Electric vehicle cluster charging strategy optimization method and device, equipment and medium

Abstract

The invention discloses an electric vehicle cluster charging strategy optimization method, device and equipment and a medium, and relates to the field of electric vehicle charging optimization, and the method comprises the steps: constructing a flexibility feasible region of each electric vehicle according to a flexibility model of each electric vehicle, charging characteristics and upper and lower limits of electric quantity, calculating a reference set of the electric vehicle cluster according to the flexibility feasible regions of all the electric vehicles in the electric vehicle cluster; performing affine transformation on the reference set by adopting a structure preserving transformation method to obtain an H polyhedron for internal approximation; solving the polyhedral linear optimization model by taking the maximum volume of the H polyhedron as a target to obtain an overall flexibility set of the electric vehicle cluster; constructing a target function and a charging power constraint condition based on the overall flexibility set; according to the method, the utilization rate of renewable energy sources can be improved, and meanwhile, carbon emission is reduced.

Description

Technical Field

[0001] The present application relates to the field of electric vehicle charging optimization, and in particular to an electric vehicle cluster charging strategy optimization method, device, equipment and medium. Background Art

[0002] Renewable energy (such as solar energy, wind energy, etc.) is intermittent and uncertain. Its power generation capacity usually fluctuates greatly with factors such as weather and time, which brings certain challenges to the stable operation of the power grid. When there is too much power generated by renewable energy, the power grid may not be able to fully absorb these power resources, resulting in the phenomenon of wind and solar power abandonment. When the power generation is insufficient, the power grid needs to quickly mobilize other power sources to supplement. Therefore, how to effectively use renewable energy to generate electricity and shave peaks and fill valleys has become an important issue facing the power grid.

[0003] As a distributed and adjustable load resource, electric vehicles (EVs) can help the power grid cope with the volatility of renewable energy through flexible charging and discharging behaviors. By adjusting the charging time and power, they can reduce the peak-to-valley difference of the power system and improve the stability and economy of the power system operation.

[0004] Electric vehicles are cars that use batteries or other rechargeable devices as a power source. These electric vehicles can be charged through charging facilities and store electrical energy in battery packs. In the power system, electric vehicles can be used as a renewable energy storage device to store electrical energy for later use through intelligent charging and discharging management, thereby improving the reliability and stability of the power system. In addition, electric vehicles can also participate in the power system by interacting with the power grid, providing flexible peak-shaving and energy storage services for the power system.

[0005] Electric vehicles have been widely used in power system frequency regulation, peak load shaving and valley filling, and new energy consumption. However, since electric vehicles are scattered at the bottom of the system structure, their ability to participate in optimized scheduling is obviously insufficient; in addition, due to differences in energy storage capacity, regulation characteristics and physical parameters, different electric vehicles are heterogeneous. Therefore, in order to give full play to the flexibility of electric vehicles, they must be integrated and quantified for better coordinated scheduling.

[0006] The so-called integration and quantification refers to the effective integration of the energy storage and release capabilities of electric vehicles with the needs of the power system, and the evaluation and management of them through quantitative methods. At present, the integration means of electric vehicles is mainly a hierarchical control architecture. In this scheme, the load aggregator acts as an interface between electric vehicles and the power grid. The load aggregator needs to model the overall flexibility of all controllable electric vehicles and participate in peak shaving and valley filling in the form of electric vehicle aggregates; in addition, the load aggregator also needs to dispatch electric vehicles to respond to more advanced commands. Therefore, the quantification and modeling of the flexibility of electric vehicle clusters is crucial for load aggregators to respond to demand reliably and effectively.

[0007] After electric vehicles are connected to the power grid through charging piles, they have the ability to smooth loads and participate in grid interaction, meeting their own power needs while responding to the flexibility needs of the grid. However, the flexibility of a single electric vehicle is limited. In order to effectively participate in peak load shaving and valley filling, it is usually necessary to aggregate many electric vehicles into an electric vehicle cluster and adopt effective methods to quantify the flexibility of electric vehicle aggregates to accurately reflect the response characteristics of electric vehicles when they participate in services.

[0008] In the process of flexibility quantification, existing charging strategies usually do not fully consider the generation time and volatility of renewable energy (such as photovoltaic and wind power), which leads to low utilization of renewable energy, increases the phenomenon of wind and solar power abandonment, and relies on more fossil fuel power generation to meet charging needs at night, which is not conducive to reducing carbon emissions. Summary of the invention

[0009] The purpose of this application is to provide a method, device, equipment and medium for optimizing the charging strategy of electric vehicle clusters, which can improve the utilization rate of renewable energy while reducing carbon emissions.

