CN114398723A - Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method and system - Google Patents
Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method and system Download PDFInfo
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Abstract
The application discloses a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method and system, wherein the method comprises the following steps: constructing an electric automobile individual model; constructing an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of a charging station; extending the electric vehicle individual model definition domain to the same scheduling period, and carrying out cluster modeling on the large-scale electric vehicle grid-connected based on Minkowski, so as to obtain an electric vehicle cluster characteristic model which is regarded as a charging station generalized energy storage model; calculating characteristic parameters of the electric automobile cluster, compressing a variable space of the electric automobile cluster into a variable space of a generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as flexible energy storage resources. The invention can realize the response potential analysis of the electric automobile, so that the charging station as a whole participates in the load dispatching of the power grid, thereby obtaining the adjustment compensation and increasing the benefit.
Description
Technical Field
The invention belongs to the technical field of power system scheduling control, and relates to a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method and system.
Background
According to the latest new energy automobile industry development planning, the new energy automobile charging pile is listed as one of seven industry directions of new capital construction, and the fast development of the electric automobile is further promoted. Along with the development of battery technology and the increasing perfection of matched infrastructure, the quantity of electric automobiles in China is rising year by year. According to the industrial development planning, the hold of electric automobiles in China will reach 8300 thousands by 2030, the equivalent energy storage capacity will reach 50 hundred million kilowatts, the charging demand of the electric automobiles will occupy 6 to 7 percent of the power consumption of the whole society, and the maximum charging load will occupy 11 to 12 percent of the load of a power grid. Therefore, the development of large-scale electric automobiles becomes an inevitable trend of electric energy substitution and green traffic.
On one hand, a large amount of electric automobile loads are randomly accessed into the system, impact is caused to the power grid, outstanding contradictions such as peak-to-valley difference, voltage deviation, local blockage and the like of the power system tend to be aggravated, and effective management must be carried out; on the other hand, the distributed energy storage characteristic of the electric automobile provides abundant schedulable resources for power grid peak regulation, voltage regulation, new energy consumption and the like, and effective management is required.
How to ensure the safe operation of the urban power grid, meet the requirement of large-scale electric automobile access to the maximum extent, fully utilize schedulable resources of the electric automobiles, support the development of urban energy Internet and provide unprecedented major challenges for the operation control of the urban power grid. In order to effectively manage the electric automobile and fully utilize energy storage resources, it is necessary to perform cluster characteristic analysis on the electric automobile.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method and system, the individual variable space of an electric vehicle is projected to a hypercube space, the constraint relation among variables is kept, all feasible charging and discharging decisions of a charging station are contained in the hypercube space, an electric vehicle set is compressed into a generalized energy storage model, the dimensionality of the model is greatly reduced, the problem of explosive development of the model and data dimensionality when the large-scale electric vehicle is connected into a power grid is solved, the responsiveness of the generalized energy storage model as a flexible energy storage resource is further excavated, and the electric vehicle is comprehensively supported to participate in load scheduling.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the Minkowski sum-based large-scale electric vehicle cluster characteristic analysis method comprises the following steps of:
step 1: analyzing physical characteristics and operating characteristics of the electric automobile, and constructing an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
step 2: constructing an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of a charging station;
and step 3: extending the electric vehicle individual model definition domain to the same scheduling period, and carrying out cluster modeling on the large-scale electric vehicle grid-connected based on Minkowski, so as to obtain an electric vehicle cluster characteristic model which is regarded as a charging station generalized energy storage model;
and 4, step 4: calculating characteristic parameters of the electric automobile cluster, compressing a variable space of the electric automobile cluster into a variable space of a generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as flexible energy storage resources.
