CN105024432B - A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price - Google Patents
A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price Download PDFInfo
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Abstract
A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price, including：The prediction of electric energy public service platform, the basic daily load information of sampling optimization period region of interest within；When there is the charging pile in new EV accesses target area, its inbound information is read；User inputs the charge information of vehicle；Build EV charge-discharge electric power models；Virtual electricity price information is calculated, reflects the load level of target area indirectly；Build the scheduling model using charge-discharge electric power as optimized variable；Comprehensive wavelet analysis pretreatment and fuzzy clustering method determine the dynamic sharing electricity price calculated for user cost；The autonomous Response Decision of user；It is electrically operated to implement charge and discharge to EV according to user's decision-making, and uploads plan.The present invention can realize the peak load shifting of EV cluster loads, and reduce user's discharge and recharge cost on the basis of user's charge requirement and distribution transformer capacity limitation is met.When EV cluster scales are larger, the present invention remains to meet that grid side it is expected.
Description
The technical field is as follows:
the invention belongs to the technical field of Electric Vehicle (EV) and power grid interaction, and particularly relates to an electric vehicle charge and discharge optimal scheduling based on virtual electricity price and an implementation method thereof.
The background art comprises the following steps:
in recent years, the demand for energy and environment is increasing, the shortage of fossil fuel and global warming are attracting more and more attention, and the increase of environmental protection concept makes people strongly demand the reduction of petroleum consumption in the aspect of traffic. The electric automobile has good energy-saving and low-emission potential due to a special energy driving mode, and is widely developed. The electric automobile can improve the energy utilization efficiency and reduce the pollution to the environment, the popularization and promotion of the electric automobile become the future trend, and active policy measures are also adopted by various countries to encourage the development of the electric automobile.
The electrified revolution in transportation systems has gradually shifted the energy demand of vehicles from fossil fuels to electric power systems. However, with the large-scale development of electric vehicles, because the charging behavior of vehicle owners is often random, a large number of electric vehicles are connected to a power grid for charging, and will certainly cause huge pressure on the structure and operation of the power grid. The load peak-valley difference is an important safe and economic index for the operation of the power system, and the aggravation of the peak-valley difference can bring adverse consequences such as the reduction of the utilization efficiency of power grid equipment, the increase of the power purchase risk of the power supply side and the like. A large number of electric vehicles are randomly accessed into a power grid to carry out disordered charging, so that the load peak-valley difference of the system is further aggravated, and negative effects are brought to the running state of a distribution network.
The charging and discharging of the electric automobile connected to the power distribution network are reasonably controlled, so that the influence of large-scale electric automobile charging on the power distribution network can be reduced, and the requirements on the stability and the economy of a power distribution system are met. At present, many domestic and foreign research achievements for ordered charging and discharging of electric vehicles exist. Non-patent document 1 proposes an ordered charging strategy based on dynamic time-of-use electricity prices, which reduces the user charging cost while achieving peak clipping and valley filling of the charging load, but does not consider the role of V2G. Non-patent document 2 establishes a distributed electric vehicle charging and discharging game model with the aim of minimizing power consumption cost, improves power consumption economy, and adjusts a system load curve. The invention patent application with the application number of 201410233619.7 provides an electric vehicle cluster charging and discharging optimization control method, and provides a feasible theoretical basis for electric vehicles to participate in power grid interaction. The research is often lack of consideration on continuous adjustability of charge and discharge power, and a development and perfection space exists in the optimization depth.
List of references:
[ non-patent document 1] "an electric vehicle charging station ordered charging strategy based on dynamic time-of-use electricity prices", proceedings of the chinese motor engineering, 2014, 34 (22);
non-patent document 2, "research on distributed electric vehicle networking policy", journal of electrotechnical art, 2014, 29 (8);
[ patent document ] "electric vehicle cluster charge-discharge optimization control method", 201410233619.7.
The invention content is as follows:
the invention provides an electric vehicle charging and discharging optimization scheduling method based on virtual electricity price, aiming at overcoming the defects in the prior art.