[0010] To achieve the above objectives, this application provides the following solutions:

[0011] In a first aspect, the present application provides a method for optimizing a charging strategy for an electric vehicle cluster, comprising:

[0012] Determine electric vehicle parameters for the electric vehicle cluster;

[0013] Modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model of each electric vehicle;

[0014] Constructing a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics, and upper and lower limits of power, and calculating a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster;

[0015] Performing an affine transformation on the reference set using a structure-preserving transformation method to obtain an H polyhedron for internal approximation;

[0016] Taking the maximum volume of the H polyhedron as the goal, solving the polyhedron linear optimization model to obtain the overall flexibility set of the electric vehicle cluster;

[0017] Constructing an objective function and charging power constraints based on the overall flexibility set, charging characteristics and power upper and lower limits, electric vehicle charging power, and renewable energy generation power;

[0018] Under the constraint of the charging power constraint, the objective function is solved to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

[0019] In a second aspect, the present application provides an electric vehicle cluster charging strategy optimization device, comprising:

[0020] a parameter determination module, for determining electric vehicle parameters of an electric vehicle cluster;

[0021] A flexibility model building module, used for modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters, so as to obtain a flexibility model of each electric vehicle;

[0022] A benchmark set calculation module, used to construct a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics and upper and lower limits of power, and calculate a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster;

[0023] An approximate polyhedron construction module, used for performing affine transformation on the reference set by adopting a structure-preserving transformation method to obtain an H polyhedron for internal approximation;

[0024] A polyhedron optimization module, used for solving a polyhedron linear optimization model with the goal of maximizing the volume of the H polyhedron, to obtain an overall flexibility set of the electric vehicle cluster;

[0025] A charging model building module, used to build an objective function and charging power constraints according to the overall flexibility set, charging characteristics and upper and lower limits of power, electric vehicle charging power, and renewable energy generation power;

[0026] The charging strategy optimization module is used to solve the objective function under the constraint of the charging power constraint condition to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

[0027] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for optimizing charging strategies for electric vehicle clusters.

[0028] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing a charging strategy for an electric vehicle cluster.

[0029] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0030] The present application provides a method, device, equipment and medium for optimizing the charging strategy of an electric vehicle cluster. The base set of the electric vehicle cluster is calculated according to the flexibility feasible domain of all electric vehicles in the electric vehicle cluster. The base set is affine transformed by a structure-preserving transformation method to obtain an H polyhedron (Half-plane polytopes) for internal approximation, and the polyhedron linear optimization model is solved with the goal of maximizing the volume of the H polyhedron to obtain the overall flexibility set of the electric vehicle cluster, and the flexibility quantification of the electric vehicle cluster is accurately and efficiently realized. The optimal charging strategy of the electric vehicle cluster is obtained based on the overall flexibility set. The matching amount of the electric vehicle charging power and the renewable energy generation power given by the optimal charging strategy ensures the maximum utilization rate of the renewable energy. The present application solves with the goal of maximizing the utilization rate of renewable energy, fully considers the renewable energy, improves the utilization rate of renewable energy, and reduces carbon emissions at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 This is an application environment diagram of an electric vehicle cluster charging strategy optimization method in one embodiment of the present application;

[0033] Figure 2A schematic diagram of a flow chart of an electric vehicle cluster charging strategy optimization method provided in one embodiment of the present application;

[0034] Figure 3 A schematic diagram of a detailed process of aggregating the feasible domain of electric vehicle flexibility provided in one embodiment of the present application;

[0035] Figure 4 A schematic diagram of functional modules of a device for optimizing charging strategy for electric vehicle clusters provided in another embodiment of the present application;

[0036] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] As a key element in the development of future power grids, electric vehicles are gradually showing their great potential in promoting energy transformation and achieving green and low-carbon goals. How to reasonably aggregate multiple electric vehicles to form a strong force is the key to better tapping into the role of electric vehicles in serving the power system.

[0040] In order to realize the responsiveness of electric vehicles and effectively participate in peak load shifting, the embodiment of the present application provides an electric vehicle cluster charging strategy optimization method, which involves an electric vehicle cluster flexibility feasible domain approximation method based on structure-preserving transformation. The electric vehicle cluster charging strategy optimization method of the embodiment of the present application is based on the overall flexibility of the electric vehicle cluster. for It is very difficult to accurately calculate the Minkowski sum of this polyhedron. The embodiment of the present application adopts an inner approximation feasible domain aggregation method based on structure preservation to achieve flexibility aggregation of electric vehicle clusters, so as to achieve a closer fit between the charging power of electric vehicles and the power generation power of renewable energy, better play the role of peak shaving and valley filling, and optimize the charging strategy.

