CN114398723B - Large-scale electric vehicle cluster characteristic analysis method and system based on Minkowski sum - Google Patents
Large-scale electric vehicle cluster characteristic analysis method and system based on Minkowski sum Download PDFInfo
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Abstract
The application discloses a mass electric vehicle cluster characteristic analysis method and system based on Minkowski sum, wherein the method comprises the following steps: constructing an electric automobile individual model; constructing an electric automobile aggregation model participating in power grid load scheduling, namely a total load model of the charging station; extending the individual model definition domain of the electric vehicle 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; calculating characteristic parameters of the electric automobile clusters, compressing variable space of the electric automobile clusters into variable space of the charging station generalized energy storage model, and obtaining response capability of the generalized energy storage model serving as flexible storage resources. The invention can realize response potential analysis of the electric automobile, so that the charging station participates in power grid load dispatching as a whole, thereby obtaining adjustment compensation and increasing income.
Description
Technical Field
The invention belongs to the technical field of power system dispatching control, and relates to a mass electric vehicle cluster characteristic analysis method and system based on Minkowski sum.
Background
According to the latest development planning of new energy automobile industry, the new energy automobile charging pile is listed as one of seven industrial directions of 'new construction', which is necessary to further promote the rapid development of the electric automobile. With the development of battery technology and the increasingly perfect supporting infrastructure, the electric automobile conservation amount in China is rising year by year. According to the industrial development planning, the electric vehicles in China can reach 8300 ten thousand by 2030, the equivalent energy storage capacity can reach 50 hundred million kilowatt-hours, the electric vehicle charging demand can be 6 to 7 percent of the total social electricity consumption, and the maximum charging load can be 11 to 12 percent of the power grid load. Therefore, the development of large-scale electric vehicles has become the necessary trend of electric energy replacement and green traffic.
On one hand, a large amount of electric automobile loads are randomly connected into the system, so that impact can be caused on the power grid, and the outstanding contradiction such as peak-valley difference, voltage offset, partial blocking and the like of the power system can be aggravated, so that effective management is required; on the other hand, the distributed energy storage characteristic of the electric automobile provides rich schedulable resources for power grid peak regulation, voltage regulation, new energy consumption and the like, and effective management is needed.
How to ensure the safe operation of the urban power grid, meet the requirement of large-scale electric vehicle access to the maximum extent, fully utilize the schedulable resources of the electric vehicle, support the development of the urban energy Internet and provide unprecedented great 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 analyze cluster characteristics of the electric automobile.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a method and a system for analyzing the cluster characteristics of large-scale electric vehicles based on Minkowski sum, which project the variable space of an electric vehicle to a hypercube space, simultaneously reserve the constraint relation among variables, wherein the hypercube space contains all feasible charge and discharge decisions of a charging station, compress an electric vehicle set into a generalized energy storage model, greatly reduce the dimension of the model, solve the problem of model and data dimension explosive development when the large-scale electric vehicle is connected into a power grid, further excavate the response capability of the generalized energy storage model as flexible charge resources, and comprehensively support the electric vehicle to participate in power grid load scheduling.
In order to achieve the above object, the present invention adopts the following technical scheme:
the mass electric vehicle cluster characteristic analysis method based on the Minkowski sum comprises the following steps:
step 1: analyzing physical characteristics and operation characteristics of the electric automobile, and constructing an electric automobile individual model which comprises charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state of the electric automobile;
step 2: constructing an electric automobile aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
step 3: extending the individual model definition domain of the electric vehicle 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;
step 4: calculating characteristic parameters of the electric automobile clusters, compressing variable space of the electric automobile clusters into variable space of the charging station generalized energy storage model, and obtaining response capability of the generalized energy storage model serving as flexible storage resources.
The invention further comprises the following preferable schemes:
preferably, in step 1, the electric vehicle has load translation and reverse power supply capabilities, and the individual models thereof are shown in formulas (1) - (5):
in the method, in the process of the invention,charging and discharging power of the electric automobile n in the period t are respectively; />The upper limit of the charge and discharge power of the electric automobile n is respectively set; />Representing an electric automobile n grid-connected period set;
wherein s is n,t Sum s n,t-1 Respectively representing the battery electric quantity of the electric automobile n in the period t and the period last; η (eta) ch 、η dis Charging and discharging efficiencies of the electric automobile respectively; Δt represents a time window; η (eta) ref Representing a discharge compensation coefficient, determined by the discharge loss;
in the method, in the process of the invention,representing a battery power safety boundary of the electric automobile n;
the electric automobile can only be in a charging state or a discharging state at the same time, so that the electric automobile has the following components:
preferably, in step 2, the electric vehicle charging station is used as a natural aggregator, and the electric vehicle aggregation model is constructed as shown in formulas (6) - (7) by managing charging and discharging of electric vehicles in the station, participating in power grid load dispatching as flexible load, and directly participating in the power grid load dispatching is the total load of the charging station:
in the method, in the process of the invention,and->The total charge and discharge power of the charging station j in the period t is respectively;
and->Charging and discharging schedule of electric automobile n in t periodA power;
an electric car set in charging station j;
t is the set of scheduling periods.