The invention aims at peak clipping and valley filling, gives consideration to the load information of a power distribution system, the electric energy loss cost of a user and the battery loss cost, and provides a virtual electricity price-based electric vehicle charge and discharge optimal scheduling and an implementation method thereof, so that when a large number of electric vehicles are connected into a power grid for charging, peak clipping and valley filling of electric vehicle cluster loads are realized, and the charge and discharge cost of the user is reduced. An electric vehicle charging and discharging optimization scheduling model (CA-vTOS) with charging and discharging power as optimization variables is established based on a virtual electricity price theory, and dynamic time-of-use electricity price for calculating charging and discharging cost of electric vehicle users is determined by integrating wavelet analysis and fuzzy clustering. The technical scheme comprises the following specific processes:
1) The basic daily load information of the target area in the electric energy public service platform prediction and sampling optimization time period specifically comprises the following steps:
s11, predicting a basic load power curve: predicting a basic daily load curve of a target area according to known parameters and a model;
s12, continuous time discretization in an optimization time period: determining the minimum optimization time interval delta t, carrying out discretization analysis on the optimization time interval, and sequentially carrying out the basic daily load obtained in the step S11Sampling the curve to obtain each k epsilon [1, J ]]Predicted load value L in time interval B (k) (ii) a J is the number of optimized time periods divided according to set determination in one day;
2) When a new electric vehicle l is connected to the charging pile in the target area, network access information of the vehicle l is read, wherein the network access information comprises network access time T in,l Power battery capacity C s,l And an initial State of Charge (SOC) S 0,l Etc.;
3) The user inputs charging information of the vehicle i: expected off-grid time T out,l Desired state of charge S E,l (ii) a Judging whether the charging requirement of the user can be met or not by combining the read information and the user input information, if not, informing the user of incorrect input through a user interaction interface, prompting the user to input expected off-network time or expected SOC information again, if the user receives the prompt and inputs implementable and correct charging information, performing the operations of the steps 4) to 10), otherwise, giving up the user, and executing the step 2);
4) Constructing a charge-discharge power model of the electric automobile: p l (k)＝p l (k)f m,l (k)；
In the formula, P l (k) Represents charge/discharge power of the vehicle l; p is a radical of l (k) Representing the exchange of power between the vehicle l and the system during a period k, p l (k)>, 0 represents that the vehicle l is in a charging state; p is a radical of l (k)&0 represents in a discharge state; p is a radical of formula l (k) =0 indicates in a float state; f. of m,l (k) For characterizing the operability of the vehicle battery for each time period, the expression is:
wherein, T m,l Duration T for switching on the grid for vehicle l pe,l ＝T out,l -T in,l A set of time periods involved;
assuming that the power batteries of the electric vehicle participating in scheduling are all lithium batteries, according to the charge-discharge related characteristics of the lithium batteries, in a single time period, the lithium batteries can be regarded as constant-power charge-discharge, and the relationship between the charge state and the corresponding charge-discharge time is characterized as follows:
S l (k)＝S l (k-1)+P l (k)η(P l (k))Δt/C s,l (2)
in the formula, S l (k-1)、S l (k) Representing the SOC of the vehicle l in the k-1 th and k-th periods respectively; eta (P) l (k) Power exchange efficiency, in particular with respect to the power exchange direction:
wherein eta is c 、η d Respectively representing charging and discharging efficiencies;
5) Reading total load information of target area at vehicle access moment from electric energy public service platform
Wherein, the first and the second end of the pipe are connected with each other,the distribution network total load information of the target area in the k time period is shown when the vehicle I is accessed;representing the cluster load of the electric automobile:wherein, M l-1 The vehicle set represents a vehicle set which finishes the charge plan formulation when the vehicle l is connected into the power grid; the calculation process is completed by an electric energy public service platform;
6) Calculating virtual electricity price information, and indirectly reflecting the load level of a target area:
in the formula (I), the compound is shown in the specification,a virtual electricity price representing a k time period when the vehicle l is accessed;andrepresenting a virtual electricity price adjustment coefficient; I.C. A R,j 、φ R,j Respectively representing a reference electricity price and a reference load value; [ u ] of] + Represents max {0, u };represents the predicted total load, whereinRepresenting a base load predicted value;
7) Establishing an electric automobile charge-discharge optimization scheduling model;
by combining the steps, and aiming at minimizing the virtual charge-discharge cost, a charge-discharge optimization scheduling model of the electric automobile is established to optimize the charge-discharge power of the electric automobile, wherein the established model is as follows:
s.t.S min ≤S l (k)≤S max (7)
-P d ≤P l (k)≤P c (8)
k＝1,2,…,J.