[0041] The electric vehicle cluster charging strategy optimization method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the electric vehicle parameters of the electric vehicle cluster to the server 104. After the server 104 receives the electric vehicle parameters of the electric vehicle cluster, for the electric vehicle parameters, the server 104 models the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model for each electric vehicle; constructs a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics, and upper and lower limits of power, and calculates a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster; uses a structure-preserving transformation method to perform an affine transformation on the benchmark set to obtain an H polyhedron for internal approximation; solves the polyhedron linear optimization model with the goal of maximizing the volume of the H polyhedron to obtain an overall flexibility set for the electric vehicle cluster; constructs an objective function and charging power constraints according to the overall flexibility set, charging characteristics, upper and lower limits of power, electric vehicle charging power, and renewable energy generation power; solves the objective function under the constraints of the charging power constraints to obtain an optimal charging strategy for the electric vehicle cluster.

[0042] The server 104 can feed back the obtained electric vehicle parameters to the terminal 102. In addition, in some embodiments, the electric vehicle cluster charging strategy optimization method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly process the electric vehicle parameters, or the server 104 can obtain the electric vehicle parameters from the data storage system and process the electric vehicle parameters.

[0043] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for optimizing the charging strategy of an electric vehicle cluster is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 207. Among them:

[0045] Step 201 : determining electric vehicle parameters of an electric vehicle cluster.

[0046] Step 202 : Modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model of each electric vehicle.

[0047] Step 203 , constructing a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics and upper and lower limits of power, and calculating a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster.

[0048] Step 204: perform affine transformation on the reference set using a structure-preserving transformation method to obtain an H polyhedron for internal approximation.

[0049] Step 205 , solving the polyhedron linear optimization model with the goal of maximizing the volume of the H polyhedron to obtain the overall flexibility set of the electric vehicle cluster.

[0050] Step 206, constructing an objective function and charging power constraint conditions according to the overall flexibility set, charging characteristics and upper and lower limits of power, electric vehicle charging power, and renewable energy generation power.

[0051] Step 207, under the constraint of the charging power constraint condition, solve the objective function to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

[0052] Implementing the above steps 201 to 207 can improve the utilization rate of renewable energy and reduce carbon emissions.

[0053] In another exemplary embodiment of the present application, step 201 specifically includes: sampling data from an electric vehicle cluster to obtain sampled data; performing Monte Carlo sampling on the sampled data to generate electric vehicle parameters; the electric vehicle parameters include: the number of electric vehicles, the time when the electric vehicle arrives at the charging pile, the time when the electric vehicle leaves the charging pile, the capacity of the electric vehicle, the maximum charging power of the electric vehicle, the minimum charging power of the electric vehicle, the initial charging state of the electric vehicle, and the final charging state of the electric vehicle.

[0054] In another exemplary embodiment of the present application, step 202 specifically includes:

[0055] According to the electric vehicle parameters, random sampling is used to simulate the behavior of each electric vehicle in the electric vehicle cluster, so as to achieve modeling of the charging dynamic process of each electric vehicle in the electric vehicle cluster and obtain a flexibility model of each electric vehicle. Specifically:

[0056] For an electric vehicle cluster (EV cluster) consisting of N electric vehicles (EVs), the charging dynamic process of the i-th electric vehicle is extracted to construct a model.

[0057] The net energy provided to each EV can be described by a discrete time difference equation within a finite time range, as shown in Equation (1).

[0058] x i (t+1)=x i (t)+u i (t)·δ (1)

[0059] Where: δ is the time interval between adjacent moments, t∈T:={0,1,...,T-1}; x i (t) represents the i-th electric vehicle EV i The amount of electricity obtained at time t, x i (t+1) represents the i-th electric vehicle EV i The amount of electricity obtained at time t+1, Use x i (0) = 0 to x i (t) represents the amount of energy delivered to the EV in the previous time period t i The net energy of . Using the vector represents the resulting net energy distribution, which satisfies the relationship in equation (1). At the same time, u i (t) represents the i-th electric car exist The charging power at the moment, vector represents the i-th electric vehicle EV i The charging characteristics of the i-th electric vehicle EV. i The lossless model of the charging dynamic process, whose equivalent form is usually used to model the flexibility of electric vehicle clusters, is thus i The expression of the flexibility model is:

[0060]

[0061] in, represents the flexibility feasible domain of the i-th electric vehicle; u i represents the lower limit of the charging characteristics of the i-th electric vehicle, represents the upper limit of the charging characteristics of the i-th electric vehicle, represents a T×1 dimensional matrix; x i represents the lower limit of the power of the i-th electric vehicle, represents the upper limit of the power of the i-th electric vehicle; L represents a lower triangular matrix, whose non-zero elements are all δ, represents a T×T dimensional matrix; u represents the charging characteristics of the electric vehicle; := represents defined as or set to.

[0062] The flexibility model can capture the time-varying power and energy constraints of electric vehicles, power demand, charging completion and deadlines, and the connection status of electric vehicles at different points in time. In addition, the model can be compatible with the aggregation model used in the electricity market by setting upper and lower limits on power or battery status at different points in time.

[0063] In another exemplary embodiment of the present application, step 203 specifically includes:

[0064] (1) Determine the charging characteristics and upper and lower limits of power.

[0065] Specifically, step 203 constructs the i-th electric vehicle EV according to the above formula (2): i The flexible feasible domain of charging characteristics and power upper and lower limits are determined first. The specific process is as follows: the i-th electric vehicle EV i The flexibility feasible domain U i are all bounded sets consisting of 4T closed half-spaces, that is, each U in the electric vehicle cluster is a convex polyhedron. For the i-th electric vehicle, the charging start time is a i , the charging end time of the i-th electric car is d i EV i Its charging characteristics and upper and lower limits of power can be shown by formula (3).

[0066]

[0067] in, represents the upper limit of the charging characteristics of the i-th electric vehicle at time t; u i (t) represents the lower limit of the charging characteristic of the i-th electric vehicle at time t; x i (t) represents the lower limit of the power of the i-th electric car at time t, represents the upper limit of the power of the i-th electric car at time t; represents the maximum charging power of the i-th electric vehicle; represents the minimum charging power of the i-th electric vehicle; I represents the T×T dimensional unit matrix; a irepresents the charging start time of the i-th electric vehicle; d i represents the charging end time of the i-th electric vehicle; represents the maximum battery capacity of the i-th electric vehicle; represents the initial state of the i-th electric vehicle when charging begins; Indicates the state of the i-th electric vehicle when charging is completed;

[0068] (2) Substituting the charging characteristics and the upper and lower limits of the power (Formula (3)) into the flexibility model (Formula (2)), the flexibility feasible domain of each electric vehicle is obtained. To simplify the representation, the expression of its flexibility feasible domain is shown in Formula (4).

[0069]

[0070] in, represents the flexibility feasible domain of the i-th electric vehicle; u represents the charging characteristics of the electric vehicle; represents a T×1-dimensional matrix; H represents a polyhedral matrix, H:=(L,-L,I T ,-I T ), L represents the lower triangular matrix, I T represents the T-dimensional identity matrix; h i represents the flexibility set parameter of the i-th electric vehicle, represents the upper limit of the power of the ith electric car, x i represents the lower limit of the power of the i-th electric vehicle, represents the upper limit of the charging characteristics of the i-th electric vehicle, u i represents the lower limit of the charging characteristics of the i-th electric vehicle.

[0071] (3) The flexibility feasible domains of all electric vehicles in the electric vehicle cluster are averaged to obtain a benchmark set of the electric vehicle cluster. The expression of the benchmark set is shown in formula (5).

[0072]

[0073] The right side vector is the average value of each flexibility set parameter, h i is the flexibility model parameter of the ith electric vehicle. The choice of the benchmark set aims to approximate the set of (possibly heterogeneous) individual flexibility sets in an averaged manner.

[0074] The Minkowski summation of the flexibility feasible domain of each electric vehicle in the electric vehicle cluster is used to obtain the overall flexibility set of the electric vehicle cluster. As shown in formula (6).

[0075]

[0076] in, It is also a T-dimensional polyhedron.

[0077] In another exemplary embodiment of the present application, step 204 specifically includes:

[0078] In order to solve the overall flexibility set of electric vehicle clusters (a polyhedron), constructing an H polyhedron To approximate the polyhedron To meet in, Given a benchmark set (an H expression polyhedron) is obtained by affine transformation, the H polyhedron The expression of is shown in formula (7).