The invention further comprises the following preferred embodiments:
preferably, in step 1, the electric vehicle has load translation and reverse power supply capabilities, and the individual model thereof is as shown in formulas (1) to (5):
in the formula (I), the compound is shown in the specification,
respectively is the charging power and the discharging power of the electric automobile n in the time period t;
respectively representing the upper limits of charging and discharging power of the electric automobile n;
representing an n grid-connected time period set of the electric automobile;
in the formula, sn,tAnd sn,t-1Respectively representing the battery capacity of the electric vehicle n in a time period t and a previous time period; etach、ηdisRespectively the charging and discharging efficiency of the electric automobile; Δ t represents a time window; etarefRepresents a discharge compensation coefficient determined by a discharge loss;
in the formula (I), the compound is shown in the specification,
representing a battery level safety boundary of the electric vehicle n;
the electric vehicle can only be in a charging state or a discharging state at the same time, so that the electric vehicle has the following components:
preferably, in step 2, the electric vehicle charging station is used as a natural aggregator, and participates in power grid load scheduling as a flexible load through charging and discharging of electric vehicles in the management station, and what directly participates in the power grid load scheduling is the total load of the charging station, so that an electric vehicle aggregation model is constructed according to the following formulas (6) to (7):
in the formula (I), the compound is shown in the specification,
and
respectively the total charging power and the total discharging power of the charging station j in the time period t;
t is a set of scheduling periods.
Preferably, step 3 specifically comprises:
step 3.1: calculating grid-connected state X of electric automobile based on parking time of electric automobilen,t；
Step 3.2: based on grid-connected state Xn,tExtending the definition domains of the charging and discharging power, the battery electric quantity and the battery electric quantity safety boundary in the electric automobile individual model to the same scheduling period, so that the decision space of the electric automobile individual model is Minkowski additivity;
step 3.3: integrating the battery electric quantity after the domain extension in the electric automobile individual model in the step 3.2 according to the three stages of the battery electric quantity in the network access period, the normal network connection period and the network disconnection period in the whole network connection process;
step 3.4: utilizing Minkowski addition to obtain the charging and discharging power after the domain extension, the battery electric quantity safety boundary and the envelope space corresponding to the integrated battery electric quantity in the electric automobile individual model;
step 3.5: and constructing an electric vehicle cluster characteristic model, and regarding the electric vehicle cluster characteristic model as a charging station generalized energy storage model.
Preferably, in step 3.1, the grid-connected state X of the electric automobilen,tThe parking time of the electric automobile is directly calculated to obtain:
in the formula, Xn,tRepresenting the state of the electric vehicle n in the t period; xn,t1 means that the electric vehicle n is in a grid-connected state in the period t,
indicating the time at which the electric vehicle n arrives at the charging station,
indicating the time at which the electric vehicle n leaves the charging station.
Preferably, in step 3.2, the domain of charge and discharge power, battery level safety boundaries in the electric vehicle individual model is extended to the full tuning period T, so that the electric vehicle individual decision space minkowski additivity:
sn,tand sn,t-1Respectively representing the battery capacity of the electric vehicle n in a time period t and a previous time period;
ηch、ηdisrespectively the charging and discharging efficiency of the electric automobile;
ηrefrepresents a discharge compensation coefficient; Δ t represents a time window;
Preferably, in step 3.3, the battery power s of the electric vehicle n in step 3.2 in the time period t is determinedn,tThe integration is carried out according to three stages of a network access time interval, a normal grid connection time interval and a grid disconnection time interval in the whole grid connection process, and the integration method specifically comprises the following steps:
and a network access time period:
wherein s isn,arrivalThe initial electric quantity of the electric automobile is the battery electric quantity s of the previous time periodn,t-1＝0，
Namely satisfies Xn,t(Xn,t-Xn,t-1)＝1；
And (3) normal grid connection time period:
an off-grid period:
wherein s isn,leaveFor the off-grid electric quantity of the electric automobile, the formula implies
And
namely satisfies Xn,t-1(Xn,t-1-Xn,t)＝1；
Formulae (13) - (15) have minkowski additivity, further integrated as:
preferably, in step 3.4, the minkowski addition processing formula (9), formula (10), formula (12) and formula (16) is used to obtain the envelope spaces corresponding to formulas (17) to (20):
preferably, in step 3.5, constructing the electric vehicle cluster characteristic model as follows:
in the formula (I), the compound is shown in the specification,
and
respectively representing the charging and discharging scheduling power of the generalized energy storage model of the charging station j in the time period t; sj,tAnd representing the electric quantity of the generalized energy storage model of the charging station j in the time period t.
