CN117220281A - Electric automobile access power grid adjustment capability quantitative evaluation method and system - Google Patents
Electric automobile access power grid adjustment capability quantitative evaluation method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for quantitatively evaluating the electric vehicle access power grid regulating capability, which are used for acquiring load data of a power grid side, electric vehicle parameters and electric vehicle load characteristic parameters of a plurality of electric vehicles to be scheduled, constructing electric vehicle response power grid regulating capability quantitative evaluation indexes according to the load data of the power grid side and the electric vehicle parameters, dividing a plurality of electric vehicles to be scheduled into groups to obtain a plurality of electric vehicle types, determining comprehensive weights of the electric vehicle response power grid regulating capability quantitative evaluation indexes of the electric vehicle types by utilizing an entropy weight method, evaluating according to the comprehensive weights of the electric vehicle response power grid regulating capability quantitative evaluation indexes and a preset evaluation method to obtain evaluation results, making a scheduling strategy according to the evaluation results by power grid personnel, and improving the electric vehicle response power grid regulating capability quantitative evaluation accuracy by considering the response potential of the electric vehicles.
Description
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a method and a system for quantitatively evaluating the adjustment capability of an electric automobile access power grid.
Background
With the large-scale access of electric vehicle (Electr ic Vehic le, EV) loads to the power grid, planning and operation of the power distribution network meet new challenges. The method has important practical significance in researching ordered control and unified scheduling of electric vehicle load charge and discharge so as to realize peak clipping and valley filling of a large-scale electric vehicle auxiliary power distribution network.
With the development and application of smart grid construction, demand response, energy efficiency management and other technologies, interruptible/adjustable user side multi-element loads such as commercial central air conditioners, electric vehicles (Electr ic Vehic le, EV), distributed energy storage and the like rapidly increase and form new schedulable resources. Among them, EV load is widely regarded as exhibiting great advantages in the fields of energy saving, emission reduction, demand response, and the like due to its excellent environmental protection, load adjustability, and the like. Real-time control of EV load is one of the important ways to solve the problem of serious overload of the power distribution network caused by large-scale EV access. On the premise of meeting the power grid demand, how to reduce the scheduling times of EV load, improve the scheduling efficiency, reduce the scheduling cost and have important significance for developing the development planning of the electric automobile in a further step.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the invention provides a method and a system for quantitatively evaluating the adjustment capability of an electric automobile access power grid, which are used for quantitatively evaluating the adjustment capability of the electric automobile access power grid by considering the response potential of the electric automobile, so that the accuracy of quantitatively evaluating the adjustment capability of the electric automobile access power grid is improved.
A first aspect of an embodiment of the present invention provides a method for quantitatively evaluating an adjustment capability of an electric automobile to access a power grid, where the method includes:
acquiring load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters;
constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to load data of a power grid side and electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index system comprises subjective response potential indexes and objective response potential indexes;
dividing a plurality of electric vehicles to be scheduled into groups according to electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, and determining comprehensive weights of electric vehicle response power grid adjustment capability quantitative evaluation indexes of each electric vehicle class by utilizing an entropy weight method;
And evaluating according to the comprehensive weight of the electric vehicle response power grid regulation capacity quantitative evaluation index and a preset evaluation method to obtain an evaluation result, so that power grid personnel can formulate a scheduling strategy according to the evaluation result.
According to the embodiment, load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters are obtained, an electric vehicle response power grid adjustment capability quantitative evaluation index system is constructed according to the load data of the power grid side and the electric vehicle parameters, the electric vehicles to be scheduled are grouped according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, the comprehensive weight of electric vehicle response power grid adjustment capability quantitative evaluation indexes of each electric vehicle class is determined by utilizing an entropy weight method, evaluation is carried out according to the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation indexes and a preset evaluation method, an evaluation result is obtained, so that power grid personnel formulate a scheduling strategy according to the evaluation result, the electric vehicle access power grid adjustment capability quantitative evaluation is carried out by taking the response potential of the electric vehicle into consideration, and the electric vehicle response power grid adjustment capability quantitative evaluation accuracy is improved.
In one possible implementation manner of the first aspect, an electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to load data and electric vehicle parameters at a power grid side, where the electric vehicle response power grid adjustment capability quantitative evaluation index includes a subjective response potential index and an objective response potential index, and specifically includes:
establishing objective response potential indexes according to load data of a power grid side and electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (g) The probability distribution function of the voltage amplitude of the node i at the moment t is obtained; u (U) max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively,is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
The energy loss rate after failure is:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total stop of the mth charging user in the distribution network in the statistical timeThe electric time, M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of part of equipment is as follows:
n EOT ＝∑P m
wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
the service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge and discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
in the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
in the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm The temperature of the liquid crystal is not changed,g cm the maximum charge response rate is for the user.
In one possible implementation manner of the first aspect, the grouping of the plurality of electric vehicles to be scheduled according to the electric vehicle load characteristic parameter, to obtain a plurality of electric vehicle classes, specifically:
obtaining a plurality of behavior sequences of each electric automobile to be scheduled according to the electric automobile load characteristic parameters, and randomly selecting a plurality of clustering centers, wherein the behavior sequences comprise a plurality of evaluation indexes;
the Euclidean distance from each behavior sequence of the electric automobile to each clustering center is calculated, the Euclidean distance of each behavior sequence is obtained, the behavior sequences with the Euclidean distance smaller than a preset value are divided into corresponding clustering centers, and a plurality of classes are obtained, wherein the Euclidean distance calculation mode is as follows:
wherein W is i Represents the charging behavior sequence of the ith electric automobile, C t Represents the t-th cluster center, W ij The j index value C representing the i electric automobile ij A j-th index value representing an i-th cluster center;
calculating the average distance between each behavior sequence and other behavior sequences in the same class to obtain a first average distance, calculating the average distance between each behavior sequence and the behavior sequences in other classes to obtain a second average distance, and obtaining contour coefficients of classes corresponding to each behavior sequence according to the first average distance and the second average distance, wherein the calculation formula of the contour coefficients is as follows:
Wherein a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient;
calculating the contour coefficient mean value according to the contour coefficient of the class corresponding to each behavior sequence, determining the classification group number according to the Li Lunkuo coefficient mean value, and carrying out binary mean value clustering division on a plurality of electric vehicles to be scheduled according to the classification group number to obtain a plurality of electric vehicle classes, wherein the calculation formula of the contour coefficient mean value is as follows:
where a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient.