T pe,l >T c,l l＝1,2,…,n (11)
in the formula (6), V l Represents a virtual charge-discharge cost of the vehicle l; n is a radical of hydrogen l Represents T m,l The length of the collection; in the formula (7), S max 、S min Maximum and minimum values of the allowable SOC to prevent overcharge and overdischarge of the controlled vehicle; formula (8) represents the charge-discharge power constraint, P l (k) The device has the characteristic of continuous adjustability, but is generally limited by the rated charging and discharging power of a power battery or a charger; equation (9) represents a charging demand constraint, such that the SOC of the battery of the vehicle is required to meet the expectations when the vehicle is about to leave; equation (10) represents the transformer maximum load constraint, κ T For transformer efficiency, A T The rated capacity of the transformer; equation (11) represents a time relationship constraint, n is the scale of the accessed vehicles in the optimization period, T c,l Minimum time required to charge to the desired SOC: t is c,l ＝(S E,l -S 0,l )C s,l /P c η c ；
Solving the optimized scheduling model to complete the optimization of the charge and discharge power of the currently accessed vehicle, wherein at the moment, the charge and discharge scheduling of the vehicle l is as follows:wherein, the first and the second end of the pipe are connected with each other,representation set T m,l The ith element in (1);
8) Calculating the user cost of the electric automobile: u shape l ＝(c cd,l |η c ＝η d ＝1)+c bat,l +c loss,l ；
In the formula of U l Represents a user cost; c. C bat,l Represents the cost converted from the life loss of the lithium battery of the vehicle l; c. C loss,l Represents the cost of electric energy loss; c. C cd,l |η c ＝η d =1, which represents an ideal charge/discharge cost irrespective of charge/discharge efficiency:pri (k) represents electricity price information, and the electricity price information is dynamic time-of-use electricity price in the invention, namely electricity prices with fixed peak and low valley and electricity prices with variable peak and valley periods, wherein the peak and low valley electricity prices are respectively represented as pri h 、pri l (ii) a The method comprises the following steps of utilizing wavelet analysis and fuzzy clustering methods to divide time-of-use electricity price peak-valley time periods:
s81, pretreatment: carrying out wavelet decomposition with the load information of 3 in scale, setting the high-frequency components of the first layer and the second layer to zero, and obtaining new load information after reconstruction
S82, attribute characterization: attribute representation is carried out on the reconstructed load information by adopting slightly large and slightly small semi-trapezoidal fuzzy distribution to form an attribute matrix A (a) J×2 The calculation method comprises the following steps:
s83, carrying out translation-standard deviation transformation on the matrix A, and establishing a fuzzy similar matrix R (R) by using an absolute value subtraction method J×J ；
S84, solving the continuous quadratic form of the similar matrix, namely R → R 2 →R 4 →…→R 2i → 8230until R appears k οR k ＝R k (o denotes the fuzzy matrix synthesis operation), at this time, R k Namely the transfer closure t (R) of the similarity matrix;
s85, in the transfer closure, t (R) = (t) ij ) In, let λ be t ij To find the lambda-intercept of t (R)Array R ij Then, taking values of lambda from large to small to form dynamic clustering, and taking a proper lambda value to determine a peak-valley time interval division scheme;
9) Autonomous response decision of the electric vehicle user;
determining the charging and discharging cost of the user by using the charging and discharging power of the electric automobile obtained by the optimization model in the step 7) and the dynamic time-of-use electricity price obtained in the step 8), informing the user of the charging and discharging scheduling plan and the corresponding income condition, and selecting the response scheduling plan or starting disordered charging by the user in an autonomous response charging mode;
10 Performing charging and discharging operations on the electric automobile according to the user decision and uploading the plan;
if the user selects to start disordered charging, the charging facility provides continuous constant-power charging service for the accessed electric automobile until the charging requirement of the user is met or the vehicle leaves; if the user chooses to respond to the scheduling, then according to the scheduling plan P l Carrying out specific charging and discharging operations on the electric automobile, and accordingly determining a charging and discharging plan of the vehicle l; uploading the charge-discharge plan to an electric energy public service platform, integrating the planned load by the electric energy public service platform, completing one-time real-time update of the target area load information, and waiting for the next electric automobile to access the network; if a new vehicle is accessed, jumping to the step 2); this process will continue until the optimization period ends.