Preferably, step 4 specifically includes:
step 4.1: calculating parameters of electric vehicle cluster characteristics
ΔSj,trepresenting the electric quantity change of the charging station j generalized energy storage model caused by the grid connection state change of the electric automobile in the t period;
step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric vehicle cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource:
in the formula, Sj,tThe electric quantity of the generalized energy storage model of the charging station j in the time period t is obtained;
respectively is the charging power and the discharging power of the electric automobile n in the time period t; etachAnd ηdisRespectively charge and discharge efficiency; etarefSupplementing the discharge with a factor; t is a scheduling time interval set; Δ t represents a time window.
Preferably, in step 4.1, the parameter calculation formula of the electric vehicle cluster characteristic is as follows:
wherein the content of the first and second substances,
respectively representing the upper limits of charging and discharging power of the electric automobile n;
sn,arrivalthe initial electric quantity of the electric automobile; sn,leaveThe off-grid electric quantity of the electric automobile;
Xn,tindicating the state of the electric vehicle n during the time period t.
Preferably, in step 4, a historical data set of the electric vehicle is predefined, the charging station defines and records daily service information of the electric vehicle according to the historical data set, then electric vehicle cluster characteristic parameters based on the historical data are calculated, a variable space of the electric vehicle cluster is compressed into a variable space of a charging station generalized energy storage model, and the response capability of the generalized energy storage model as a flexible energy storage resource is obtained.
The invention also provides a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis system, which comprises:
the electric automobile individual model building module is used for analyzing physical characteristics and operating characteristics of the electric automobile and building an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
the electric vehicle aggregation model building module is used for building an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model building module is used for extending the electric vehicle individual model definition domain to the same scheduling period, carrying out cluster modeling on the large-scale electric vehicle grid connection based on Minkowski, obtaining an electric vehicle cluster characteristic model, and regarding the electric vehicle cluster characteristic model as a charging station generalized energy storage model;
and the response capability analysis module is used for calculating characteristic parameters of the electric automobile cluster, compressing the variable space of the electric automobile cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource.
The beneficial effect that this application reached:
the method analyzes the physical characteristics and the operating characteristics of the electric automobile, constructs an electric automobile polymerization model, carries out cluster modeling on the large-scale electric automobile grid connection based on Minkowski, calculates to obtain the enveloping space of the clustered electric automobile, compresses the variable space of the electric automobile cluster into the variable space of the generalized energy storage model of the charging station, realizes the response potential analysis of the electric automobile, and enables the charging station to participate in the load dispatching of the power grid as a whole.
The electric vehicle charging station is used as a natural aggregator and participates in power grid load dispatching as a flexible load through charging and discharging of electric vehicles in a management station. When the electric vehicle charging station participates in power grid load scheduling, adjustment compensation can be obtained, the electric vehicle charging station belongs to the field of power markets, for example, the electric vehicle charging station participates in a power grid peak regulation auxiliary service market, capacity participating in peak regulation can be declared according to calculated response potential, compensation corresponding to the load capacity participating in peak regulation can be obtained through modes of transferring charging time, reducing charging amount or discharging to a power grid, and the like, and therefore income is increased. In addition, the electric automobile participating in the power grid load dispatching can further achieve peak regulation, voltage regulation, reduction of local blockage of a power grid, improvement of the distributed new energy consumption level, reduction of carbon emission of power production, promotion of achievement of a low-carbon target and achievement of multiple win.
Drawings
FIG. 1 is a flow chart of a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method of the present invention;
figure 2 is a minkowski sum algorithm schematic.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the method for analyzing the characteristics of the large-scale electric vehicle cluster based on minkowski sum comprises the following steps:
step 1: analyzing physical characteristics and operating characteristics of the electric automobile, and constructing an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
the electric automobile has load translation and reverse power supply capacity, and an individual model thereof is shown in formulas (1) to (5):
in the formula (I), the compound is shown in the specification,
respectively is the charging power and the discharging power of the electric automobile n in the time period t;
respectively representing the upper limits of charging and discharging power of the electric automobile n;
representing an n grid-connected time period set of the electric automobile;
in the formula, sn,tAnd sn,t-1Respectively representing the battery capacity of the electric vehicle n in a time period t and a previous time period; etach、ηdisRespectively the charging and discharging efficiency of the electric automobile; Δ t represents a time window; etarefRepresents a discharge compensation coefficient determined by a discharge loss;
in the formula (I), the compound is shown in the specification,
representing a battery level safety boundary of the electric vehicle n;
the electric vehicle can only be in a charging state or a discharging state at the same time, so that the electric vehicle has the following components:
step 2: constructing an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of a charging station;
the electric vehicle charging station is used as a natural aggregator, the electric vehicle charging station participates in power grid load dispatching as a flexible load through charging and discharging of electric vehicles in a management station, the electric vehicle charging station directly participates in power grid load dispatching and is the total load of the charging station, and an electric vehicle aggregation model is constructed according to the following formulas (6) to (7):
in the formula (I), the compound is shown in the specification,
and
respectively the total charging power and the total discharging power of the charging station j in the time period t;
t is a set of scheduling periods.