In this embodiment, a plurality of behavior sequences of each electric automobile to be scheduled are obtained according to electric automobile load characteristic parameters, a plurality of clustering centers are randomly selected, the Euclidean distance from each behavior sequence of the electric automobile to each clustering center is calculated, the Euclidean distance from each behavior sequence is obtained, the Euclidean distance is smaller than the preset value, the behavior sequences of each behavior sequence are divided into corresponding clustering centers, a plurality of classes are obtained, the average distance between each behavior sequence and other behavior sequences in the same class is calculated, a first average distance is obtained, the average distance between each behavior sequence and the behavior sequences in other classes is calculated, a second average distance is obtained, the contour coefficient of the class corresponding to each behavior sequence is obtained according to the first average distance and the second average distance, the contour coefficient average value is calculated according to the contour coefficient of the class corresponding to each behavior sequence, the Li Lunkuo coefficient average value determines the classification group number, the electric automobiles to be scheduled are divided into a plurality of electric automobiles with the same charging behaviors according to the classification group number, the electric automobiles with the same charging behaviors are obtained by using a clustering algorithm, the evaluation of the electric automobiles with the same behavior are converted into the vehicles with the same behaviors, the electric vehicles with the same traveling behaviors are evaluated, the evaluation rule is comprehensively evaluated, and the traveling rule evaluation is carried out, and the traveling rule evaluation is reduced.
In one possible implementation manner of the first aspect, the method for determining the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index of each electric vehicle class by using the entropy weight method specifically includes:
performing data standardization on the evaluation indexes in each behavior sequence to obtain a judgment index matrix, and performing standardization processing on the judgment index matrix to obtain a standard judgment index matrix;
calculating information entropy of each electric automobile by using a standard judgment index matrix, and calculating a first evaluation index weight of each electric automobile according to the information entropy;
calculating a second evaluation index weight of each electric automobile by using a standard deviation method;
calculating a third evaluation index weight of each electric automobile by using a CRITIC weight method;
and obtaining the comprehensive weight of the electric vehicle response power grid regulation capacity quantification evaluation index according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight.
In a possible implementation manner of the first aspect, the second evaluation index weight of each electric automobile is calculated by using a standard deviation method, which specifically is:
calculating the mean value and standard deviation of each evaluation index in the standard evaluation index matrix, and obtaining a second evaluation index weight according to the mean value and the standard deviation, wherein the second evaluation index weight is as follows:
Wherein omega is 2j Represents the second evaluation index weight, S j Represents the standard deviation of the j-th index.
In a possible implementation manner of the first aspect, calculating a third evaluation index weight of each electric automobile by using a CRITIC weight method specifically includes:
and calculating the pearson correlation coefficient of each evaluation index in the standard evaluation index matrix, wherein the calculation formula of the pearson correlation coefficient is as follows:
in the formula, cov (y) i ,y j ) Representing covariance of the ith and jth evaluation indexes，And->Representing the variance of the ith index vector and the variance of the jth index vector, respectively, P ij The variance of the ith index vector and the pearson correlation coefficient of the jth index vector are used;
calculating according to the pearson correlation coefficient of each evaluation index to obtain the information quantity of each index, and obtaining a third evaluation index weight by using the information quantity of each index, wherein the calculation formula of the third evaluation index weight is as follows:
wherein C is j For the information amount size indicated by the j-th index,ω 3j representing the evaluation index weight in the third behavior sequence, S j Represents the standard deviation of the j-th index.
In one possible implementation manner of the first aspect, the comprehensive weight of the electric vehicle response power grid adjustment capability quantization evaluation index is obtained according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, and specifically is:
Obtaining comprehensive weights of the electric vehicle response power grid regulation capacity quantification evaluation indexes according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, wherein the calculation formula of the comprehensive weights is as follows:
wherein omega is 1j For the first evaluation index weight, namely the real-time adjustment of the response power grid of the electric automobileControl capability evaluation index entropy weight value omega 2j The second evaluation index weight is the standard deviation method weight value, omega of the real-time regulation and control capability evaluation index of the electric automobile response power grid 3j And (3) evaluating the CRITIC weight value of the index for the third evaluation index weight, namely the real-time regulation and control capability of the electric automobile in response to the power grid.
In one possible implementation manner of the first aspect, the evaluation is performed according to a comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index and a preset evaluation method, so as to obtain an evaluation result, which specifically is:
converting each evaluation index in the standard evaluation index matrix into a very large index, obtaining a unified index matrix, and carrying out standardization processing on the unified index matrix to obtain a standardized matrix;
determining the maximum value and the minimum value of each evaluation index in the standardized matrix, wherein the maximum value is:
Z + ＝(Z 1 + ,Z 2 + ,L,Z m + )
＝(max(z 11 ,z 21 ,L,z n1 ),max(z 12 ,z 22 ,L,z n2 ),L,max(z 1m ,z 2m ,L,z nm )
Wherein Z is + Is the maximum value in the standardized matrix;
the minimum value is:
Z - ＝(Z 1 - ,Z 2 - ,L,Z m - )
＝(min(z 11 ,z 21 ,L,z n1 ),min(z 12 ,z 22 ,L,z n2 ),L,min(z 1m ,z 2m ,L,z nm )
wherein Z is - Is the minimum value in the standardized matrix;
and calculating by using the maximum value, the minimum value and the comprehensive weight to obtain an optimal scheme and a worst scheme, wherein the calculation formulas of the optimal scheme and the worst scheme are as follows:
wherein D is i + Represent the optimal method, D i - Represents the worst scheme lambda j The weight of the j-th evaluation index;
and calculating according to the optimal scheme and the worst scheme to obtain the evaluation results of each electric automobile, wherein the calculation formula of the evaluation results is as follows:
wherein D is i + Is the optimal scheme, namely the closeness degree of the ith target and the optimal target, D i - For the worst scheme, i.e. the closeness of the ith target to the worst target, C i Representing the evaluation result, C i The larger the value, the better the electric car.
A second aspect of the embodiment of the present invention provides a system for quantitatively evaluating the adjustment capability of an electric automobile to an electric network, where the system includes:
the electric vehicle dispatching system comprises an acquisition module, a dispatching module and a dispatching module, wherein the acquisition module is used for acquiring load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be dispatched and electric vehicle load characteristic parameters;
the construction module is used for constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to load data of a power grid side and electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index system comprises subjective response potential indexes and objective response potential indexes;
The comprehensive weight calculation module is used for grouping a plurality of electric vehicles to be scheduled according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, and determining the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index of each electric vehicle class by using an entropy weight method;
the evaluation result calculation module is used for evaluating according to the comprehensive weight of the electric vehicle response power grid regulation capacity quantitative evaluation index and a preset evaluation method to obtain an evaluation result, so that power grid personnel can formulate a scheduling strategy according to the evaluation result.
In one possible implementation manner of the second aspect, an electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to load data on a power grid side and electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index includes a subjective response potential index and an objective response potential index, and specifically includes:
establishing objective response potential indexes according to load data of a power grid side and electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
Wherein F is N,j,t (g) The probability distribution function of the voltage amplitude of the node i at the moment t is obtained; u (U) max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively,is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
the energy loss rate after failure is:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, wherein M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of part of equipment is as follows:
n EOT ＝∑P m
Wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
the service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge and discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
In the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
in the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm Unchanged g cm The maximum charge response rate is for the user.
