(i,t) (85)
W(i,t)＝Δt·(PcN-P(i,t)) (86)
wherein, BEV(t) total subsidy costs of the aggregator to all electric vehicles at time t; b isev(i, t) is the compensation cost of the aggregator to the electric vehicle i at the moment t; and W (i, t) is the response electric quantity of the electric automobile i at the time t.
S4, calculating response potentials of various electric vehicle users, determining power grid side dispatching requirements in each time period according to the predicted loads, and making a real-time dispatching scheme in each time period according to the power grid side dispatching requirements and the electric vehicle user potentials, wherein the method comprises the following steps:
s4.1, the scheduling potential of various electric vehicles and the scheduling requirement of each time period are calculated as follows:
Pne(t)＝Ppev(t)+Ppb(t)-Paim (87)
Ppev(t)＝PcN(Nn(t)+Np(t)) (88)
Figure BDA0003494463620000124
wherein, Pne(t) scheduling requirement at time t, Ppev(t) predicting the electric vehicle load to be connected before the next moment, P, for time tpb(t) predicted normal load at time t, PaimA control target power for the grid side; n is a radical ofn(t) the number of electric vehicles at the charging station at time t; n is a radical ofp(t) the number of electric vehicles expected to arrive at the charging station before the next time instant; pcap(q, t) is the scheduling potential of the q-type electric vehicle at the t moment; n is a radical ofs(q, t) is the number of q electric vehicles at the time t, and q is A, B or C;
s4.2, making a real-time scheduling scheme of each time interval according to the scheduling requirements of the power grid side and the potential of users, and when a certain time interval has a scheduling requirement PneAnd (t), scheduling various electric vehicles according to the sequence of the scheduling priorities from high to low, and scheduling the electric vehicles with the lower level when the scheduling potential of the users with the high scheduling priorities cannot meet the scheduling requirement of the power grid, wherein the specific scheduling scheme is as follows:
1) when P is presentne(t)<PcapWhen the electric vehicles of the type A are in the state of being in the (A, t);
2) when P iscap(A,t)≤Pne(t)<Pcap(A,t)+Pcap(B, t), scheduling all the A-type electric vehicles and part of the B-type electric vehicles within the time t;
3) when P is presentcap(A,t)+Pcap(B,t)≤Pne(t)<Pcap(A,t)+Pcap(B,t)+Pcap(C, t), scheduling all A, B type electric vehicles and part of C type electric vehicles in the t moment;
4) when P is presentcap(A,t)+Pcap(B,t)+Pcap(C,t)≤PneAnd (t), scheduling all the electric vehicles within the time t.
S5, establishing a real-time optimization model, taking power grid side control target power as constraint, taking electric vehicle aggregator profit gain ratio and electric vehicle user average profit gain ratio as comprehensive targets, and solving the electric vehicle charging power by adopting a particle swarm algorithm, wherein the method comprises the following steps:
s5.1, the real-time optimization model takes the power grid side control target power as constraint:
Figure BDA0003494463620000131
p (i, t) is the charging power of the electric automobile i after scheduling at the time t; pu(t) the sum of the unscheduled loads at the time t, including the electric vehicle load in the unscheduled state and the conventional load; paimA control target power for the grid side;
s5.2, the real-time optimization model takes the profit-gain ratio of an electric vehicle aggregator and the average profit-gain ratio of electric vehicle users as comprehensive targets, and the real-time optimization model specifically comprises the following steps:
the cost of the aggregator includes the service fee revenue loss Δ B at time tser(t) and a compensation fee B for the userEV(t) a compensation fee B from the grid company at the time of the benefit tg(t), namely:
Figure BDA0003494463620000132
BEVA(t)＝Bg(t)-ΔBser(t)-BEV(t) (92)
ηEVA(t)＝BEVA(t)/Bser(t) (93)
wherein, Bser(t) and B'ser(t) service fee revenues predicted before and after the optimization at time t, respectively; p (t) is the estimated total charging load after the optimization at the time t; b isEVA(t) estimated profit of the aggregator after optimization at time t; c. Cser(t) charging service price at time t; etaEVA(t) estimated profitability gain of the aggregator after optimization at time t;