[0079]

[0080] in, represents the H polyhedron; represents the benchmark set; represents the translation parameter; P represents the scaling parameter.

[0081] As an H polyhedron, we can construct a linear optimization to make the H polyhedron P fit the actual flexibility set, that is, adopt the optimization problem of polyhedron inclusion as shown in formula (8).

[0082]

[0083] Where: and are the optimization variables of formula (8); And Vol(·) is a T-dimensional volume function. So far, the optimal inner polyhedron accommodation problem has been simplified to a linear programming problem to achieve a conservative inner approximation of the feasible domain of electric vehicle cluster flexibility.

[0084] In another exemplary embodiment of the present application, step 205 specifically includes:

[0085] (1) Construct a polyhedron linear optimization model. Specifically:

[0086] First, an internal approximation is constructed for each individual EV flexibility set to simplify the translation parameter in equation (8) in step 204. The calculation problem of P and the scaling parameter, that is, the inner approximation of the flexibility model of a single electric vehicle can be expressed as formula (9).

[0087]

[0088] Where: and These are optimization variables used to ensure that each EV i The polyhedron of can fully approximate its flexible feasible domain, γ i represents the first optimization variable when approximating the flexibility model of the i-th electric vehicle; Γ i represents the second optimization variable when approximating the flexibility of the i-th electric vehicle within the model.

[0089] The Minkowski sum of the polyhedron of each electric vehicle is completed by adding the γ elements and Γ elements in equation (9) respectively, as shown in equation (10).

[0090]

[0091] Combining equation (10) with equation (7), we can get the relationship between the translation parameters: The scaling parameter relationship is Polyhedron It can be obtained by performing relevant translation and scaling operations on Γ and γ, and then obtaining That is to say, the benchmark set is affine transformed using the structure-preserving transformation method, so as to reduce the flexibility of a single electric vehicle. The feasible domain is significantly different from the benchmark set in shape or dimension, so as to complete the internal approximation of the total flexibility set.

[0092] By combining equation (8) and equation (10), we can get The volume can be converted into formula (11).

[0093]

[0094] Since the benchmark set is fixed, so maximizing The volume of is equivalent to maximizing the scaling factor P, that is, equation (8) can be transformed into the linearization problem of equation (12). The expression of the polyhedron linear optimization model is shown in equation (12).

[0095]

[0096] Wherein, maximize means maximization; st means the constraint condition of the polyhedron linear optimization model; P means the scaling parameter; represents the translation parameter; N represents the number of electric vehicles in the electric vehicle cluster; γ i represents the first optimization variable when approximating the flexibility model of the i-th electric vehicle; Γ i represents the second optimization variable when approximating the flexibility model of the i-th electric vehicle;i represents the equivalent matrix after mapping the transformation of the i-th electric car; H represents the polyhedral matrix; h i represents the flexibility set parameter of the i-th electric vehicle; h0 represents the average value of the flexibility set parameters of all electric vehicles in the electric vehicle cluster. P, γ i , Γ i and Λ i is the decision variable of the linear programming problem shown in formula (12).

[0097] (2) With the goal of maximizing the volume of the H polyhedron, the polyhedron linear optimization model is solved to obtain the values ​​of the scaling parameters and the translation parameters; the values ​​of the scaling parameters and the translation parameters are substituted into the expression of the H polyhedron to obtain the value of the H polyhedron; and the overall flexibility set of the electric vehicle cluster is determined according to the value of the H polyhedron.

[0098] In this step, the maximum scaling parameter P is obtained by solving the above equation (12) and substituting it into equation (7) to obtain Finally, the overall flexibility set of the electric vehicle cluster is obtained as shown in formula (6):

[0099] In another exemplary embodiment of the present application, in step 206, the overall flexibility set of the electric vehicle cluster obtained in step 205 is The electric vehicle characteristics obtained by formula (3) are used to define the charging power P of the electric vehicle. charge (t), an objective function for maximizing the utilization rate of renewable energy is constructed based on the electric vehicle charging power and the renewable energy generation power. The expression of the objective function is shown in formula (13).

[0100]

[0101] Where T represents the total time of the scheduling cycle; η represents the charging efficiency; x i (t) x i (t) represents the power of the i-th electric car at time t, u i (t) represents the charging power of the i-th electric vehicle at time t, U represents the overall flexibility set; V max Indicates the maximum voltage output by the electric vehicle; V min represents the minimum voltage output by the electric vehicle; V(t) represents the voltage output by the electric vehicle at time t; P RE (t) represents the power generated by renewable energy at time t; P charge (t) represents the charging power of the electric vehicle at time t; min(P charge (t),P RE(t)) represents the matching amount between the electric vehicle charging power and the renewable energy generation power at time t.