The expressions (6) - (7) are the total charging and discharging power of the charging station and also the basis for later participation in the load scheduling of the power grid, the charging and discharging scheduling power of each period of the electric vehicle is obtained by the Minkowski method, and the total charging station load (the expressions (6) - (7)) is a part of the electric vehicle cluster characteristic model (the expressions (21) - (22)).
And step 3: extending the electric vehicle individual model definition domain to the same scheduling period, and carrying out cluster modeling on the large-scale electric vehicle grid-connected based on Minkowski, so as to obtain an electric vehicle cluster characteristic model which is regarded as a charging station generalized energy storage model;
based on Minkowski and the decision space of the individual superposition electric vehicles, the charging station as a whole participates in the load dispatching of the power grid.
Minkowski sums As shown in FIG. 2, the Minkowski sum algorithm presupposes that the two variable spaces have the same domain of definition.
Due to the difference of individual grid-connected time of the electric automobile, the definition domain of the electric automobile
Heterogeneity exists, and the heterogeneity needs to be extended to the same scheduling time period T, so that an electric vehicle cluster characteristic model is obtained, and the electric vehicle cluster characteristic model can also be regarded as a charging station generalized energy storage model.
Step 3.1: grid-connected state X of electric automobilen,tThe parking time of the electric automobile can be directly calculated to obtain:
in the formula, Xn,tRepresenting the state of the electric vehicle n in the t period; xn,t1 means that the electric vehicle n is in a grid-connected state in the period t,
indicating the time at which the electric vehicle n arrives at the charging station,
indicating the time at which the electric vehicle n leaves the charging station.
Step 3.2: extending the electric automobile individual model definition domain to the same scheduling period, so that the electric automobile individual decision space Minkowski additivity;
still further, extending the domain of equations (1) - (4) to the full scheduling period T, makes the electric vehicle individual decision space minkowski additivity:
step 3.3: the battery electric quantity s of the electric automobile n in the step 3.2 in the time period tn,tThe integration is carried out according to three stages in the whole grid connection process:
the three stages in the whole grid connection process comprise:
and a network access time period:
the characteristic of the electric automobile in the network access period is that the initial electric quantity s of the electric automobile needs to be consideredn,arrivalThe battery power s of the previous time period is hidden in the formulan,t-1＝0，
Namely satisfies Xn,t(Xn,t-Xn,t-1)＝1。
And (3) normal grid connection time period:
the normal grid connection time period of the electric vehicle, also called a general time period, can be degraded into formula (3).
An off-grid period:
the off-grid time interval characteristic of the electric automobile is that the off-grid electric quantity s of the electric automobile needs to be consideredn,leaveIn the formula is hidden
And
namely satisfies Xn,t-1(Xn,t-1-Xn,t)＝1。
Formulae (13) - (15) have minkowski additivity, further integrated as:
step 3.4: the minkowski addition formula (9), formula (10), formula (12) and formula (16) are again used to obtain envelope spaces corresponding to formulae (17) to (20):
the application of this part of the formula, minkowski sum, first decomposes the formula (11) into three stages of charging and discharging, making it minkowski additivity, resulting in the formula (16), again according to the minkowski sum and a determined envelope space (formulas (17) - (20), similar to the dimensions of the dotted line in fig. 2.