for the electric vehicle users, the cost mainly includes the response cost, and generally, the user response cost has the characteristics of monotonous non-reduction and concave about the reduction electric quantity, so that the cost is characterized by a quadratic function:
Bx(i,t)＝ax[W(i,t)]2+bxW(i,t) (94)
wherein, Bx(i, t) is the response cost of the electric automobile user i at the moment t; w (i, t) is the reduction electric quantity of the electric automobile user i at the time t; a isxAnd bxAre coefficients, all constants greater than 0;
the user benefits comprise the electric quantity cost Delta B reduced by the user i of the electric automobile at the moment tc(i, t) and the compensation charge B of the aggregator to the electric vehicle user i at the time tev(i, t), namely:
Figure BDA0003494463620000141
Bevs(i,t)＝Bev(i,t)+ΔBc(i,t)-Bx(i,t) (96)
ηevs(i,t)＝Bevs(i,t)/Bc(i,t) (97)
wherein, Bc(i, t) is the electric quantity cost of the electric automobile user i at the moment t, cch(t) is the charge price at time t; b isevs(i,t)、ηevs(i, t) respectively representing the income and income increasing ratio of the electric vehicle user i participating in demand response at the moment t;
integrating all users participating in demand response at time t to investigate average value of demand responseProfit Bevm(t) and average profitability ηevm(t) is:
Figure BDA0003494463620000142
in order to give consideration to the requirements and benefits of all parties, the power grid side control target power is taken as scheduling constraint, and the profit-to-profit ratio increase of the aggregator and the average profit-to-gain ratio increase of the users are maximized to form a comprehensive optimization target, namely:
maxF(t)＝β1ηEVA(t)+β2ηevm(t) (99)
wherein, beta1And beta2The weight coefficients are the profit-increase ratio of the aggregator and the average profit-to-average ratio of the user respectively.
In this embodiment, the conventional load of a certain commercial district is taken as a basic load, and the predicted load of the electric vehicle is determined by sampling the probability distribution of the charging time and the initial electric quantity of the electric vehicle in the commercial district, as shown in fig. 2; controlling the power value of a target power under the voltage level of 10kV, wherein the current-carrying capacity of a wire is 381A, and the power factor is 0.95; the time-of-use electricity price for charging the electric vehicle is consistent with that of a certain charging station in Guangzhou city, as shown in table 1. Total number of electric vehicles arriving per day N of the electric vehicle aggregatorv600, PcN60kW and C are taken0Taking 60 kW.h, and 0.95 of eta; sdm Taking 1; the electric automobile stay time follows normal distribution of N (2, 0.5); sMTaking 0.9; szdUniform distribution of U (0.6, 0.9) is obeyed; bEVATaking the c of 2.5 yuan/(kW.h) B, C type users0Respectively taking 1.5 yuan/(kW.h), 2.5 yuan/(kWh.h) and cmRespectively taking 3 yuan/(kW & h) and 4 yuan/(kW & h); a is ax、bxRespectively take 0.005 yuan/(kW. h)2And 1.5 yuan/(kWh. h); Δ t is 15 min; according to the size of the profit ratio, beta1、β2The ratio of 1: 4, and is 1.
TABLE 1
Figure BDA0003494463620000151
When the proportion of the subscribers is 30%, the total load curve before and after optimization when the ordinary subscribers participate in the response is shown in fig. 3a, the EV load curve is shown in fig. 3b, the number of responding vehicles and the average power are shown in fig. 3c, and the optimization results are shown in table 2.
Therefore, from the 41 th time period, because the predicted load exceeds the control target, the EVA determines a scheduling scheme according to the scheduling requirement and the scheduling potentials of various EVs, and schedules the corresponding EVs. Through scheduling, the EV charging load peak clipping rate can reach 8.35%, and the total load peak clipping rate is reduced to 7.68% due to larger basic load. When the load suddenly increases, the number of responding EVs rapidly increases, while the average charging power of the responding EVs is regulated to rapidly decrease. And in the vicinity of the original load peak period, the reduced power is also close to the peak value, and the effectiveness of the real-time scheduling strategy provided by the method is verified.