Preferably, step 3 specifically includes:
step 3.1: electric vehicle parking time-based grid-connected state X of electric vehicle n,t ；
Step 3.2: based on grid-connected state X n,t Extending the definition domain of the safety boundaries of the charge and discharge power, the battery power and the battery power in the electric automobile individual model to the same scheduling period, so that the decision space of the electric automobile individual is minkowski additivity;
step 3.3: integrating the battery power after the domain prolongation in the electric automobile individual model in the step 3.2 according to three stages of a network access period, a normal network connection period and a network disconnection period in the whole network connection process;
step 3.4: the Minkowski addition is utilized to obtain the charge and discharge power, the battery electric quantity safety boundary and the envelope space corresponding to the integrated battery electric quantity after the extension of the definition domain in the electric automobile individual model;
step 3.5: and constructing an electric automobile cluster characteristic model, and regarding the electric automobile cluster characteristic model as a charging station generalized energy storage model.
Preferably, in step 3.1, the grid-connected state X of the electric vehicle n,t The parking time of the electric automobile is directly calculated to obtain:
wherein X is n,t Representing the state of the electric automobile n in the t period; x is X n,t =1 indicates that electric car n is in a grid-connected state for period t,indicating the time when electric vehicle n arrives at the charging station,/>indicating the time when electric vehicle n leaves the charging station.
Preferably, in step 3.2, the definition domain of the safety boundaries of the charge and discharge power, the battery power and the battery power in the electric automobile individual model is extended to the full scheduling period T, so that the decision space of the electric automobile individual is minkowski additivity:
charging and discharging power of the electric automobile n in the period t are respectively;
the upper limit of the charge and discharge power of the electric automobile n is respectively set;
s n,t sum s n,t-1 Respectively representing the battery electric quantity of the electric automobile n in the period t and the period last;
η ch 、η dis charging and discharging efficiencies of the electric automobile respectively;
η ref representing the discharge compensation coefficient; Δt represents a time window;
representing a battery power safety boundary of the electric automobile n;
an electric car set in charging station j; t is the set of scheduling periods.
Preferably, in step 3.3, the battery power s of the electric automobile n in step 3.2 in the period t is set n,t The integration is carried out according to three stages of the network access period, the normal network connection period and the off-network period in the whole network connection process, and the method is specific:
network access period:
wherein s is n,arrival The initial electric quantity of the electric automobile is the electric quantity s of the battery in the last period n,t-1 ＝0，I.e. satisfy X n,t (X n,t -X n,t-1 )＝1；
Normal grid-connected period:
off-grid period:
wherein s is n,leave Is off-grid electric quantity of the electric automobile, and is hidden inAnd-> I.e. satisfy X n,t-1 (X n,t-1 -X n,t )＝1；
Formulas (13) - (15) have minkowski additivity, further integrated as:
preferably, in step 3.4, the envelope space corresponding to formulas (17) - (20) is obtained by minkowski addition processing formula (9), formula (10), formula (12) and formula (16):
preferably, in step 3.5, the electric automobile cluster characteristic model is constructed as follows:
in the method, in the process of the invention,and->Respectively representing the charge and discharge scheduling power of the generalized energy storage model of the charging station j in the period t; s is S j,t And the electric quantity of the generalized energy storage model of the charging station j in the period t is represented.