The beneficial effects of the invention at least comprise the following aspects:
(1) The peak clipping and valley filling of the electric automobile cluster load can be realized on the basis of meeting the charging requirements of users and the capacity limit of a distribution transformer, the charging and discharging cost of the users is reduced, and the electric automobile cluster load is easily accepted by the users of the electric automobiles.
(2) When the cluster scale of the electric automobile is large, the load curve can be smoothed in the period of connecting the power grid, the occurrence of the peak-valley singularity phenomenon is avoided, the user income is correspondingly reduced at the moment, and the expectation of the power grid side is met.
(3) The optimized power utilization of the system is realized by exerting the load transfer potential of the electric automobile cluster; meanwhile, the dynamic time-of-use electricity price for charging and discharging of the electric automobile can be matched and optimized to obtain charging and discharging power and provide certain benefit incentive for electric automobile users participating in scheduling, an implementation method is provided for the established model, and bidirectional balance of power supply and demand and economic benefits of the users is facilitated.
Description of the drawings:
FIG. 1 is a diagram of an electric vehicle charge-discharge optimization scheduling architecture according to the present invention;
FIG. 2 is a flow chart of the electric vehicle charge-discharge optimization scheduling based on virtual electricity prices according to the present invention;
FIG. 3 is a load curve for the chaotic and CA-vTOS charge-discharge optimization modes of the present invention;
FIG. 4 is a graph comparing user costs in the chaotic and CA-vTOS charge-discharge optimization modes of the present invention;
FIG. 5 is a graph comparing load curves of the disordered and CA-vTOS charge-discharge optimization modes for different electric vehicle scales.
The specific implementation mode is as follows:
the invention is further described with reference to the accompanying drawings, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention. The electric vehicle charge-discharge optimization scheduling architecture constructed by the invention is shown as the attached figure 1, and comprises the following steps:
1) The basic daily load information of the target area in the electric energy public service platform prediction and sampling optimization time period specifically comprises the following steps:
s11, predicting a basic load power curve: predicting a basic daily load curve of a target area according to known parameters and a model;
s12, continuous time discretization in an optimization time period: a minimum optimization time period deltat is determined,discretizing analysis is carried out on the optimized time interval, and the basic daily load curves obtained in the step S11 are sampled in sequence to obtain each k E [1, J ]]Predicted value L of load in time interval B (k) (ii) a J is the number of optimized time periods divided according to set determination in one day;
2) When a new electric vehicle l is connected to the charging pile in the target area, network access information of the vehicle l is read, wherein the network access information comprises network access time T in,l Power battery capacity C s,l And initial state of charge S 0,l Etc.;
3) The user inputs charging information of the vehicle i: expected off-grid time T out,l Desired state of charge S E,l (ii) a Judging whether the charging requirement of the user can be met or not by combining the read information and the user input information, if not, informing the user of incorrect input through a user interaction interface, prompting the user to input expected off-network time or expected SOC information again, if the user receives the prompt and inputs implementable and correct charging information, performing the operations of the steps 4) to 10), otherwise, giving up the user, and executing the step 2);
4) Constructing a charge-discharge power model of the electric automobile: p is l (k)＝p l (k)f m,l (k)；
Assuming that all power batteries of the electric vehicle participating in scheduling are lithium batteries, according to the charge-discharge related characteristics of the lithium batteries, the lithium batteries can be regarded as constant-power charge-discharge in a single time period, and the representation of the relation between the charge state and the corresponding charge-discharge time is shown as a formula (2);
5) Reading total load information of target area at vehicle I access moment from electric energy public service platform
6) Calculating virtual electricity price information to indirectly reflect the load level of the target area, wherein the relation between the virtual electricity price and the load is shown in formulas (4) to (5):
7) Establishing an electric vehicle charge-discharge optimization scheduling model as shown in formulas (6) to (11);
solving the optimized scheduling model to finish the current access vehicleThe optimization of vehicle charge-discharge power, at this moment, the charge-discharge regulation of vehicle l plans to be:wherein, the first and the second end of the pipe are connected with each other,representation set T m,l The ith element in (1);
8) Calculating the cost of the electric automobile user: u shape l ＝(c cd,l |η c ＝η d ＝1)+c bat,l +c loss,l ；
9) Autonomous response decision of the electric vehicle user;
determining the charging and discharging cost of the user by using the charging and discharging power of the electric vehicle obtained by the optimization model in the step 7) and the dynamic time-of-use electricity price obtained in the step 8), informing the user of the charging and discharging scheduling plan and the corresponding income condition, and selecting the response scheduling plan or starting disordered charging by the user in an autonomous response charging mode;
10 According to the user decision, carrying out charging and discharging operations on the electric automobile and uploading the plan;
if the user selects to start disordered charging, the charging facility provides continuous constant-power charging service for the accessed electric automobile until the charging requirement of the user is met or the vehicle leaves; if the user chooses to respond to the scheduling, then according to the scheduling plan P l Carrying out specific charging and discharging operations on the electric automobile, and accordingly determining a charging and discharging plan of the vehicle l; uploading the charging and discharging plan to an electric energy public service platform, integrating the planned load by the electric energy public service platform, completing one-time real-time updating of the load information of the target area, and waiting for the next electric automobile to be connected to the network; if a new vehicle is accessed, jumping to the step 2); this process will continue until the optimization period is over.