As can be seen from table 2, in the whole scheduling process, the total number of the response participating time periods is 26, 17 EVs participate in the response in each response time period on average, the average response power of these vehicles is 25kW, the average delay charging time period is 18.9 minutes, the total revenue of the aggregator is 48871 yuan, the revenue gain ratio is 6.84, the average revenue of the response participating EV users is 46.71 yuan, and the average revenue gain ratio is 1.66.
TABLE 2
Figure BDA0003494463620000152
Example 2:
in this embodiment, when the proportion of the subscribers is 30%, the total load curve of whether the normal subscriber participates in the response is shown in fig. 4, and the optimization result is shown in table 3.
TABLE 3
Figure BDA0003494463620000161
It can be seen that the total load peak reduction rate is increased from 2.27% to 7.68% when the ordinary user participates in the response, compared with the non-participation in the response. Because the schedulable potential of each time interval is greatly increased when the ordinary users participate in the scheduling, the load peak value is reduced more. When the ordinary users do not participate in the response, the number of schedulable EVs is too small to meet the power grid requirement, subsidies of the power grid to the aggregator are reduced, the income and income increasing ratio of the aggregator is reduced, and the average income and average income increasing ratio of the EVs participating in the response are reduced, so that the ordinary users need to be brought into the DR range, the power grid requirement can be better met, meanwhile, the income of the aggregator and the user can be increased, and mutual profit and win-win of three parties are realized.
Example 3:
in this embodiment, when the proportions of the subscribers are different, the optimization effects of real-time scheduling are also different, and fig. 5 shows load curves of response participated by the common users when the proportions of the subscribers are 15%, 30%, and 45%, respectively, and the optimization results are shown in table 4.
TABLE 4
Figure BDA0003494463620000162
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. An electric vehicle real-time scheduling method considering predicted load and user requirements is characterized by comprising the following steps:
s1, classifying electric vehicle users according to whether the electric vehicle users are signed with an electric vehicle aggregator or not and the difference of charging requirements, and establishing charging control models of various electric vehicle users according to the classification;
s2, establishing a relation model of the response probability of the non-signed user and the compensation electricity price;
s3, establishing a subsidy mechanism of the power grid for the aggregator and the aggregator for the electric vehicle user participation demand response;
s4, calculating response potentials of various electric vehicle users, determining power grid side scheduling requirements in each time period according to the predicted loads, and making a real-time scheduling scheme in each time period according to the power grid side scheduling requirements and the electric vehicle user potentials;
s5, establishing a real-time optimization model, taking the power grid side control target power as constraint, taking the profit gain ratio of the electric automobile aggregator and the average profit gain ratio of the electric automobile users as comprehensive targets, and solving the charging power of the electric automobile by adopting a particle swarm algorithm.
2. The method for real-time dispatching of electric vehicles taking into account predicted loads and user demands according to claim 1, characterized in that: in step S1, a lithium battery is used as an object, the self-discharge process of the battery is ignored and the battery is considered to keep the charging power constant in each optimization time period approximately, and a charging model of the electric vehicle is established as follows:
Figure FDA0003494463610000011
wherein S isneThe charging capacity requirement of the electric automobile is met; s. the0The initial state of charge (SOC) of the electric automobile when the electric automobile is connected; sexThe expected state of charge when the electric vehicle leaves; c0Is the battery capacity; s (t) is the charge state of the electric automobile at the time t; eta is charging efficiency; pc(t) is the charging power of the electric automobile at the moment t; Δ t is the interval of time; pcNRated charging power for the electric vehicle; t is tarAnd texRespectively representing the arrival time and the predicted departure time of the electric automobile;
determining the electric quantity boundary of the electric automobile, specifically as follows:
Tcmin＝C0(Sex-S0)/(ηPcN) (2)
tml＝tar+Tcmin (3)
Figure FDA0003494463610000012
tex＝tar+Ttl (5)
tmc＝tex-Tcmin (6)
Figure FDA0003494463610000021
wherein, TcminThe shortest charging time of the electric automobile; t is tmlThe fastest leaving time of the electric automobile; smax(t) is the electric quantity upper bound of the electric automobile at the time t when the electric automobile is in the station; t istlThe stay time of the electric automobile in the station; t is tmcThe latest charging starting time of the electric automobile; sminAnd (t) is the lower limit of the electric quantity at the time t when the electric automobile is in the station.