Preferably, step 4 specifically includes:
step 4.1: calculating parameters of electric automobile cluster characteristics
And->Respectively representing the maximum charge and discharge power of the generalized energy storage model of the charging station j in the period t;
and->Representing the electric quantity boundary of a generalized energy storage model of the charging station j in a t period;
ΔS j,t the method comprises the steps that the electric quantity change of a charging station j generalized energy storage model caused by electric vehicle grid-connected state change in a t period is represented;
step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric automobile cluster into the variable space of the charging station generalized energy storage model, and obtaining the response capability of the generalized energy storage model as the flexible storage resource:
wherein S is j,t The electric quantity of the generalized energy storage model of the charging station j in the period t is calculated;charging and discharging power of the electric automobile n in the period t are respectively; η (eta) ch And eta dis Respectively charging and discharging efficiency; η (eta) ref Supplementing the discharge with a coefficient; t is a scheduling period set; Δt represents a time window.
Preferably, in step 4.1, the parameter calculation formula of the electric automobile cluster characteristic is:
wherein,the upper limit of the charge and discharge power of the electric automobile n is respectively set;
representing a battery power safety boundary of the electric automobile n;
an electric car set in charging station j; t is a scheduling period set;
s n,arrival the initial electric quantity of the electric automobile; s is(s) n,leave The off-grid electric quantity of the electric automobile;
X n,t the state of the electric vehicle n in the t period is shown.
Preferably, in step 4, a historical data set of the electric vehicle is defined in advance, the charging station records daily service information of the electric vehicle according to the historical data set definition, then characteristic parameters of an electric vehicle cluster based on the historical data are calculated, and a variable space of the electric vehicle cluster is compressed into a variable space of a generalized energy storage model of the charging station, so that the response capability of the generalized energy storage model serving as flexible storage resources is obtained.
The invention also provides a mass electric vehicle cluster characteristic analysis system based on the Minkowski sum, which comprises:
the electric automobile individual model construction module is used for analyzing the physical characteristics and the operation characteristics of the electric automobile and constructing an electric automobile individual model, and comprises electric automobile charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state;
the electric vehicle aggregation model construction module is used for constructing an electric vehicle aggregation model which participates in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model construction module is used for extending the definition domain of the electric vehicle individual model 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;
the response capacity analysis module is used for calculating characteristic parameters of the electric automobile clusters, compressing variable spaces of the electric automobile clusters into variable spaces of the charging station generalized energy storage model, and obtaining the response capacity of the generalized energy storage model serving as flexible storage resources.
The beneficial effect that this application reached:
according to the invention, the physical characteristics and the operation characteristics of the electric vehicles are analyzed, an electric vehicle aggregation model is constructed, the clustered electric vehicles are subjected to clustered modeling based on Minkowski and large-scale electric vehicle grid connection, the envelope space of the clustered electric vehicles is calculated, the variable space of the electric vehicle cluster is compressed into the variable space of the charging station generalized energy storage model, the response potential analysis of the electric vehicles is realized, and the charging station is used as a whole to participate in power grid load scheduling.
The electric vehicle charging station is used as a natural aggregator, and takes part in power grid load dispatching as a flexible load by managing charging and discharging of electric vehicles in the station. When the electric vehicle charging station participates in power grid load dispatching, adjustment compensation can be obtained, and the electric vehicle charging station belongs to the category of electric power markets, such as the auxiliary service market of power grid peak shaving, and the electric vehicle charging station can declare the capacity participating in peak shaving according to calculated response potential and obtain compensation corresponding to the load capacity participating in peak shaving by transferring charging time, reducing charging amount or discharging to the power grid and the like, so that income is increased. In addition, the electric automobile participates in power grid load dispatching, so that peak regulation, voltage regulation, partial blocking of a power grid reduction, distributed new energy consumption level improvement, carbon emission reduction of power production are further achieved, achievement of a low-carbon target is facilitated, and win-win is achieved.
Drawings
FIG. 1 is a flow chart of a method for analyzing the cluster characteristics of a large-scale electric vehicle based on Minkowski sum;
fig. 2 is a schematic diagram of the minkowski and algorithm.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
As shown in fig. 1, the mass electric vehicle cluster characteristic analysis method based on Minkowski sum of the invention comprises the following steps:
step 1: analyzing physical characteristics and operation characteristics of the electric automobile, and constructing an electric automobile individual model which comprises charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state of the electric automobile;
the electric automobile has load translation and reverse power supply capacity, and the individual model is shown in formulas (1) - (5):
in the method, in the process of the invention,charging and discharging power of the electric automobile n in the period t are respectively; />The upper limit of the charge and discharge power of the electric automobile n is respectively set; />Representing an electric automobile n grid-connected period set;
wherein s is n,t Sum s n,t-1 Respectively representing the battery electric quantity of the electric automobile n in the period t and the period last; η (eta) ch 、η dis Charging and discharging efficiencies of the electric automobile respectively; Δt represents a time window; η (eta) ref Representing a discharge compensation coefficient, determined by the discharge loss;
in the method, in the process of the invention,representing a battery power safety boundary of the electric automobile n;
the electric automobile can only be in a charging state or a discharging state at the same time, so that the electric automobile has the following components:
step 2: constructing an electric automobile aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle charging station is taken as a natural aggregator, the charging and discharging of electric vehicles in the station are managed to participate in power grid load scheduling as flexible loads, the electric vehicle charging station directly participates in the power grid load scheduling to be the total load of the charging station, and an electric vehicle aggregation model is constructed as shown in formulas (6) - (7):
in the method, in the process of the invention,and->The total charge and discharge power of the charging station j in the period t is respectively;
and->The power is respectively scheduled for charging and discharging of the electric automobile n in the period t;
an electric car set in charging station j;
t is the set of scheduling periods.