Drawings
Fig. 1: the flow diagram of one embodiment of the electric automobile access power grid adjustment capability quantitative evaluation method is provided by the invention;
fig. 2: the cluster electric vehicle demand response power grid adjustment capability quantitative evaluation index system structure schematic diagram of one embodiment of the electric vehicle access power grid adjustment capability quantitative evaluation method is provided by the invention;
fig. 3: the cluster electric vehicle demand response power grid adjustment capability quantitative evaluation index system structure schematic diagram of one embodiment of the electric vehicle access power grid adjustment capability quantitative evaluation method is provided by the invention;
Fig. 4: the subjective response rate model schematic diagram of the EV user excited electricity price of one embodiment of the electric automobile access power grid regulation capacity quantitative evaluation method is provided by the invention;
fig. 5: according to the method for quantitatively evaluating the adjustment capability of the electric automobile access power grid, which is provided by the invention, a combined weight flow diagram of the electric automobile response power grid adjustment capability quantitative evaluation index is calculated based on an entropy weight-CRITIC-standard deviation method weighting model;
fig. 6: according to the TOPSIS-based calculation electric vehicle response power grid adjustment capability quantitative evaluation index scoring flow diagram of one embodiment of the electric vehicle access power grid adjustment capability quantitative evaluation method;
fig. 7: the system structure schematic diagram of another embodiment of the electric automobile access power grid adjustment capability quantitative evaluation method is provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a method for quantitatively evaluating the adjustment capability of an electric vehicle to an electric network according to an embodiment of the present invention includes steps S11 to S14, which are specifically as follows:
s11, acquiring load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters.
In this embodiment, load data on a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled, and electric vehicle load characteristic parameters are obtained, and it is to be noted that the electric vehicle parameters at least include data such as electric vehicle power, battery usage conditions, electric vehicle charging and discharging amounts, and the electric vehicle load characteristic parameters at least include network access time, network departure time, and initial remaining power of the network access.
S12, constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to load data of a power grid side and electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index system comprises subjective response potential indexes and objective response potential indexes.
As a preferred embodiment, an electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to load data and electric vehicle parameters at a power grid side, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index comprises a subjective response potential index and an objective response potential index, and specifically comprises:
Establishing objective response potential indexes according to load data of a power grid side and electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (g) The probability distribution function of the voltage amplitude of the node i at the moment t is obtained; u (U) max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
/>
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively,is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
the energy loss rate after failure is:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system,γ FL,i for the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, wherein M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of part of equipment is as follows:
n EOT ＝∑P m
wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
the service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge and discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
Establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
in the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
in the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm Unchanged g cm The maximum charge response rate is for the user.
In this embodiment, as shown in fig. 2, from both the power grid side and the user side, an electric vehicle response power grid regulation capability quantitative evaluation index system is proposed according to an EV participation power grid active control framework, and the evaluation index system is shown in fig. 3, and includes a subjective response potential and an objective response potential, and calculates the subjective response potential and the objective response potential respectively.
The EV objective response potential is the sum of the charge response potential and the discharge response potential of the user i at the time t, and the calculation mode is as follows:
in the method, in the process of the invention,charge response potential for user i at time t, < >>For the discharge response potential of user i at time t, P re (i, t) is the objective response potential. />
The subjective charge-discharge response potential of the EV user is calculated by the subjective charge response rate and the subjective discharge response rate of the user, and the result is as follows:
in the method, in the process of the invention,subjective charge response rate for user, < >>G (e) is the subjective charge-discharge response potential for subjective discharge response rate.
The objective response potential of the electric automobile refers to the maximum response power which can be achieved in the charging process and is respectively limited by factors such as safety constraint on the power grid side, safety constraint on the battery and the like. The safety constraint of the power grid side is mainly the safety and rationality of the power grid operation. The battery safety constraint mainly considers the relation between the initial electric quantity and the rated charge-discharge power during charge-discharge.
The subjective response level of the EV is mainly affected by the charge (discharge) demand caused by the price of the exciting electricity and the current remaining amount of the user, and for the user's wish at the same excitation level, both cases of positive response and negative response may be set, and the average value of the response rates of these two cases is taken as the overall response wish of the user, as shown in fig. 6.
The EV objective response potential constraints comprise grid-side safety constraints and battery safety constraints, wherein the grid-side safety constraints comprise node voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability rate and partial equipment outage times.
Node voltage yield R N,i,t The node voltage qualification rate is used for describing probability distribution of voltage amplitude of a certain node of the power distribution network in a certain moment, taking a node i as an example, and the node voltage qualification rate at the moment t is calculated as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (. Cndot.) is the probability distribution function of the voltage amplitude of node i at time t, U max For the upper limit of the qualified voltage, U min Is the lower limit of the pass voltage.
Average voltage deviation valueThe average deviation value of the node voltage represents the difference value between the node voltage and the line root node voltage, wherein the difference value comprises the lowest voltage and the highest voltage.
In the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively,is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref Is the line root node voltage amplitude.
Failure energy loss rate C FE And reflecting the energy loss condition of the whole power distribution network after the fault for the index.
C FE ＝p FE T FL
Wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL Is the time for fault repair.
Electricity consumption reliability R RSL The method comprises the following steps:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, M represents the total number of charging users in the distribution network, and T represents the statistical time.
Number of partial plant outages n EOT In order to count the times that the system is not powered off but part of user equipment is stopped or not available due to the power quality problem in the time.
n EOT ＝∑P m
Wherein P is m Representing the number of times that the mth billing subscriber in the distribution network has equipment out of service or unavailable due to the system blackout caused by the power quality problem in the statistical time.
The battery safety mainly considers factors such as battery life safety constraint, charge-discharge electric quantity constraint, charging pile power constraint and the like.
During the EV participation in the DR, the electric power of the EV changes due to the influence of external temperature, humidity, running environment, and the like. Therefore, to ensure the service life of the EV battery, the safe charge limit of the battery must be satisfied, that is:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when user i charges (discharges) at time t in response to power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 Is EV battery capacity.
Because the residual electric quantity of the EV network access has randomness, the EV can autonomously select to perform charge and discharge response in the DR participation process, if the charge response is selected, the EV electric quantity needs to reach the initial expected electric quantity before leaving a station on the premise of meeting travel requirements, if the discharge response is selected, the battery electric quantity requirement is also required to be met, and the service condition of the EV is also influenced by the excessively low electric quantity. The constraint of the SOC variation caused by S (i, t) in the DR process is as follows:
charging response:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
discharge response:
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) The desired charge level for user i.
Since the rated power of the charging pile has a great influence on the response potential of the EV, a constraint of associating the EV electric quantity with the charging (discharging) can be considered: when the EV arrival remaining power is higher, the smaller the initial charge power is given, the larger the initial discharge power is; when the EV arrival remaining amount is lower, the larger the initial charge power is given, and the smaller the initial discharge power is. And as EV charge increases, charging power decreases. When the EV electric quantity decreases, the discharge power decreases. The specific constraints are as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge (discharge) power of user i at time t, P m (i,t)、P M (i, t) are the minimum charge (discharge) power and the maximum charge (discharge) power of the user i at time t, respectively.