The step 1) of the invention is completed by the charge of an electric energy public service platform; and (3) performing steps 2) to 10) in each charging pile in the target area, wherein the specific process is shown in the attached figure 2.
In the present embodiment, one group includes residentsThe power distribution network of the district charging facility cluster is a target research area. The capacity of an access transformer is 750kVA, the efficiency is 0.95, the transformer is provided with a base load and an electric automobile cluster load, and the highest base line load accounts for 80% of the maximum load of the distribution transformer. The quantity of the electric automobiles provided with the distribution network service is 50, the power battery capacity of the electric automobiles is 60 kW.h, the rated charging and discharging power is 7kW, the charging and discharging efficiency is 0.92, and the SOC boundary of the batteries (S) max 、S min ) 0.9 and 0.1. According to the characteristics of the residential basic load information,is-0.21; i is R,1 、I R,2 Respectively setting low valley electricity price and high peak electricity price as 0.37425 yuan/(kW & h) and 1.5096 yuan/(kW & h); phi is a R,1 Taking the mean value of the load at the valley 405.1019 phi R,2 The difference between the peak and valley load means was taken as 75.7441. The probability of the user selecting the response charge-discharge scheduling is 1, the expected SOC when the user leaves is 0.9, the design calculation time length is 24h, and the time interval delta t is 0.5h. λ was chosen to be 0.9.
The method comprises the following steps of (1) setting that the time point when an electric vehicle user leaves a residence in the morning is subjected to normal distribution (an expected value is 7; the point in time when the residence is returned to afternoon follows a normal distribution (expectation value of 19, standard deviation of 1.5 h; when the vehicle returns to the residence, the SOC of the vehicle power battery follows normal distribution (the expected value is 0.6, and the standard deviation is 0.1), and the parameters of the charge-discharge starting-stopping time, the starting SOC and the like of the EV are mutually independent.
In order to better embody the control effect of the present invention, the simulation is compared with the disordered charging mode. The load curve in the unordered and CA-vTOS charging and discharging optimization mode is shown in figure 3, and the cost ratio of each electric vehicle user is shown in figure 4.
TABLE 1 statistical information of optimization effects of unordered and CA-vTOS
Table 1 shows the statistics of the optimization effect of the disorder and CA-vTOS. As can be known by combining the table 1, the attached drawings 3 and the attached drawings 4, in the disordered charging mode, a large number of electric vehicles are charged in the load peak time period at night, the peak valley difference of the system is further aggravated, the maximum load exceeds 10.55% of the capacity limit of the transformer, and the safe and reliable operation of the distribution network is influenced. In the CA-vTOS charging and discharging optimization mode, the vehicle discharges at the peak of the virtual electricity price and charges at the valley, the peak clipping and valley filling effects are realized on the system load, the peak-valley difference and the load fluctuation rate are reduced compared with the disordered charging, and the CA-vTOS charging and discharging optimization mode can flexibly adjust the charging and discharging power, so that the CA-vTOS charging and discharging optimization mode has greater advantages in the aspect of improving the load fluctuation.
In order to embody the superiority of the invention in comparison with the disordered charging mode under different electric vehicle cluster scales, a load curve comparison graph of the CA-vTOS charging and discharging optimization mode and the disordered charging mode under different electric vehicle cluster scales is obtained through simulation in the embodiment, and is shown in an attached figure 5; and load curve statistics for the CA-vTOS mode at different scales, as shown in table 2.