The formulas (6) - (7) are the total charge and discharge power of the charging station, which is also the basis for participating in power grid load dispatching in the later period, the charge and discharge dispatching power of the electric automobile in each period is obtained by the Minkowski sum method, and the total charge and discharge power of the charging station (formulas (6) - (7)) is a part of the electric automobile cluster characteristic model (formulas (21) - (22)).
Step 3: extending the individual model definition domain of the electric vehicle 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;
based on Minkowski and superposition of the decision space of the individual electric vehicles, the charging station as a whole participates in the power grid load scheduling.
Minkowski and as shown in fig. 2, the precondition for minkowski and the algorithm is that the two variable spaces have the same definition domain.
Because of the difference of individual grid connection time of electric vehicles, the definition domain of the electric vehiclesThe heterogeneity exists, and the heterogeneity needs to be extended to the same scheduling period T, so that an electric automobile cluster characteristic model is obtained, and the electric automobile cluster characteristic model can also be regarded as a charging station generalized energy storage model.
Step 3.1: grid-connected state X of electric automobile n,t The parking time of the electric automobile can be directly calculated to obtain:
wherein X is n,t Representing the state of the electric automobile n in the t period; x is X n,t =1 indicates that electric car n is in a grid-connected state for period t,indicating the time of arrival of electric vehicle n at the charging station, +.>Indicating the time when electric vehicle n leaves the charging station.
Step 3.2: extending the definition domain of the individual model of the electric automobile to the same scheduling period, so that the decision space of the individual electric automobile is minkowski additivity;
further, the definition domain of the formulas (1) - (4) is extended to the full scheduling period T, so that the decision space of the individual electric automobile is minkowski additivity:
step 3.3: the battery electric quantity s of the electric automobile n in the step 3.2 in the period t is calculated n,t The integration is carried out according to three stages in the whole grid connection process:
the three stages in the whole grid connection process comprise:
network access period:
the characteristic of the network access time period of the electric automobile is that the initial electric quantity s of the electric automobile needs to be considered n,arrival The battery power s of the last period is implied n,t-1 ＝0，I.e. satisfy X n,t (X n,t -X n,t-1 )＝1。
Normal grid-connected period:
the normal grid connection period of the electric automobile, which is also called a general period, can be degenerated into formula (3).
Off-grid period:
the off-grid period characteristic of the electric automobile is that off-grid electric quantity s of the electric automobile needs to be considered n,leave In which is impliedAnd-> I.e. satisfy X n,t-1 (X n,t-1 -X n,t )＝1。
Formulas (13) - (15) have minkowski additivity, further integrated as:
step 3.4: the envelope spaces corresponding to the formulas (17) to (20) are obtained by again using the minkowski addition processing formulas (9), (10), (12) and (16):
the partial formula, namely Minkowski sum, is applied by decomposing the formula (11) into three stages of charging and discharging to enable the charge and discharge to have Minkowski additivity, so as to obtain the formula (16), and the envelope space is used as the electric characteristics of the electric automobile cluster, namely the charging and discharging electric quantity of the charging station generalized energy storage model according to the Minkowski sum and determined envelope spaces (formulas (17) - (20) similar to the dimension of the dotted line in fig. 2.
Step 3.5: the electric automobile cluster characteristic model is:
in the method, in the process of the invention,and->Respectively representing the charge and discharge scheduling power of the generalized energy storage model of the charging station j in the period t; s is S j,t And the electric quantity of the generalized energy storage model of the charging station j in the period t is represented.