The objective response potential of user i at time t is calculated as P re (i,t)：
P re (i,t)＝P na (i,t)-P m (i,t)
Wherein P is na (i, t) is the natural charging power when not participating in the DR process, and the calculation formula is as follows:
users with potential for discharge response, i.e. P m The response potential when (i, t) < 0 is calculated as follows:
P c re (i,t)＝P na (i,t)
in the method, in the process of the invention,the charge and discharge response potential of the user i at the time t is respectively the sum of the charge and discharge response potential and the charge and discharge response potential is the total objective response potential P re (i,t)。
For EV users, whether to participate in DR depends on their subjective intent, and therefore, the magnitude of EV response potential depends on both external objective competence and subjective intent of the user. The subjective response level of the EV is mainly affected by the charge (discharge) demand caused by the price of the exciting electricity and the current remaining amount of the user, and the price has the lowest perceivable difference threshold for the user. I.e. when the price of excitation power is within this threshold range, the user is insensitive to the price of excitation power, has substantially no response or has very little response. When this threshold range is exceeded, the user starts to respond, and as the price of electricity for the incentive increases, the degree of user response increases gradually. The user response degree also has a saturation value, and when the excitation electricity price reaches a certain degree, the user response degree is maximally not improved, namely the response saturation region. For user willingness at the same motivation level, both positive and negative response scenarios may be set, with the average of the response rates of both scenarios as the overall willingness of the user to respond.
And->Charging positive response and charging negative response curves for the user, respectively:
/>
wherein e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, g c And g d Respectively the charge and discharge response rate, g cm And g dm Respectively the maximum charge and discharge response rates of users, e c1 Charging the user with an excitation response threshold above which the excitation electricity price is higherWhen the charging response of the user is always greater than 0, e d1 Discharging the minimum excitation value, e d2 Discharging the excitation response critical value for the user, and when the excitation electricity price is higher than the excitation response critical value, the charging response of the user is always larger than 0, e cm Saturation excitation electricity price for charging of user, charging response of user maintains g when excitation electricity price reaches this value cm Unchanged, e dm For the user's discharge saturated excitation electricity price, when the excitation electricity price reaches this value, the user's discharge response maintains g dm Is unchanged. The overall charge response rate of the user is calculated as follows:
in the method, in the process of the invention,and->The positive discharge response and the negative discharge response curves of the user are respectively shown as follows:
the overall discharge response rate of the user is calculated as follows:
and calculating the subjective charge-discharge response potential of the EV user by combining the subjective charge response rate and the subjective discharge response rate of the user, wherein the calculation result is as follows:
In the method, in the process of the invention,subjective charge response rate for user, < >>G (e) is the subjective charge-discharge response potential for subjective discharge response rate.
S13, grouping the electric vehicles to be scheduled according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, and determining comprehensive weights of electric vehicle response power grid adjustment capability quantitative evaluation indexes of the electric vehicles of the electric vehicle classes by using an entropy weight method.
In a preferred embodiment, a plurality of electric vehicles to be scheduled are grouped according to electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, which are specifically as follows:
obtaining a plurality of behavior sequences of each electric automobile to be scheduled according to the electric automobile load characteristic parameters, and randomly selecting a plurality of clustering centers, wherein the behavior sequences comprise a plurality of evaluation indexes;
the Euclidean distance from each behavior sequence of the electric automobile to each clustering center is calculated, the Euclidean distance of each behavior sequence is obtained, the behavior sequences with the Euclidean distance smaller than a preset value are divided into corresponding clustering centers, and a plurality of classes are obtained, wherein the Euclidean distance calculation mode is as follows:
wherein W is i Represents the charging behavior sequence of the ith electric automobile, C t Represents the t-th cluster center, W ij The j index value C representing the i electric automobile ij A j-th index value representing an i-th cluster center;
calculating the average distance between each behavior sequence and other behavior sequences in the same class to obtain a first average distance, calculating the average distance between each behavior sequence and the behavior sequences in other classes to obtain a second average distance, and obtaining contour coefficients of classes corresponding to each behavior sequence according to the first average distance and the second average distance, wherein the calculation formula of the contour coefficients is as follows:
wherein a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient;
calculating the contour coefficient mean value according to the contour coefficient of the class corresponding to each behavior sequence, determining the classification group number according to the Li Lunkuo coefficient mean value, and carrying out binary mean value clustering division on a plurality of electric vehicles to be scheduled according to the classification group number to obtain a plurality of electric vehicle classes, wherein the calculation formula of the contour coefficient mean value is as follows:
where a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient.
In a preferred embodiment, an entropy weight method is used to determine the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index of each electric vehicle, specifically:
Performing data standardization on the evaluation indexes in each behavior sequence to obtain a judgment index matrix, and performing standardization processing on the judgment index matrix to obtain a standard judgment index matrix;
calculating information entropy of each electric automobile by using a standard judgment index matrix, and calculating a first evaluation index weight of each electric automobile according to the information entropy;
calculating a second evaluation index weight of each electric automobile by using a standard deviation method;
calculating a third evaluation index weight of each electric automobile by using a CRITIC weight method;
and obtaining the comprehensive weight of the electric vehicle response power grid regulation capacity quantification evaluation index according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight.
In a preferred embodiment, the second evaluation index weight of each electric automobile is calculated by using a standard deviation method, specifically:
calculating the mean value and standard deviation of each evaluation index in the standard evaluation index matrix, and obtaining a second evaluation index weight according to the mean value and the standard deviation, wherein the second evaluation index weight is as follows:
wherein omega is 2j Represents the second evaluation index weight, S j Represents the standard deviation of the j-th index.
In a preferred embodiment, the CRITIC weighting method is used to calculate a third evaluation index weight of each electric automobile, specifically:
And calculating the pearson correlation coefficient of each evaluation index in the standardized matrix, wherein the calculation formula of the pearson correlation coefficient is as follows:
in the formula, cov (y) i ,y j ) Representing the covariance of the ith and jth evaluation indexes,and->Representing the variance of the ith index vector and the variance of the jth index vector, respectively, P ij The variance of the ith index vector and the pearson correlation coefficient of the jth index vector are used;
calculating according to the pearson correlation coefficient of each evaluation index to obtain the information quantity of each index, and obtaining a third evaluation index weight by using the information quantity of each index, wherein the calculation formula of the third evaluation index weight is as follows:
wherein C is j For the information amount size indicated by the j-th index,ω 3j representing the evaluation index weight in the third behavior sequence, S j Represents the standard deviation of the j-th index.
In a preferred embodiment, the comprehensive weight of the electric vehicle response power grid regulation capability quantization evaluation index is obtained according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, specifically:
obtaining comprehensive weights of the electric vehicle response power grid regulation capacity quantification evaluation indexes according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, wherein the calculation formula of the comprehensive weights is as follows:
Wherein omega is 1j The first evaluation index weight is the entropy weight value of the evaluation index of the real-time regulation and control capability of the electric automobile response power grid, omega 2j The second evaluation index weight is the standard deviation method weight value, omega of the real-time regulation and control capability evaluation index of the electric automobile response power grid 3j And (3) evaluating the CRITIC weight value of the index for the third evaluation index weight, namely the real-time regulation and control capability of the electric automobile in response to the power grid.