TABLE 2 statistics of CA-vTOS patterns at different scales
As can be seen from fig. 5 and table 2, as the charging energy permeability increases, the load fluctuation standard deviation increases, which indicates that the scale of the accessed electric vehicle is larger and exceeds the optimal access scale of the distribution network, but in the main parking period of the vehicle (between 16.
As described above, the present invention can be preferably implemented, and the above-described embodiments are only exemplary embodiments of the present invention and are not intended to limit the scope of the implementation of the present invention, and various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principle and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.
Claims (1)
1. Electric vehicle charging and discharging optimal scheduling method based on virtual electricity price, wherein the rated value of charging power of each charging pile is P c Rated value of discharge power P d The method comprises the following steps:
1) The basic daily load information of the target area in the electric energy public service platform prediction and sampling optimization time period specifically comprises the following steps:
s11, predicting a basic load power curve: predicting a basic daily load curve of the target area according to the known parameters and the model;
s12, continuous time discretization in an optimization time period: determining the minimum optimization time interval delta t, carrying out discretization analysis on the optimization time interval, and sequentially sampling the basic daily load curve obtained in the step S11 to obtain each k E [1, J ]]Predicted value L of load in time interval B (k) (ii) a J is the number of optimized time segments divided according to the set determination in one day;
2) When a new electric automobile l is connected to the charging pile in the target area, network access information of the automobile l is read, wherein the network access information comprises network access time T in,l Power battery capacity C s,l And initial state of charge S 0,l ；
3) The user inputs charging information of the vehicle i: expected off-grid time T out,l Desired state of charge S E,l (ii) a Judging whether the charging requirement of the user can be met or not by combining the read information and the user input information, if not, informing the user of incorrect input through a user interaction interface, prompting the user to input expected off-network time or expected SOC information again, if the user receives the prompt and inputs implementable and correct charging information, performing the operations of the steps 4) to 10), otherwise, giving up the user, and executing the step 2);
4) Constructing a charge-discharge power model of the electric automobile: p l (k)＝p l (k)f m,l (k)；
In the formula, P l (k) Represents charge/discharge power of the vehicle l; p is a radical of l (k) Representing the exchange of power between the vehicle l and the system during a period k, p l (k) 0 indicates that the vehicle i is in a charged state; p is a radical of l (k) < 0 indicates being dischargedA state; p is a radical of l (k) =0 indicates in float state; f. of m,l (k) For characterizing the operability of the vehicle battery at each time interval, the expression is as follows:
wherein, T m,l Duration T for switching on the grid for vehicle l pe,l ＝T out,l -T in,l A set of time periods involved;
assuming that the power batteries of the electric vehicle participating in scheduling are all lithium batteries, according to the charge-discharge related characteristics of the lithium batteries, the lithium batteries can be regarded as constant-power charge-discharge in a single time period, and the relationship between the charge state and the corresponding charge-discharge time is characterized as follows:
S l (k)＝S l (k-1)+P l (k)η(P l (k))Δt/C s,l (2)
in the formula, S l (k-1)、S l (k) Respectively representing the SOC of the vehicle l in k-1 and k periods; eta (P) l (k) Power exchange efficiency, in particular with respect to the power exchange direction:
wherein eta is c 、η d Respectively showing charging and discharging efficiencies;
5) Reading total load information of target area at vehicle access moment from electric energy public service platform
Wherein the content of the first and second substances,to representWhen the vehicle l is accessed, the total load information of the distribution network in the target area in the k time period;representing the cluster load of the electric automobile:wherein M is l-1 The vehicle set represents a vehicle set which finishes the charge plan formulation when the vehicle l is connected into the power grid; total load informationThe calculation process of (2) is completed by the electric energy public service platform;
6) Calculating virtual electricity price information, and indirectly reflecting the load level of a target area:
in the formula (I), the compound is shown in the specification,a virtual electricity price representing a k time period when the vehicle l is accessed;andrepresenting a virtual electricity price adjustment coefficient; i is R,j 、φ R,j Respectively representing a reference electricity price and a reference load value; [ u ] of] + Represents max {0, u };indicating predicted total negativityA lotus root, whereinRepresenting a base load predicted value;
7) Establishing an electric automobile charge-discharge optimization scheduling model;