Step 3.5: the electric automobile cluster characteristic model is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively representing the charging and discharging scheduling power of the generalized energy storage model of the charging station j in the time period t; sj,tAnd representing the electric quantity of the generalized energy storage model of the charging station j in the time period t.
And 4, step 4: calculating characteristic parameters of the electric automobile cluster, compressing a variable space of the electric automobile cluster into a variable space of a generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as flexible energy storage resources.
Step 4.1: calculating parameters of electric vehicle cluster characteristics
The parameter calculation formula of the cluster characteristic of the electric automobile is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively representing the maximum charging and discharging power of the generalized energy storage model of the charging station j in the time period t;
ΔSj,tand representing the electric quantity change of the charging station j generalized energy storage model caused by the grid connection state change of the electric automobile in the t period.
Step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric vehicle cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource:
in the formula (I), the compound is shown in the specification,
and
the maximum charging power and the maximum discharging power of the generalized energy storage model of the charging station j in the time period t are respectively obtained; sj,tFor the electric quantity of the generalized energy storage model of the charging station j in the time period t,
and
upper and lower limits of its boundary, respectively; delta Sj,tThe method comprises the following steps that (1) the electric quantity change of a charging station j generalized energy storage model caused by the change of the grid-connected state of the electric automobile in a t period is realized; etachAnd ηdisRespectively charge and discharge efficiency; etarefThe discharge compensation factor is determined by the discharge loss.
The essence of the formula (32) is to project the variable space of the electric vehicle individual to a hypercube space, and simultaneously, the constraint relation among the variables is preserved, and the electric vehicle is integrated
The model is compressed into a generalized energy storage model, so that the dimensionality of the model is greatly reduced.
All feasible charging and discharging decisions of the charging station are contained in the hypercube space, and parameters of the cluster characteristics of the electric vehicles
Determines a generalized energy storage model asResponse capability of the flexible storage resource.
During specific implementation, a historical data set of the electric automobile is predefined, the charging station records daily service information of the electric automobile according to the historical data set definition, then electric automobile cluster characteristic parameters based on the historical data are calculated, a variable space of an electric automobile cluster is compressed into a variable space of a generalized energy storage model of the charging station, and the response capability of the generalized energy storage model as a flexible energy storage resource is obtained.
The invention relates to a Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis system, which comprises:
the electric automobile individual model building module is used for analyzing physical characteristics and operating characteristics of the electric automobile and building an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
the electric vehicle aggregation model building module is used for building an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model building module is used for extending the electric vehicle individual model definition domain to the same scheduling period, carrying out cluster modeling on the large-scale electric vehicle grid connection based on Minkowski, obtaining an electric vehicle cluster characteristic model, and regarding the electric vehicle cluster characteristic model as a charging station generalized energy storage model;
and the response capability analysis module is used for calculating characteristic parameters of the electric automobile cluster, compressing the variable space of the electric automobile cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource.
The method analyzes the physical characteristics and the operating characteristics of the electric automobile, constructs an electric automobile polymerization model, carries out cluster modeling on the large-scale electric automobile grid connection based on Minkowski, calculates to obtain the enveloping space of the clustered electric automobile, compresses the variable space of the electric automobile cluster into the variable space of the generalized energy storage model of the charging station, realizes the response potential analysis of the electric automobile, and enables the charging station to participate in the load dispatching of the power grid as a whole.
The electric vehicle charging station is used as a natural aggregator and participates in power grid load dispatching as a flexible load through charging and discharging of electric vehicles in a management station. When the electric vehicle participates in power grid load scheduling, adjustment compensation can be obtained, and the electric vehicle charging station belongs to the field of power markets, for example, the electric vehicle charging station participates in a power grid peak regulation auxiliary service market, can report the capacity participating in peak regulation according to the calculated response potential, and can obtain compensation corresponding to the load capacity participating in peak regulation by transferring charging time, reducing the charging amount or discharging to a power grid and the like, so that the income is increased.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (13)
1. The Minkowski-sum-based large-scale electric vehicle cluster characteristic analysis method is characterized by comprising the following steps of:
the method comprises the following steps:
step 1: analyzing physical characteristics and operating characteristics of the electric automobile, and constructing an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
step 2: constructing an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of a charging station;
and step 3: extending the electric vehicle individual model definition domain to the same scheduling period, and carrying out cluster modeling on the large-scale electric vehicle grid-connected based on Minkowski, so as to obtain an electric vehicle cluster characteristic model which is regarded as a charging station generalized energy storage model;
and 4, step 4: calculating characteristic parameters of the electric automobile cluster, compressing a variable space of the electric automobile cluster into a variable space of a generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as flexible energy storage resources.
2. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 1, characterized in that:
in the step 1, the electric automobile has load translation and reverse power supply capacity, and an individual model of the electric automobile is shown in formulas (1) to (5):
in the formula (I), the compound is shown in the specification,
respectively is the charging power and the discharging power of the electric automobile n in the time period t;
respectively representing the upper limits of charging and discharging power of the electric automobile n;
representing an n grid-connected time period set of the electric automobile;
in the formula, sn,tAnd sn,t-1Battery respectively representing electric automobile n in t period and previous periodAn amount of electricity; etach、ηdisRespectively the charging and discharging efficiency of the electric automobile; Δ t represents a time window; etarefRepresents a discharge compensation coefficient determined by a discharge loss;
in the formula (I), the compound is shown in the specification,
representing a battery level safety boundary of the electric vehicle n;
the electric vehicle can only be in a charging state or a discharging state at the same time, so that the electric vehicle has the following components:
3. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 1, characterized in that:
in the step 2, the electric vehicle charging station is used as a natural aggregator, the electric vehicle charging station is used as a flexible load to participate in power grid load scheduling through charging and discharging of the electric vehicle in the management station, the electric vehicle charging station directly participates in power grid load scheduling and is the total load of the charging station, and an electric vehicle aggregation model is constructed according to the following formulas (6) to (7):
in the formula (I), the compound is shown in the specification,
and
respectively the total charging power and the total discharging power of the charging station j in the time period t;
t is a set of scheduling periods.
4. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 1, characterized in that:
the step 3 specifically comprises the following steps:
step 3.1: calculating grid-connected state X of electric automobile based on parking time of electric automobilen,t；
Step 3.2: based on grid-connected state Xn,tExtending the definition domains of the charging and discharging power, the battery electric quantity and the battery electric quantity safety boundary in the electric automobile individual model to the same scheduling period, so that the decision space of the electric automobile individual model is Minkowski additivity;
step 3.3: integrating the battery electric quantity after the domain extension in the electric automobile individual model in the step 3.2 according to the three stages of the battery electric quantity in the network access period, the normal network connection period and the network disconnection period in the whole network connection process;
step 3.4: utilizing Minkowski addition to obtain the charging and discharging power after the domain extension, the battery electric quantity safety boundary and the envelope space corresponding to the integrated battery electric quantity in the electric automobile individual model;
step 3.5: and constructing an electric vehicle cluster characteristic model, and regarding the electric vehicle cluster characteristic model as a charging station generalized energy storage model.
5. The Minkowski-based large-scale electric vehicle cluster characteristic analysis method according to claim 4, wherein:
step 3.1, the grid-connected state X of the electric automobilen,tThe parking time of the electric automobile is directly calculated to obtain:
in the formula, Xn,tRepresenting the state of the electric vehicle n in the t period; xn,t1 means that the electric vehicle n is in a grid-connected state in the period t,
indicating the time at which the electric vehicle n arrives at the charging station,
indicating the time at which the electric vehicle n leaves the charging station.