In the embodiment, based on a k-means clustering algorithm and a contour coefficient method, the scattered electric vehicles are divided into multiple groups according to the distribution characteristics of EV load characteristic parameters, and the specific steps are as follows:
based on the similarity of the EV on the flow behavior, a k-means clustering algorithm is used for dividing the clusters from the network access time, the network departure time and the initial residual power of the arrival of the EV. And (3) converting the evaluation of the single electric automobile into the evaluation of the vehicles with the same travel behaviors, and performing comprehensive schedulability evaluation on the EV travel rules.
The k-means clustering algorithm is a selection that divides all elements into k clusters by calculating the "distance" of each element to the center of a randomly selected cluster, the result of which depends on the accuracy of the data and the number of clusters. The specific principle is as follows:
Given electric automobile sample w= [ W ] 1 ,W 2 ,…,W i ,…W n ],W i Is a sequence of three dimensions including the network entry time, the network exit time and the initial remaining power of the station entry of the EV. Initializing cluster centers { C (C) of j electric automobile groups 1 ,C 2 ,L,C t Then by calculating the Euclidean distance of each sequence to each cluster center, the calculation is as follows:
wherein W is i Represents the charging behavior sequence of the ith electric automobile, C t Represents the t-th cluster center, W ij The j index value C representing the i electric automobile ij The j-th index value representing the i-th cluster center.
In order to quickly find the optimal classification group number and obtain a better clustering effect, a contour coefficient method is introduced to determine a proper k value on the basis of using a k-means clustering algorithm. The profile coefficient method is to determine the most suitable number of classification groups by comparing the similarity between classification groups and the difference between individuals, and the number of classification groups can be evaluated more reasonably by calculating the mean value of the profile coefficient of each sample. The specific definition is as follows:
where a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient.
The average distance a (i) of sample i to other samples in the same class is calculated. The smaller a (i) the closer, i.e. more similar, the sample i is to the other samples in the same class. The average distance b (i) of the sample i to all samples of the other classes is calculated, called the dissimilarity between the sample i and the other classes. The larger b (i) indicates that the farther, i.e., the less similar, the sample i is from the other classes.
The value of s (i) is between [ -1,1], the closer to 1 the larger b (i) the smaller a (i) is, the more similar the interior of the class is, and the more dissimilar the classes are; the closer to 0 illustrates the more or less distance between the interior of the category and the category, the less obvious the demarcation line, and the sample can be assigned to either category; closer to-1 indicates that the more similar between categories the more similar the interior of the category is, but the more dissimilar.
Based on the entropy weight-CRITIC-standard deviation method weighting model, the quantitative evaluation index weight of the electric automobile response power grid regulation capability is calculated respectively, and as shown in fig. 4, the method specifically comprises the following steps:
step one, data standardization: for the forward index, the larger the index value, the better. For the negative index, the smaller the index value is, the better the index value is, and the specific processing procedures are as follows:
in which W is ij An evaluation index of the j-th EV response DR real-time regulatory ability, i=1, l, n, j=1, l, m, max (W j )，min(W j ) Respectively obtaining a maximum value and a minimum value of different objects under the same evaluation index, obtaining a judgment index matrix Y after dimensionless treatment,indicating a forward index>Indicating a negative going indicator.
And step two, the entropy weight method distributes weights according to the information quantity transmitted to a decision maker by each index, and is an objective weight giving method. When an evaluation index plays a smaller role in the system, the entropy weight of the information is smaller, and compared with other indexes, the influence on decision making is smaller.
(1) And (3) carrying out standardization processing on the judgment index matrix Y: p exists for the traditional entropy weight method ij =0, resulting in lnp ij In a nonsensical case, the scheme adopts the following formula to carry out standardization treatment:
wherein y is ij Represents an evaluation index, p ij Represents the normalized evaluation index.
(2) Information entropy E of each EV response DR real-time regulation capability evaluation index is respectively obtained t ：
Wherein p is ij Represents the standardized evaluation index, E j Representing the entropy of the information.
(3) Calculating the information entropy weight omega of each EV response DR real-time regulation and control capability evaluation index 1j Obtaining the objective weight omega= [ omega ] of the evaluation index 11 ω 12 ··· ω 1m ]：
Wherein omega is 1j And evaluating the objective weight of the index.
The standard deviation method is used in statistics in a relatively large number, and the principle is that if the standard deviation calculated by a certain index is larger, the degree of variation of the index is larger, the amount of information which can be provided is larger, the effect in evaluation is larger, and the weight of the index is also larger, and vice versa.
First calculate the mean of the j-th indexThen calculate the standard deviation of the j-th indexThe weight omega of the j-th index thereof 2j Is->
The CRITIC weighting method is an evaluation index objective weighting method proposed by Diakoulaki. When the method is used for determining the weight, not only the information content of the indexes is considered, but also the contrast between different schemes and the conflict among the indexes are introduced, and the calculation result is more objective, reasonable and accurate.
(1) First, the calculated evaluation index and pearson correlation coefficient normalization matrix are used to calculate the evaluation index matrix correlation coefficient.
In the formula, cov (y) i ,y j ) Representing the covariance of the ith index and the jth index,and->Representing the variance of the respective index vectors, respectively. P (P) ij Is the pearson correlation coefficient of the two vectors.
(2) The volatility and conflict of the jth index with other indexes can be calculated and expressed by using standard deviation. Then:
wherein C is j The information amount size indicated by the j-th index.
(3) Finally, calculate CRITIC weight ω of the j-th index 3j :
The comprehensive weight calculating method comprises the following steps: the entropy weighting method belongs to the first class of weighting methods, while the CRITIC weighting method and the standard deviation method belong to the second class of methods. And calculating the EV response DR real-time regulation capability evaluation index comprehensive weight lambda by combining an entropy weight method and a CRITIC weight method and a standard deviation method, wherein the comprehensive weight lambda is shown in the following formula:
wherein omega is 1j Evaluating index entropy weight value omega for EV response DR real-time regulation capability 2j Index standard deviation method weight value omega for EV response DR real-time regulation capability evaluation 3j And evaluating a CRITIC weight value of an index for EV response DR real-time regulation capability.
S14, evaluating according to comprehensive weights of the electric vehicle response power grid adjustment capability quantitative evaluation indexes and a preset evaluation method to obtain an evaluation result, so that power grid personnel can formulate a scheduling strategy according to the evaluation result.