3. The method of claim 2, wherein the method comprises the steps of: in step S1, it is assumed that the subscriber will actively participate in demand response without emergency travel demand, and a scheduling power difference limit S is setdmAs a limiting condition for freely scheduling the subscribers by the aggregator, the method is used for avoiding that the subscribers without emergency travel demands are always in a scheduled state and cannot be fully charged, namely, the difference between the current electric quantity of a certain subscriber and the electric quantity charged to the current moment according to rated power after the certain subscriber enters the station exceeds the scheduling electric quantity difference limit SdmIf so, the electric automobile does not participate in scheduling any more; the method comprises the following steps of recording a signed user without an emergency travel demand as a class A user, wherein a charging control model of the class A user is as follows:
Figure FDA0003494463610000022
wherein q (i) is the category of the electric automobile i; d (i) is the scheduling priority of the electric vehicle i, d (i) 1 means the highest scheduling priority, d (i) 2 and d (i) 3 means the scheduling priorities are sequentially lowered; s. theex(i) The expected electric quantity of the electric automobile i is obtained; sMThe maximum charging capacity of the electric automobile is obtained; pmin(i, t) and Pmax(i, t) a lower limit and an upper limit of the charging power of the electric vehicle i at the moment t, respectively; s (i, t) is a schedulable state of the electric vehicle i at time t, s (i, t) ═ 0 represents non-schedulable, s (i, t) ═ 1 represents schedulable, and subscribers with a demand of an emergency trip are always in a non-schedulable state, specifically as follows:
Figure FDA0003494463610000023
Figure FDA0003494463610000024
wherein S isb(i, t) is that the electric automobile i is charged according to the rated charging power PcNFrom arrival time tarCharging to the standard electric quantity at the moment t; sd(i, t) is the dispatching electric quantity difference of the electric automobile i at the time t; s (i, t) is the electric quantity of the electric automobile i at the moment t; s0(i) The initial charge state of the electric automobile when the electric automobile is connected;
for users who have no contract with the aggregator, namely ordinary users, the users often have no choice of long-term contract due to high flexibility of daily travel demands; if the travel demand of the ordinary users is low when the aggregator issues a certain demand response and wants to participate in the demand response for a certain profit, the ordinary users can participate in the demand response in a temporary signing manner and receive the scheduling of the aggregator, so that the ordinary users with demand response participation will participate in the demand response without reducing expected charging capacity if the stay time of the ordinary users in the charging station exceeds the shortest charging time, the users are marked as B-class users, the scheduling priority is next to A-class users, and when the A-class users cannot meet the power grid scheduling demand, the aggregator needs to guide the B-class users to participate in the demand response at the compensation electricity price of the B-class users; for the ordinary users who stay in the charging station for a time length shorter than the shortest charging time length, the lowest charging electric quantity is needed to participate in demand response as a charging demand, the ordinary users are marked as class C users, the scheduling priority of the class C users is lower than that of class A users and class B users, when the class A users and the class B users cannot meet the power grid scheduling demand, a aggregator needs to pay a compensation power price higher than that of the class B users to guide the class C users to participate in the demand response, and the charging control model of the ordinary users is as follows:
Figure FDA0003494463610000031
wherein S iszd(i) The lowest charging capacity provided for the electric vehicle i is generally lower than the maximum charging capacity S of the electric vehicleM；Sex(i) The expected state of charge when the electric vehicle i leaves; t iscmin(i) The shortest charging time of the electric automobile i is obtained; t istl(i) The stay time of the electric automobile i in the station is shown; smin(i, t) is the lower electric quantity bound of the electric automobile i at the time t when the electric automobile i is in the station, SmaxAnd (i, t) is the upper limit of the electric quantity of the electric automobile i at the time t when the electric automobile i is in the station.