Step 4: calculating characteristic parameters of the electric automobile clusters, compressing variable space of the electric automobile clusters into variable space of the charging station generalized energy storage model, and obtaining response capability of the generalized energy storage model serving as flexible storage resources.
Step 4.1: calculating parameters of electric automobile cluster characteristics
The parameter calculation formula of the electric automobile cluster characteristic is as follows:
in the method, in the process of the invention,and->Respectively representing the maximum charge and discharge power of the generalized energy storage model of the charging station j in the period t;
and->Representing the electric quantity boundary of a generalized energy storage model of the charging station j in a t period;
ΔS j,t and the electric quantity change of the charging station j generalized energy storage model caused by the electric vehicle grid-connected state change in the period t is represented.
Step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric automobile cluster into the variable space of the charging station generalized energy storage model, and obtaining the response capability of the generalized energy storage model as the flexible storage resource:
in the method, in the process of the invention,and->Respectively obtaining maximum charge and discharge power of the generalized energy storage model of the charging station j in a period t; s is S j,t Electric quantity of the generalized energy storage model in t period for charging station j, < ->And->Upper and lower boundaries of the respective frame; ΔS j,t The method comprises the steps that the electric quantity change of a charging station j generalized energy storage model caused by the electric vehicle grid-connected state change in a t period is achieved; η (eta) ch And eta dis Respectively charging and discharging efficiency; η (eta) ref The discharge supplement factor is determined by the discharge loss.
The essence of formula (32) is to project the variable space of the individual electric vehicles into a hypercube space while preserving the constraint relationship between the variables, which aggregates the electric vehiclesThe model is compressed into a generalized energy storage model, so that the dimension of the model is greatly reduced.
The hypercube space contains all feasible charging and discharging decisions of the charging station, while parameters of electric vehicle cluster characteristicsDeciding a generalized energy storage model as flexible storageResponse capability of the payload resource.
In specific implementation, a historical data set of the electric vehicle is defined in advance, the charging station records daily service information of the electric vehicle according to the historical data set definition, then characteristic parameters of an electric vehicle cluster 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 serving as flexible storage resources is obtained.
The invention relates to a mass electric vehicle cluster characteristic analysis system based on Minkowski sum, which comprises:
the electric automobile individual model construction module is used for analyzing the physical characteristics and the operation characteristics of the electric automobile and constructing an electric automobile individual model, and comprises electric automobile charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state;
the electric vehicle aggregation model construction module is used for constructing an electric vehicle aggregation model which participates in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model construction module is used for extending the definition domain of the electric vehicle individual model 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;
the response capacity analysis module is used for calculating characteristic parameters of the electric automobile clusters, compressing variable spaces of the electric automobile clusters into variable spaces of the charging station generalized energy storage model, and obtaining the response capacity of the generalized energy storage model serving as flexible storage resources.
According to the invention, the physical characteristics and the operation characteristics of the electric vehicles are analyzed, an electric vehicle aggregation model is constructed, the clustered electric vehicles are subjected to clustered modeling based on Minkowski and large-scale electric vehicle grid connection, the envelope space of the clustered electric vehicles is calculated, the variable space of the electric vehicle cluster is compressed into the variable space of the charging station generalized energy storage model, the response potential analysis of the electric vehicles is realized, and the charging station is used as a whole to participate in power grid load scheduling.