As a preferred embodiment, the electric vehicle is evaluated according to the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index and a preset evaluation method, so as to obtain an evaluation result, specifically:
converting each evaluation index in the standard evaluation index matrix into a very large index, obtaining a unified index matrix, and carrying out standardization processing on the unified index matrix to obtain a standardized matrix;
determining the maximum value and the minimum value of each evaluation index in the standardized matrix, wherein the maximum value is:
Z + ＝(Z 1 + ,Z 2 + ,L,Z m + )
＝(max(z 11 ,z 21 ,L,z n1 ),max(z 12 ,z 22 ,L,z n2 ),L,max(z 1m ,z 2m ,L,z nm )
wherein Z is + Is the maximum value in the standardized matrix;
the minimum value is:
Z - ＝(Z 1 - ,Z 2 - ,L,Z m - )
＝(min(z 11 ,z 21 ,L,z n1 ),min(z 12 ,z 22 ,L,z n2 ),L,min(z 1m ,z 2m ,L,z nm )
wherein Z is - Is the minimum value in the standardized matrix;
and calculating by using the maximum value, the minimum value and the comprehensive weight to obtain an optimal scheme and a worst scheme, wherein the calculation formulas of the optimal scheme and the worst scheme are as follows:
wherein D is i + Represent the optimal method, D i - Represents the worst scheme lambda j The weight of the j-th evaluation index;
and calculating according to the optimal scheme and the worst scheme to obtain the evaluation results of each electric automobile, wherein the calculation formula of the evaluation results is as follows:
wherein D is i + Is the optimal scheme, namely the closeness degree of the ith target and the optimal target, D i - For the worst scheme, i.e. the closeness of the ith target to the worst target, C i Representing the evaluation result, C i The larger the value, the better the electric car.
In this embodiment, comprehensive quantitative evaluation of the electric vehicle response power grid adjustment capability is performed based on a TOPSIS comprehensive evaluation method and comprehensive weights, as shown in fig. 5, and specifically includes:
TOPSIS comprehensive evaluation method: the evaluation process of the TOPSIS comprehensive evaluation method comprises the steps of firstly carrying out standardization processing on each index original data matrix, then comparing the distances between the schemes and ideal points, wherein the distances between the scheme attributes and positive and negative ideal points and the relative closeness between each scheme and the positive ideal points are needed, and sequencing the schemes to be determined according to the order of the relative closeness from large to small, so that the evaluation objects are better arranged in the front.
The first step: unified index type
The indexes are classified into a very small index (the smaller the index value is, the better) and a very large index (the larger the index value is, the better), and all the indexes are unified into the very large index first. Obtaining a judging index matrix Y according to the dimensionless treatment, and supposing an evaluation matrix Y of a multi-index evaluation problem, wherein the matrix is provided with m evaluation objects and n evaluation indexes:
and a second step of: construction of a normalization matrix
In order to prevent the influence of each index dimension on the comprehensive evaluation method, data normalization processing is performed on the index which has been converted into an extremely large size. The normalized matrix is then denoted as Z, each element in Z being:
and a third step of: index weight
The comprehensive weight lambda is obtained through calculation, and reasonable calculation and determination of the index weight are key to the application of the TOPSIS comprehensive evaluation method.
Fourth step: calculating the difference between each evaluation index and the optimal and worst vectors
First define the maximum Z + And a minimum value Z - ：
Z + ＝(Z 1 + ,Z 2 + ,L,Z m + )
＝(max(z 11 ,z 21 ,L,z n1 ),max(z 12 ,z 22 ,L,z n2 ),L,max(z 1m ,z 2m ,L,z nm )
Z - ＝(Z 1 - ,Z 2 - ,L,Z m - )
＝(min(z 11 ,z 21 ,L,z n1 ),min(z 12 ,z 22 ,L,z n2 ),L,min(z 1m ,z 2m ,L,z nm )
Then calculating the optimal scheme and the worst scheme:
in the formula, the optimal scheme D i + From Z + Maximum value of each column element in (b) and worst scheme D i - From Z - Minimum value of each column element, lambda j Is the weight of the j-th attribute.
Fifth step: calculating score of evaluation object
Wherein D is i + For the closeness degree of the ith target and the optimal target, D i - C for the closeness of the ith target and the worst target i The larger the value, the better the evaluation object.
The method comprises the steps of obtaining load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters, constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to the load data of the power grid side and the electric vehicle parameters, grouping the electric vehicles to be scheduled according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, determining comprehensive weights of electric vehicle response power grid adjustment capability quantitative evaluation indexes of the electric vehicle classes by an entropy weight method, evaluating according to the comprehensive weights of the electric vehicle response power grid adjustment capability quantitative evaluation indexes and a preset evaluation method to obtain evaluation results, so that power grid personnel can formulate a scheduling strategy according to the evaluation results, and the accuracy of electric vehicle response power grid adjustment capability quantitative evaluation is improved by considering response potential of the electric vehicles.
The more detailed working principle and the step flow of this embodiment can be, but not limited to, those described in the related embodiment one.
Example two
Accordingly, referring to fig. 7, fig. 7 is a system for quantitatively evaluating the adjustment capability of an electric automobile access power grid, provided by the invention, as shown in the figure, the system for quantitatively evaluating the adjustment capability of the electric automobile access power grid comprises:
the acquiring module 701 is configured to acquire load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled, and electric vehicle load characteristic parameters;
the construction module 702 is configured to construct an electric vehicle response power grid adjustment capability quantitative evaluation index system according to load data of a power grid side and electric vehicle parameters, where the electric vehicle response power grid adjustment capability quantitative evaluation index system includes a subjective response potential index and an objective response potential index;
the comprehensive weight calculation module 703 is configured to divide a plurality of electric vehicles to be scheduled into groups according to electric vehicle load characteristic parameters, obtain a plurality of electric vehicle classes, and determine the comprehensive weight of the electric vehicle response power grid adjustment capability quantization evaluation index of each electric vehicle class by using an entropy weight method;
the evaluation result calculation module 704 is configured to perform evaluation according to the comprehensive weight of the electric vehicle response power grid adjustment capability quantization evaluation index and a preset evaluation method, so as to obtain an evaluation result, so that a power grid personnel can formulate a scheduling strategy according to the evaluation result.
In a preferred embodiment, an electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to load data and electric vehicle parameters at a power grid side, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index comprises a subjective response potential index and an objective response potential index, and specifically comprises:
establishing objective response potential indexes according to load data of a power grid side and electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (g) Is the probability distribution function of the voltage amplitude of the node i at the moment t, U max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively,is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
The energy loss rate after failure is:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, wherein M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of part of equipment is as follows:
n EOT ＝∑P m
wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
the service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge and discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
in the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
in the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm Unchanged g cm The maximum charge response rate is for the user.
The electric automobile access power grid adjustment capability quantitative evaluation system can implement the electric automobile access power grid adjustment capability quantitative evaluation method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the above method embodiments, and in this embodiment, no further description is given.