4. The method of claim 3 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: in step S2, a relational model between the response probability of the non-subscriber and the compensation electricity price is established, which is specifically as follows:
when the scheduling potential of the contracted user can not meet the power grid requirement, reasonable compensation power price needs to be formulated to guide the ordinary user to participate in response, and as the compensation power price is higher, the income of the user participating in the demand response is higher, and the response probability is higher, a relationship between the response probability of the ordinary user and the compensation power price, which is a non-contracted user, is established by adopting a direct proportion function, specifically as follows:
Figure FDA0003494463610000041
μ＝1/(cm-c0) (13)
wherein p isx(t) is the response probability of the ordinary user at the time t; c (t) is the compensation electricity price at the time t; mu is the response probability of the ordinary user improved by increasing the unit compensation electricity price; c. C0And cmRespectively, the lowest compensation electricity price and the highest compensation electricity price of the aggregator to the general users.
5. The method of claim 4 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: step S3 includes the following steps:
s3.1, establishing a compensation mechanism of the power grid for the aggregators, and when the aggregators participate in demand response, compensating the aggregators according to the load reduction degree on the power grid side, wherein the compensation mechanism specifically comprises the following steps:
Bg(t)＝Wy(t)·bEVA·a (14)
Figure FDA0003494463610000042
Df(t)＝W(t)/Pne(t) (16)
Figure FDA0003494463610000043
wherein, Bg(t) the compensation cost of the power grid to the aggregator at time t; wy(t) is the effective response electric quantity of the aggregator at the time t; bEVAUsually 0-5 (yuan/kW.h) is taken as a subsidy standard for participating in response; a is a response coefficient, and the response coefficient under the real-time peak clipping demand response is 3; df(t) response completion of the aggregator at time t; n is a radical of hydrogens(t) engaging in demand response for time tThe sum of the number of arrived electric vehicles and the number of predicted arrived electric vehicles; p (i, t) is the charging power of the electric automobile i at the moment t;
s3.2, establishing a compensation mechanism of the aggregator for the electric vehicle users, and subsidizing the electric vehicle users according to the electric quantity reduction of the electric vehicle, which is specifically as follows:
Figure FDA0003494463610000044
Bev(i,t)＝c(t)·W(i,t) (19)
W(i,t)＝Δt·(PcN-P(i,t)) (20)
wherein, BEV(t) total subsidy costs of the aggregator to all electric vehicles at time t; b isev(i, t) is the compensation cost of the aggregator to the electric vehicle i at the moment t; and W (i, t) is the response electric quantity of the electric automobile i at the time t.
6. The method for optimizing real-time scheduling of electric vehicles according to any one of claims 1 to 5, wherein the method comprises the following steps: step S4 includes the following steps:
s4.1, the scheduling potential of various electric vehicles and the scheduling requirement of each time period are calculated as follows:
Pne(t)＝Ppev(t)+Ppb(t)-Paim (21)
Ppev(t)＝PcN(Nn(t)+Np(t)) (22)
Figure FDA0003494463610000051
wherein, Pne(t) scheduling requirement at time t, Ppev(t) predicting the electric vehicle load to be connected before the next moment, P, for time tpb(t) predicted normal load at time t, PaimA control target power for the grid side; n is a radical ofn(t) the number of electric vehicles at the charging station at time t;Np(t) the number of electric vehicles predicted to arrive at the charging station before the next time instant; pcap(q, t) is the scheduling potential of the q-class electric vehicle at the t moment; n is a radical ofs(q, t) is the number of q types of electric vehicles at the time t, and q is A, B or C;
s4.2, making a real-time scheduling scheme of each time interval according to the scheduling requirements of the power grid side and the potential of users, and when a certain time interval has a scheduling requirement PneAnd (t), scheduling various electric vehicles according to the sequence of the scheduling priorities from high to low, and scheduling the electric vehicle with the lower level when the scheduling potential of the user with the high scheduling priority cannot meet the scheduling requirement of the power grid.
7. The method of claim 6 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: in step S4.2, the specific scheduling scheme is as follows:
1) when P is presentne(t)<Pcap(A, t), scheduling part of the class A electric vehicles within the time t;
2) when P is presentcap(A,t)≤Pne(t)<Pcap(A,t)+Pcap(B, t), scheduling all the A-type electric vehicles and part of the B-type electric vehicles within the time t;
3) when P is presentcap(A,t)+Pcap(B,t)≤Pne(t)<Pcap(A,t)+Pcap(B,t)+Pcap(C, t), scheduling all A, B electric vehicles and part of C electric vehicles within the time t;
4) when P is presentcap(A,t)+Pcap(B,t)+Pcap(C,t)≤PneAnd (t), scheduling all the electric vehicles within the time t.