The electric vehicle charging station is used as a natural aggregator, and takes part in power grid load dispatching as a flexible load by managing charging and discharging of electric vehicles in the station. When the electric vehicle participates in power grid load dispatching, adjustment compensation can be obtained, and the electric vehicle belongs to the category of electric power markets, such as the electric vehicle participates in a power grid peak shaving auxiliary service market, the electric vehicle charging station can declare the capacity participating in peak shaving according to calculated response potential, and obtains compensation corresponding to the load capacity participating in peak shaving by transferring charging time, reducing charging quantity or discharging to the power grid and other modes, so that income is increased.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only 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 to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (7)
1. The large-scale electric automobile cluster characteristic analysis method based on the Minkowski sum is characterized by comprising the following steps of:
the method comprises the following steps:
step 1: analyzing physical characteristics and operation characteristics of the electric automobile, and constructing an electric automobile individual model which comprises charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state of the electric automobile;
step 2: constructing an electric automobile aggregation model participating in power grid load scheduling, namely a total load model of the charging station;
step 3: extending the individual model definition domain of the electric vehicle 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; the method specifically comprises the following steps:
step 3.1: electric vehicle parking time-based grid-connected state X of electric vehicle n,t ；
Grid-connected state X of electric automobile n,t The parking time of the electric automobile is directly calculated to obtain:
wherein X is n,t Representing the state of the electric automobile n in the t period; x is X n,t =1 indicates that electric car n is in a grid-connected state for period t,indicating the time of arrival of electric vehicle n at the charging station, +.>Indicating a time when the electric vehicle n leaves the charging station;
step 3.2: based on grid-connected state X n,t Extending the definition domain of the safety boundaries of the charge and discharge power, the battery power and the battery power in the electric automobile individual model to the same scheduling period, so that the decision space of the electric automobile individual is minkowski additivity;
extending the definition domain of the safety boundaries of the charge and discharge power, the battery power and the battery power in the electric automobile individual model to the full scheduling period T, so that the decision space of the electric automobile individual is minkowski additivity:
charging and discharging power of the electric automobile n in the period t are respectively;
the upper limit of the charge and discharge power of the electric automobile n is respectively set;
s n,t sum s n,t-1 Respectively representing the battery electric quantity of the electric automobile n in the period t and the period last;
η ch 、η dis charging and discharging efficiencies of the electric automobile respectively;
η ref representing the discharge compensation coefficient; Δt represents a time window;
representing a battery power safety boundary of the electric automobile n;
an electric car set in charging station j; t is a scheduling period set;
step 3.3: integrating the battery power after the domain prolongation in the electric automobile individual model in the step 3.2 according to three stages of a network access period, a normal network connection period and a network disconnection period in the whole network connection process;
the battery electric quantity s of the electric automobile n in the step 3.2 in the period t is calculated n,t The integration is carried out according to three stages of the network access period, the normal network connection period and the off-network period in the whole network connection process, and the method is specific:
network access period:
wherein s is n,arrival The initial electric quantity of the electric automobile is the electric quantity s of the battery in the last period n,t-1 ＝0，I.e. satisfy X n,t (X n,t -X n,t-1 )＝1；
Normal grid-connected period:
off-grid period:
wherein s is n,leave Is off-grid electric quantity of the electric automobile, and is hidden inAnd-> I.e. satisfy X n,t-1 (X n,t-1 -X n,t )＝1；
Formulas (13) - (15) have minkowski additivity, further integrated as:
step 3.4: the Minkowski addition is utilized to obtain the charge and discharge power, the battery electric quantity safety boundary and the envelope space corresponding to the integrated battery electric quantity after the extension of the definition domain in the electric automobile individual model;
envelope spaces corresponding to formulas (17) - (20) are obtained by 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 electric quantity of the charging station generalized energy storage model;
step 3.5: constructing an electric automobile cluster characteristic model, and regarding the electric automobile cluster characteristic model as a charging station generalized energy storage model;
the electric automobile cluster characteristic model is constructed as follows:
in the method, in the process of the invention,and->Respectively representing the charge and discharge scheduling power of the generalized energy storage model of the charging station j in the period t; s is S j,t Representing the electric quantity of a generalized energy storage model of the charging station j in a t period;
step 4: calculating characteristic parameters of the electric automobile clusters, compressing variable space of the electric automobile clusters into variable space of the charging station generalized energy storage model, and obtaining response capability of the generalized energy storage model serving as flexible storage resources.
2. A mass electric vehicle cluster feature analysis method based on minkowski sum as claimed in claim 1, characterized by:
in the step 1, the electric automobile has load translation and reverse power supply capacity, and the individual model is shown in the formulas (1) - (5):
in the method, in the process of the invention,charging and discharging power of the electric automobile n in the period t are respectively; />The upper limit of the charge and discharge power of the electric automobile n is respectively set; />Representing an electric automobile n grid-connected period set;
wherein s is n,t Sum s n,t-1 Respectively representing the battery electric quantity of the electric automobile n in the period t and the period last; η (eta) ch 、η dis Charging and discharging efficiencies of the electric automobile respectively; Δt represents a time window; η (eta) ref Representing a discharge compensation coefficient, determined by the discharge loss;
in the method, in the process of the invention,representing a battery power safety boundary of the electric automobile n;
the electric automobile can only be in a charging state or a discharging state at the same time, so that the electric automobile has the following components:
3. a mass electric vehicle cluster feature analysis method based on minkowski sum as claimed in claim 1, characterized by:
in step 2, the electric vehicle charging station is used as a natural aggregator, the charging and discharging of the electric vehicle in the management station are used as flexible loads to participate in power grid load scheduling, the total load of the charging station is directly involved in the power grid load scheduling, and an electric vehicle aggregation model is constructed according to the following formulas (6) - (7):
in the method, in the process of the invention,and->The total charge and discharge power of the charging station j in the period t is respectively;
and->The power is respectively scheduled for charging and discharging of the electric automobile n in the period t;
an electric car set in charging station j;
t is the set of scheduling periods.