by combining the steps, and aiming at minimizing the virtual charge-discharge cost, a charge-discharge optimization scheduling model of the electric automobile is established to optimize the charge-discharge power of the electric automobile, wherein the established model is as follows:
s.t.S min ≤S l (k)≤S max (7)
-P d ≤P l (k)≤P c (8)
T pe,l ＞T c,l l＝1,2,…,n (11)
in the formula (6), V l Represents a virtual charge-discharge cost of the vehicle l; n is a radical of hydrogen l Represents T m,l The length of the collection; in formula (7), S max 、S min Maximum and minimum values of the allowable SOC to prevent overcharge and overdischarge of the controlled vehicle; equation (8) represents the charge-discharge power constraint, P l (k) The device has the characteristic of continuous adjustability, but is generally limited by the rated charging and discharging power of a power battery or a charger; equation (9) represents a charging demand constraint, such that the SOC of the battery of the vehicle is required to meet the expectations when the vehicle is about to leave; equation (10) represents the maximum load constraint of the transformer, κ T For transformer efficiency, A T The rated capacity of the transformer; equation (11) represents the time relationship constraintN is the scale of the accessed vehicles in the optimized time period T c,l Minimum time required to charge to the desired SOC: t is c,l ＝(S E,l -S 0,l )C s,l /P c η c ；
Solving the optimized scheduling model to complete the optimization of the charge and discharge power of the currently accessed vehicle, wherein at the moment, the charge and discharge scheduling of the vehicle l is as follows:wherein the content of the first and second substances,representation set T m,l The ith element in (1);
8) Calculating the cost of the electric automobile user: u shape l ＝(c cd,l |η c ＝η d ＝1)+c bat,l +c loss,l ；
In the formula of U l Represents a user cost; c. C bat,l Represents the cost converted from the life loss of the lithium battery of the vehicle l; c. C loss,l Represents the cost of electric energy loss; c. C cd,l |η c ＝η d And =1, which represents an ideal charge and discharge charge without considering charge and discharge efficiency:pri (k) represents electricity price information, and is a dynamic time-of-use electricity price, namely, electricity prices with fixed peak and low valley and electricity prices with variable peak and valley periods, wherein the peak and low valley electricity prices are respectively represented as pri h 、pri l (ii) a The method comprises the following steps of utilizing wavelet analysis and fuzzy clustering methods to divide time-of-use electricity price peak-valley time periods:
s81, pretreatment: carrying out wavelet decomposition with the load information of a scale of 3, setting high-frequency components of the first layer and the second layer to zero, and obtaining new load information after reconstruction
S82, attribute characterization: adopts a slightly large and a slightly small semi-trapeziumThe fuzzy distribution carries out attribute representation on the reconstructed load information to form an attribute matrix A (a) J×2 The calculation method comprises the following steps:
s83, performing translation-standard deviation transformation on the matrix A, and establishing a fuzzy similarity matrix R (R) by using an absolute value subtraction method J×J ；
S84, solving the continuous quadratic power of the similar matrix, namely R → R 2 →R 4 →…→R 2i → 8230until appearance Represents a fuzzy matrix synthesis operation, in this case, R k I.e. the transitive closure t (R) of the similarity matrix;
s85, in the transmission closure t (R) = (t) ij ) In, let λ be t ij To obtain a lambda-intercept matrix R of t (R) ij Then, the lambda is taken from large to small to form dynamic clustering, and a proper lambda value is taken to determine a peak-valley time period division scheme;
9) Autonomous response decision of the electric vehicle user;
determining the charging and discharging cost of the user by using the charging and discharging power of the electric vehicle obtained by the optimization model in the step 7) and the dynamic time-of-use electricity price obtained in the step 8), informing the user of the charging and discharging scheduling plan and the corresponding income condition, and selecting the response scheduling plan or starting disordered charging by the user in an autonomous response charging mode;
10 Performing charging and discharging operations on the electric automobile according to the user decision and uploading the plan;
if the user selects to start the disordered charging, the charging facility provides continuous constant-power charging service for the accessed EV until the charging requirement of the user is met or the vehicle leaves; if the user selects to respond to the scheduling, the scheduling plan P is selected l Carrying out specific charging and discharging operations on the electric automobile, and accordingly determining a charging and discharging plan of the vehicle l; uploading the charging and discharging plan to an electric energy public service platform, integrating the planned load by the electric energy public service platform, completing one-time real-time updating of the load information of the target area, and waiting for the next electric automobile to be connected to the network; if a new vehicle is accessed, jumping to the step 2); this process will continue until the optimization period is over.
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