8. The method of claim 6 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: s5 includes the steps of:
s5.1, constructing the constraint of a real-time optimization model by using the power grid side control target power;
and S5.2, constructing a comprehensive target of the real-time optimization model by the profit-gain ratio of the electric vehicle aggregator and the average profit-gain ratio of the electric vehicle users.
9. The method of claim 8 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: in step S5.1, the real-time optimization model takes the power grid side control target power as a constraint, which is specifically as follows:
Figure FDA0003494463610000061
p (i, t) is the charging power of the electric automobile i after scheduling at the time t; pu(t) the sum of the unscheduled loads at the time t, including the electric vehicle load in the unscheduled state and the conventional load; p isaimIs the control target power of the power grid side.
10. The method of claim 9 for optimizing real-time scheduling of electric vehicles taking into account predicted load and user demand, wherein: in step S5.2, the details are as follows:
the cost of the aggregator includes the service fee revenue loss Δ B at time tser(t) and a compensation fee B for the userEV(t) a compensation fee B from the grid company at the time of the benefit tg(t), namely:
Figure FDA0003494463610000062
BEVA(t)＝Bg(t)-ΔBser(t)-BEV(t) (26)
ηEVA(t)＝BEVA(t)/Bser(t) (27)
wherein, Bser(t) and B'ser(t) service fee revenues predicted before and after optimization at time t, respectively; p (t) is the estimated total charging load after the optimization at the time t; b isEVA(t) estimated profitability of the aggregator after optimization at time t; c. Cser(t)Charging service price for t moment; etaEVA(t) estimated profitability gain of the aggregator after optimization at time t;
for the electric vehicle users, the cost mainly includes the response cost, and generally, the user response cost has the characteristics of monotonous non-reduction and concave about the reduction electric quantity, so that the cost is characterized by a quadratic function:
Bx(i,t)＝ax[W(i,t)]2+bxW(i,t) (28)
wherein, Bx(i, t) is the response cost of the electric automobile user i at the moment t; w (i, t) is the reduction electric quantity of the electric automobile user i at the time t; a isxAnd bxAre coefficients, all constants greater than 0;
the user benefits comprise the electric quantity cost Delta B reduced by the user i of the electric automobile at the moment tc(i, t) and the compensation charge B of the aggregator to the electric vehicle user i at the time tev(i, t), namely:
Figure FDA0003494463610000063
Bevs(i,t)＝Bev(i,t)+ΔBc(i,t)-Bx(i,t) (30)
ηevs(i,t)＝Bevs(i,t)/Bc(i,t) (31)
wherein, Bc(i, t) is the electric quantity cost of the electric automobile user i at the moment t, cch(t) is the charge price at time t; b isevs(i,t)、ηevs(i, t) respectively representing the income and income increasing ratio of the electric vehicle user i participating in demand response at the moment t;
integrating all users participating in demand response at time t to investigate average profit B of usersevm(t) and average profitability ηevm(t) is:
Figure FDA0003494463610000071
in order to give consideration to the requirements and benefits of all parties, the power grid side control target power is taken as scheduling constraint, and the profit-to-profit ratio increase of the aggregator and the average profit-to-gain ratio increase of the users are maximized to form a comprehensive optimization target, namely:
max F(t)＝β1ηEVA(t)+β2ηevm(t) (33)
wherein beta is1And beta2The weight coefficients are the profit-increase ratio of the aggregator and the average profit-to-average ratio of the user respectively.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115439138A (en) * 2022-08-03 2022-12-06 广东奔流能源有限公司 Electric vehicle charging and discharging power optimal distribution method and system
CN116073418A (en) * 2023-02-14 2023-05-05 燕山大学 Electric automobile charging and discharging scheduling method based on dynamic electricity price

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115439138A (en) * 2022-08-03 2022-12-06 广东奔流能源有限公司 Electric vehicle charging and discharging power optimal distribution method and system
CN116073418A (en) * 2023-02-14 2023-05-05 燕山大学 Electric automobile charging and discharging scheduling method based on dynamic electricity price

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