4. A mass electric vehicle cluster feature analysis method based on minkowski sum as claimed in claim 1, characterized by:
the step 4 specifically comprises the following steps:
step 4.1: calculating parameters of electric automobile cluster characteristics
And->Respectively representing the maximum charge and discharge power of the generalized energy storage model of the charging station j in the period t;
and->Representing the electric quantity boundary of a generalized energy storage model of the charging station j in a t period;
ΔS j,t the method comprises the steps that the electric quantity change of a charging station j generalized energy storage model caused by electric vehicle grid-connected state change in a t period is represented;
step 4.2: based on the parameters in the step 4.2, compressing the variable space of the electric automobile cluster into the variable space of the charging station generalized energy storage model, and obtaining the response capability of the generalized energy storage model as the flexible storage resource:
wherein S is j,t The electric quantity of the generalized energy storage model of the charging station j in the period t is calculated;charging and discharging power of the electric automobile n in the period t are respectively; η (eta) ch And eta dis Respectively charging and discharging efficiency; η (eta) ref Supplementing the discharge with a coefficient; t is a scheduling period set; Δt represents a time window.
5. -a method for mass electric vehicle cluster characteristics analysis based on minkowski sums according to claim 4, characterized in that:
in step 4.1, the parameter calculation formula of the electric automobile cluster characteristic is as follows:
wherein,respectively are electricUpper limit of charge and discharge power of the automobile n;
representing a battery power safety boundary of the electric automobile n;
an electric car set in charging station j; t is a scheduling period set;
s n,arrival the initial electric quantity of the electric automobile; s is(s) n,leave The off-grid electric quantity of the electric automobile;
X n,t the state of the electric vehicle n in the t period is shown.
6. A mass electric vehicle cluster feature analysis method based on minkowski sum as claimed in claim 1, characterized by:
in step 4, a historical data set of the electric vehicle is defined in advance, the charging station records daily service information of the electric vehicle according to the historical data set definition, then characteristic parameters of an electric vehicle cluster based on the historical data are calculated, variable space of the electric vehicle cluster is compressed into variable space of a charging station generalized energy storage model, and response capacity of the generalized energy storage model serving as flexible storage resources is obtained.
7. A mass electric vehicle cluster characteristic analysis system based on minkowski sums for implementing the mass electric vehicle cluster characteristic analysis method based on minkowski sums as claimed in any one of claims 1 to 6, characterized in that:
the system comprises:
the electric automobile individual model construction module is used for analyzing the physical characteristics and the operation characteristics of the electric automobile and constructing an electric automobile individual model, and comprises electric automobile charging and discharging power, battery electric quantity, a battery electric quantity safety boundary and a charging and discharging state;
the electric vehicle aggregation model construction module is used for constructing an electric vehicle aggregation model which participates in power grid load scheduling, namely a total load model of the charging station;
the electric vehicle cluster characteristic model construction module is used for extending the definition domain of the electric vehicle individual model 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;
the response capacity analysis module is used for calculating characteristic parameters of the electric automobile clusters, compressing variable spaces of the electric automobile clusters into variable spaces of the charging station generalized energy storage model, and obtaining the response capacity of the generalized energy storage model serving as flexible storage resources.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104022513A (en) * | 2014-05-30 | 2014-09-03 | 上海电力学院 | Multistage voltage control partitioning method for grid connection of electric automobile charge, discharge and storage integrated station |