In summary, the embodiment of the application has the following beneficial effects:
the method comprises the steps of obtaining load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters, constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to the load data of the power grid side and the electric vehicle parameters, grouping the electric vehicles to be scheduled according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, determining comprehensive weights of electric vehicle response power grid adjustment capability quantitative evaluation indexes of the electric vehicle classes by using an entropy weight method, evaluating according to the comprehensive weights of the electric vehicle response power grid adjustment capability quantitative evaluation indexes and a preset evaluation method to obtain evaluation results, making a scheduling strategy according to the evaluation results by power grid personnel, and improving the accuracy of electric vehicle response power grid adjustment capability quantitative evaluation by taking response potential of the electric vehicles into consideration.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The method for quantitatively evaluating the adjustment capability of the electric automobile to the power grid is characterized by comprising the following steps of:
acquiring load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be scheduled and electric vehicle load characteristic parameters;
constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to the load data of the power grid side and the electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index system comprises subjective response potential indexes and objective response potential indexes;
dividing the electric vehicles to be scheduled into groups according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, and determining the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index of each electric vehicle class by utilizing an entropy weight method;
And evaluating according to the comprehensive weight of the electric vehicle response power grid regulation capability quantitative evaluation index and a preset evaluation method to obtain an evaluation result, so that power grid personnel can formulate a scheduling strategy according to the evaluation result.
2. The method for quantitatively evaluating the electric vehicle access power grid adjustment capability according to claim 1, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to the load data of the power grid side and the electric vehicle parameters, and the electric vehicle response power grid adjustment capability quantitative evaluation index comprises a subjective response potential index and an objective response potential index, specifically:
establishing objective response potential indexes according to the load data of the power grid side and the electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (g) The probability distribution function of the voltage amplitude of the node i at the moment t is obtained; u (U) max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively +.>Is the average voltage deviation +.>And->The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
the energy loss rate after the fault is as follows:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, wherein M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of the partial equipment is as follows:
n EOT ＝∑P m
wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
The service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge-discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
in the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
In the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm Unchanged g cm The maximum charge response rate is for the user.
3. The method for quantitatively evaluating the adjustment capability of the electric automobile to be connected to the power grid according to claim 1, wherein the grouping of the electric automobiles to be scheduled according to the electric automobile load characteristic parameter is performed to obtain a plurality of electric automobile classes, specifically:
obtaining a plurality of behavior sequences of each electric automobile to be scheduled according to the electric automobile load characteristic parameters, and randomly selecting a plurality of clustering centers, wherein the behavior sequences comprise a plurality of evaluation indexes;
calculating Euclidean distances from each behavior sequence of the electric automobile to each clustering center to obtain Euclidean distances of each behavior sequence, and dividing the behavior sequences with Euclidean distances smaller than a preset value into corresponding clustering centers to obtain a plurality of classes, wherein the Euclidean distances are calculated in the following modes:
Wherein W is i Represents the charging behavior sequence of the ith electric automobile, C t Represents the t-th cluster center, W ij The j index value C representing the i electric automobile ij A j-th index value representing an i-th cluster center;
calculating the average distance between each behavior sequence and other behavior sequences in the same class to obtain a first average distance, calculating the average distance between each behavior sequence and the behavior sequences in other classes to obtain a second average distance, and obtaining the contour coefficient of each class corresponding to each behavior sequence according to the first average distance and the second average distance, wherein the calculation formula of the contour coefficient is as follows:
wherein a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient;
calculating a contour coefficient mean value according to the contour coefficients of the classes corresponding to the behavior sequences, determining a classification group number according to the contour coefficient mean value, and carrying out binary mean clustering division on the electric vehicles to be scheduled according to the classification group number to obtain a plurality of electric vehicle classes, wherein the calculation formula of the contour coefficient mean value is as follows:
where a (i) represents a first average distance, b (i) represents a second average distance, and s (i) represents a contour coefficient.
4. The method for quantitatively evaluating the adjustment capability of the electric automobile to the power grid according to claim 1, wherein the method for determining the comprehensive weight of the electric automobile response power grid adjustment capability quantitative evaluation index of each electric automobile class by using the entropy weight method is specifically as follows:
performing data standardization on the evaluation indexes in each behavior sequence to obtain a judgment index matrix, and performing standardization processing on the evaluation index matrix to obtain a standard judgment index matrix;
calculating information entropy of each electric automobile by using the standard evaluation index matrix, and calculating first evaluation index weights of each electric automobile according to the information entropy;
calculating a second evaluation index weight of each electric automobile by using a standard deviation method;
calculating a third evaluation index weight of each electric automobile by using a CRITIC weight method;
and obtaining the comprehensive weight of the electric vehicle response power grid regulation capacity quantification evaluation index according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight.
5. The method for quantitatively evaluating the adjustment capability of an electric automobile to an electric network according to claim 4, wherein the calculating the second evaluation index weight of each electric automobile by using a standard deviation method specifically comprises:
Calculating the mean value and standard deviation of each evaluation index in the standard evaluation index matrix, and obtaining a second evaluation index weight according to the mean value and the standard deviation, wherein the second evaluation index weight is as follows:
wherein omega is 2j Represents the second evaluation index weight, S j Represents the jthStandard deviation of the index.
6. The method for quantitatively evaluating the adjustment capability of the electric automobile to the power grid according to claim 4, wherein the calculating the third evaluation index weight of each electric automobile by using CRITIC weight method specifically comprises:
and calculating the pearson correlation coefficient of each evaluation index in the standard evaluation index matrix, wherein the calculation formula of the pearson correlation coefficient is as follows:
in the formula, cov (y) i ,y j ) Representing the covariance of the ith and jth evaluation indexes, σ yi Sum sigma yj Representing the variance of the ith index vector and the variance of the jth index vector, respectively, P ij The variance of the ith index vector and the pearson correlation coefficient of the jth index vector are used;
calculating the information quantity of each index according to the pearson correlation coefficient of each evaluation index, and obtaining a third evaluation index weight by using the information quantity of each index, wherein the calculation formula of the third evaluation index weight is as follows:
Wherein C is j For the information amount size indicated by the j-th index,ω 3j representing the evaluation index weight in the third behavior sequence, S j Represents the standard deviation of the j-th index.
7. The method for quantitatively evaluating the adjustment capability of the electric automobile to the power grid according to claim 4, wherein the comprehensive weight of the electric automobile response power grid adjustment capability quantitative evaluation index is obtained according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, specifically:
obtaining comprehensive weights of the electric vehicle response power grid regulation capacity quantitative evaluation indexes according to the first evaluation index weight, the second evaluation index weight and the third evaluation index weight, wherein a calculation formula of the comprehensive weights is as follows:
wherein omega is 1j The first evaluation index weight is the entropy weight value of the evaluation index of the real-time regulation and control capability of the electric automobile response power grid, omega 2j The second evaluation index weight is the standard deviation method weight value, omega of the real-time regulation and control capability evaluation index of the electric automobile response power grid 3j And (3) evaluating the CRITIC weight value of the index for the third evaluation index weight, namely the real-time regulation and control capability of the electric automobile in response to the power grid.