6. The Minkowski-based large-scale electric vehicle cluster characteristic analysis method according to claim 5, wherein:
in step 3.2, the defined domains of the charging and discharging power, the battery power and the battery power safety boundary in the electric vehicle individual model are extended to the full-modulation period T, so that the decision space of the electric vehicle individual is minkowski additivity:
sn,tand sn,t-1Respectively representing the battery capacity of the electric vehicle n in a time period t and a previous time period;
ηch、ηdisrespectively the charging and discharging efficiency of the electric automobile;
ηrefrepresents a discharge compensation coefficient; Δ t represents a time window;
7. The Minkowski-based large-scale electric vehicle cluster characteristic analysis method according to claim 6, wherein:
in step 3.3, step 3.2 is repeatedBattery electric quantity s of electric automobile n in t periodn,tThe integration is carried out according to three stages of a network access time interval, a normal grid connection time interval and a grid disconnection time interval in the whole grid connection process, and the integration method specifically comprises the following steps:
and a network access time period:
wherein s isn,arrivalThe initial electric quantity of the electric automobile is the battery electric quantity s of the previous time periodn,t-1＝0，
Namely satisfies Xn,t(Xn,t-Xn,t-1)＝1；
And (3) normal grid connection time period:
an off-grid period:
wherein s isn,leaveFor the off-grid electric quantity of the electric automobile, the formula implies
And
namely satisfies Xn,t-1(Xn,t-1-Xn,t)＝1；
Formulae (13) - (15) have minkowski additivity, further integrated as:
8. the Minkowski-based large-scale electric vehicle cluster characteristic analysis method according to claim 7, wherein:
in step 3.4, the envelope space corresponding to the formulas (17) to (20) is obtained by using minkowski addition processing formulas (9), (10), (12) and (16):
the envelope space is used as the electricity utilization characteristic of the electric automobile cluster, namely the charge and discharge electricity quantity of the generalized energy storage model of the charging station.
9. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 8, wherein:
in step 3.5, constructing an electric automobile cluster characteristic model as follows:
in the formula (I), the compound is shown in the specification,
and
respectively representing the charging and discharging scheduling power of the generalized energy storage model of the charging station j in the time period t; sj,tAnd representing the electric quantity of the generalized energy storage model of the charging station j in the time period t.
10. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 1, characterized in that:
the step 4 specifically comprises the following steps:
step 4.1: calculating parameters of electric vehicle cluster characteristics
ΔSj,trepresenting the electric quantity change of the charging station j generalized energy storage model caused by the grid connection state change of the electric automobile in the t period;
step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric vehicle cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource:
in the formula, Sj,tThe electric quantity of the generalized energy storage model of the charging station j in the time period t is obtained;
respectively is the charging power and the discharging power of the electric automobile n in the time period t; etachAnd ηdisRespectively charge and discharge efficiency; etarefSupplementing the discharge with a factor; t is a scheduling time interval set; Δ t represents a time window.
11. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 10, wherein:
in step 4.1, a parameter calculation formula of the electric automobile cluster characteristic is as follows:
wherein the content of the first and second substances,
respectively representing the upper limits of charging and discharging power of the electric automobile n;
sn,arrivalthe initial electric quantity of the electric automobile; sn,leaveThe off-grid electric quantity of the electric automobile;
Xn,tindicating the state of the electric vehicle n during the time period t.
12. -a minkowski-sum-based large-scale electric vehicle cluster characterization method as claimed in claim 1, characterized in that:
in the step 4, a historical data set of the electric automobile is predefined, the charging station records daily service information of the electric automobile according to the historical data set definition, then electric automobile cluster characteristic parameters based on the historical data are calculated, a variable space of the electric automobile cluster is compressed into a variable space of a generalized energy storage model of the charging station, and the response capability of the generalized energy storage model as a flexible energy storage resource is obtained.
13. Minkowski-based large-scale electric vehicle cluster characteristic analysis system for realizing the Minkowski-based large-scale electric vehicle cluster characteristic analysis method as claimed in any one of claims 1 to 12, characterized in that:
the system comprises:
the electric automobile individual model building module is used for analyzing physical characteristics and operating characteristics of the electric automobile and building an electric automobile individual model, wherein the electric automobile individual model comprises electric automobile charging and discharging power, battery electric quantity safety boundary and charging and discharging states;
the electric vehicle aggregation model building module is used for building an electric vehicle aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model building module is used for extending the electric vehicle individual model definition domain to the same scheduling period, carrying out cluster modeling on the large-scale electric vehicle grid connection based on Minkowski, obtaining an electric vehicle cluster characteristic model, and regarding the electric vehicle cluster characteristic model as a charging station generalized energy storage model;
and the response capability analysis module is used for calculating characteristic parameters of the electric automobile cluster, compressing the variable space of the electric automobile cluster into the variable space of the generalized energy storage model of the charging station, and obtaining the response capability of the generalized energy storage model as the flexible energy storage resource.
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