| CN109034648A (en) * | 2018-08-13 | 2018-12-18 | 华南理工大学广州学院 | A kind of electric car cluster demand response potential evaluation method |
| CN109559035A (en) * | 2018-11-29 | 2019-04-02 | 国网(苏州)城市能源研究院有限责任公司 | A kind of urban power distribution network bi-level programming method considering flexibility |
| CN111934335A (en) * | 2020-08-18 | 2020-11-13 | 华北电力大学 | Cluster electric vehicle charging behavior optimization method based on deep reinforcement learning |
| CN112332433A (en) * | 2020-09-29 | 2021-02-05 | 国网电动汽车服务有限公司 | Transferable load capacity analysis method for electric vehicle participated in valley filling auxiliary service |
| CN112467722A (en) * | 2020-09-30 | 2021-03-09 | 国网福建省电力有限公司 | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station |
| DE102020203199A1 (en) * | 2020-03-12 | 2021-04-15 | Vitesco Technologies GmbH | Method and system for charging and load sharing |
| CN113011104A (en) * | 2021-04-02 | 2021-06-22 | 华北电力大学（保定） | Cluster electric vehicle charging load aggregation modeling method facing power grid frequency modulation control |
| CN113199946A (en) * | 2020-12-11 | 2021-08-03 | 广东电网有限责任公司电力调度控制中心 | Electric automobile energy storage polymerization modeling method based on Markov process |
| CN113315157A (en) * | 2021-06-03 | 2021-08-27 | 国网山东省电力公司电力科学研究院 | Power distribution network cooperative control method considering participation of generalized energy storage cluster |
| CN113364019A (en) * | 2021-06-29 | 2021-09-07 | 中国电力科学研究院有限公司 | Method and system for participating in electric power peak shaving by vehicle-mounted energy storage resources |
| CN113515884A (en) * | 2021-04-19 | 2021-10-19 | 国网上海市电力公司 | Distributed electric vehicle real-time optimization scheduling method, system, terminal and medium |
| CN113541168A (en) * | 2021-05-31 | 2021-10-22 | 国网江苏省电力有限公司电力科学研究院 | Electric vehicle cluster controllability determining method, scheduling method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8937456B2 (en) * | 2011-04-21 | 2015-01-20 | International Business Machines Corporation | Real time system and method for optimizing and managing a load in an electrical grid |
-
2021
- 2021-12-30 CN CN202111661620.6A patent/CN114398723B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104022513A (en) * | 2014-05-30 | 2014-09-03 | 上海电力学院 | Multistage voltage control partitioning method for grid connection of electric automobile charge, discharge and storage integrated station |
| CN109034648A (en) * | 2018-08-13 | 2018-12-18 | 华南理工大学广州学院 | A kind of electric car cluster demand response potential evaluation method |
| CN109559035A (en) * | 2018-11-29 | 2019-04-02 | 国网(苏州)城市能源研究院有限责任公司 | A kind of urban power distribution network bi-level programming method considering flexibility |
| DE102020203199A1 (en) * | 2020-03-12 | 2021-04-15 | Vitesco Technologies GmbH | Method and system for charging and load sharing |
| CN111934335A (en) * | 2020-08-18 | 2020-11-13 | 华北电力大学 | Cluster electric vehicle charging behavior optimization method based on deep reinforcement learning |
| CN112332433A (en) * | 2020-09-29 | 2021-02-05 | 国网电动汽车服务有限公司 | Transferable load capacity analysis method for electric vehicle participated in valley filling auxiliary service |
| CN112467722A (en) * | 2020-09-30 | 2021-03-09 | 国网福建省电力有限公司 | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station |
| CN113199946A (en) * | 2020-12-11 | 2021-08-03 | 广东电网有限责任公司电力调度控制中心 | Electric automobile energy storage polymerization modeling method based on Markov process |
| CN113011104A (en) * | 2021-04-02 | 2021-06-22 | 华北电力大学（保定） | Cluster electric vehicle charging load aggregation modeling method facing power grid frequency modulation control |
| CN113515884A (en) * | 2021-04-19 | 2021-10-19 | 国网上海市电力公司 | Distributed electric vehicle real-time optimization scheduling method, system, terminal and medium |
| CN113541168A (en) * | 2021-05-31 | 2021-10-22 | 国网江苏省电力有限公司电力科学研究院 | Electric vehicle cluster controllability determining method, scheduling method and system |
| CN113315157A (en) * | 2021-06-03 | 2021-08-27 | 国网山东省电力公司电力科学研究院 | Power distribution network cooperative control method considering participation of generalized energy storage cluster |
| CN113364019A (en) * | 2021-06-29 | 2021-09-07 | 中国电力科学研究院有限公司 | Method and system for participating in electric power peak shaving by vehicle-mounted energy storage resources |
Non-Patent Citations (2)
| Title |
|---|
| 基于动态实时电价的电动汽车集群分层优化调度;徐康仪;电力学报;全文 * |
| 计及电动汽车和光伏―储能的微网能量优化管理;苏粟等;电力***自动化;全文 * |
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