8. The method for quantitatively evaluating the adjustment capability of the electric automobile to the power grid according to claim 1, wherein the evaluation is performed according to the comprehensive weight of the electric automobile response power grid adjustment capability quantitative evaluation index and a preset evaluation method to obtain an evaluation result, specifically:
converting each evaluation index in the standard evaluation index matrix into a very large index, and performing standardization processing on the unified index matrix after obtaining the unified index matrix to obtain a standardized matrix;
determining the maximum value and the minimum value of each evaluation index in the standardized matrix, wherein the maximum value is:
Z + ＝(Z 1 + ,Z 2 + ,L,Z m + )
＝(max(z 11 ,z 21 ,L,z n1 ),max(z 12 ,z 22 ,L,z n2 ),L,max(z 1m ,z 2m ,L,z nm ) Wherein Z is + Is the maximum value in the standardized matrix;
the minimum value is as follows:
Z - ＝(Z 1 - ,Z 2 - ,L,Z m - )
＝(min(z 11 ,z 21 ,L,z n1 ),min(z 12 ,z 22 ,L,z n2 ),L,min(z 1m ,z 2m ,L,z nm )
wherein Z is - Is the minimum value in the standardized matrix;
and calculating by using the maximum value, the minimum value and the comprehensive weight to obtain an optimal scheme and a worst scheme, wherein the calculation formulas of the optimal scheme and the worst scheme are as follows:
wherein D is i + Represent the optimal method, D i - Represents the worst scheme lambda j Weights for the j-th said evaluation index;
and calculating according to the optimal scheme and the worst scheme to obtain an evaluation result of each electric automobile, wherein a calculation formula of the evaluation result is as follows:
Wherein D is i + Is the optimal scheme, namely the closeness degree of the ith target and the optimal target, D i - Is the worst scheme, i.e. the ith target and the mostThe closeness of the inferior target, C i Representing the evaluation result, C i The larger the value, the better the electric automobile.
9. An electric automobile inserts electric wire netting regulatory capability quantization evaluation system, characterized by comprising:
the electric vehicle dispatching system comprises an acquisition module, a dispatching module and a dispatching module, wherein the acquisition module is used for acquiring load data of a power grid side, electric vehicle parameters of a plurality of electric vehicles to be dispatched and electric vehicle load characteristic parameters;
the construction module is used for constructing an electric vehicle response power grid adjustment capability quantitative evaluation index system according to the load data of the power grid side and the electric vehicle parameters, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index system comprises a subjective response potential index and an objective response potential index;
the comprehensive weight calculation module is used for grouping the plurality of electric vehicles to be scheduled according to the electric vehicle load characteristic parameters to obtain a plurality of electric vehicle classes, and determining the comprehensive weight of the electric vehicle response power grid adjustment capability quantitative evaluation index of each electric vehicle class by using an entropy weight method;
and the evaluation result calculation module is used for evaluating according to the comprehensive weight of the electric vehicle response power grid regulation capability quantitative evaluation index and a preset evaluation method to obtain an evaluation result, so that power grid personnel can formulate a scheduling strategy according to the evaluation result.
10. The electric vehicle access power grid adjustment capability quantitative evaluation system according to claim 9, wherein the electric vehicle response power grid adjustment capability quantitative evaluation index is constructed according to the load data of the power grid side and the electric vehicle parameters, and the electric vehicle response power grid adjustment capability quantitative evaluation index comprises a subjective response potential index and an objective response potential index, specifically:
establishing objective response potential indexes according to the load data of the power grid side and the electric vehicle parameters, wherein the objective response potential indexes comprise voltage qualification rate, average voltage deviation value, energy loss rate after faults, electricity utilization reliability, partial equipment shutdown times, battery service life, charge and discharge electric quantity constraint and charging pile power constraint, and the voltage qualification rate is as follows:
R N,i,t ＝F N,j,t (U max )-F N,j,t (U min )
wherein F is N,j,t (g) The probability distribution function of the voltage amplitude of the node i at the moment t is obtained; u (U) max For the upper limit of the qualified voltage, U min Is the lower limit of the qualified voltage;
the average voltage deviation value is:
in the method, in the process of the invention,and->The voltage rising deviation index and the voltage falling deviation index of the node i at the time t are respectively +.>Is the average voltage deviation +.>And- >The voltage of the node I at the moment t is respectively at the upper bound and the lower bound of the node probability distribution confidence interval I, U ref The voltage amplitude value is the voltage amplitude value of the line root node;
the energy loss rate after the fault is as follows:
C FE ＝p FE T FL
wherein S is FL For loss of capacity for load, S SL N is the total capacity of the system FC As the number of all lost users after failure, N SC S is the total number of users of the system FL,i For the ith lost user capacity, S FL,j For the capacity of the jth user of the system, gamma FL,i For the level factor of the ith resected user, gamma SL,j The j-th user of the system has a ranking factor of 0 to 1, and the more important the user is, the larger the ranking factor is, T FL The fault repair time;
the electricity utilization reliability is as follows:
wherein t is m Representing the total power failure time of the mth charging user in the distribution network in the statistical time, wherein M represents the total number of charging users in the distribution network, and T represents the statistical time length;
the specific expression of the shutdown times of the partial equipment is as follows:
n EOT ＝∑P m
wherein P is m Representing the times that the mth billing user in the distribution network has equipment outage or unavailability when the system is not powered off due to the power quality problem in the statistical time;
the service life of the battery is as follows:
wherein S (i, t) is the SOC of user i at time t, S DR (i, t) means the amount of variation in SOC caused when the user i is charged or discharged at a time point t in response to the power P (i, t), S min And S is max SOC upper and lower limits P of EV battery c And P d Rated charge and discharge power of EV, t arrive And t leave Arrival time and expected departure time, η, of user i, respectively c Charge efficiency at the time of EV charging, C 0 EV battery capacity;
the charge-discharge electric quantity constraint is as follows:
S max ≥S(i,t)+S DR (i,t)≥S ex (i)
S(i,t)+S DR (i,t)≥S min (i)
wherein S is ex (i) A desired charge amount for user i;
the power constraint of the charging pile is as follows:
P m (i,t)≤P r (i,t)≤P M (i,t)
wherein P is c (i, t) is charging power, P d (i, t) is discharge power, P r (i, t) is the rated charge or discharge power of user i at time t, P m (i,t)、P M (i, t) is the minimum charge (discharge) power and the maximum charge or discharge power, respectively, of user i at time t;
establishing objective response potential indexes, wherein the objective response potential indexes comprise a user charging positive response curve and a charging negative response curve, and the user charging positive response curve is as follows:
in the method, in the process of the invention,representing a charging front response curve, e is the incentive price of an electric vehicle load aggregator for EV users to participate in DR response, g cm And g dm The maximum charge and discharge response rates of the users are respectively;
in the method, in the process of the invention,representing a charging negative response curve, e is the incentive price of the electric vehicle load aggregator to the EV user to participate in DR response, e c1 Charging excitation response critical value for user, when excitation electricity price user charging excitation response critical value, charging response of user is always greater than 0, e cm For the charging saturated excitation electricity price of the user, when the excitation electricity price reaches the charging saturated excitation electricity price, the charging response of the user maintains g cm Unchanged g cm The maximum charge response rate is for the user. />
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