[0102] The charging power constraint condition is determined according to the balance constraint, and the charging power constraint condition is shown in formula (14).

[0103] P charge (t)+P load (t)≤P RE (t) (14)

[0104] Among them, P load (t) represents the line load. This charging power constraint ensures that the system reduces peak load and power abandonment while meeting the needs of electric vehicles.

[0105] Subsequently, based on the established objective function and charging power constraints, the matching amount of electric vehicle charging power and renewable energy generation power is optimized, and the charging strategy of electric vehicles is dynamically adjusted.

[0106] The embodiment of the present application aims at the problem of optimal aggregation of electric vehicles. By converting non-convex optimization into convex optimization, the flexibility of electric vehicle clusters is approximated by the affine transformation of convex polyhedrons in the half-space representation, and the flexibility of electric vehicle clusters is approximated by the structure-preserving transformation of the feasible domain of electric vehicle cluster flexibility, so as to optimize the charging strategy. The steps of the electric vehicle aggregation method are summarized as follows: a mathematical model (i.e., flexibility model) is established based on the charging characteristics of electric vehicles; the flexibility feasible domain of a single electric vehicle is analyzed; an inner approximate convex body (i.e., H polyhedron) is constructed. ) and linearize it; maximize the H polyhedron The volume of electric vehicle clusters is Perform inner approximation processing; complete the aggregation of the feasible domain of electric vehicle flexibility, as follows Figure 3 As shown; optimal scheduling is performed based on the aggregated feasible domain (i.e., the overall flexibility set) and the output of renewable energy power generation power.

[0107] The embodiments of the present application have the following advantages: the current related technologies mostly aggregate the polyhedron formed by the feasible domains of multiple aggregated units in the form of Minkowski sums, and the accurate calculation of the Minkowski sums of multiple polyhedrons is very difficult. The amount of calculation of the Minkowski sums of multiple convex polyhedrons represented by vertices grows exponentially with the dimension of the polyhedron. In addition, since the flexibility of a single electric vehicle is usually provided in the form of H representing a polyhedron, the flexibility feasible domain of a group of electric vehicles is usually difficult to calculate accurately. This embodiment calculates the maximum volume approximation of the set flexibility set by optimizing the affine transformation of a given convex polyhedron, and converts the complex geometric structure into a simpler form, so that the maximum volume approximation problem is conservatively approximated to a linear programming, and the maximum volume inclusion problem is approximately solved by linear programming, which is proportional to the number and dimension of each flexibility set in the form of a polynomial. By considering the affine transformation of a more general convex polyhedron, the maximum volume approximation of the aggregated flexibility set can be effectively approximated, and the adjustment of the charging strategy can be better participated.

[0108] Specifically, this embodiment characterizes the flexibility feasible domain of a series of single electric vehicles as an affine transformation of a given benchmark set, and simplifies the optimization problem of polyhedron internal approximation into a linear programming problem about the number of flexibility feasible domains of single electric vehicles and the dimensional polynomial scaling factor, so as to achieve accurate and efficient quantification of the flexibility of electric vehicle groups. Compared with related internal approximation methods, this embodiment can effectively improve the approximate accuracy of the flexibility feasible domain of electric vehicle groups and provide higher flexibility for the power system. This embodiment can effectively guide aggregators to use electric vehicle clusters to participate in peak shaving and valley filling and flexibility supply.

[0109] Based on the same inventive concept, the embodiment of the present application also provides an electric vehicle cluster charging strategy optimization device for implementing the electric vehicle cluster charging strategy optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more electric vehicle cluster charging strategy optimization device embodiments provided below can refer to the limitations of the electric vehicle cluster charging strategy optimization method above, and will not be repeated here.

[0110] In an exemplary embodiment, Figure 4 As shown, a device for optimizing charging strategy of electric vehicle cluster is provided, comprising:

[0111] The parameter determination module 401 is used to determine the electric vehicle parameters of the electric vehicle cluster.

[0112] The flexibility model building module 402 is used to model the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model of each electric vehicle.

[0113] The benchmark set calculation module 403 is used to construct the flexibility feasible domain of each electric vehicle according to the flexibility model, charging characteristics and upper and lower limits of power, and calculate the benchmark set of the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster.

[0114] The approximate polyhedron construction module 404 is used to perform affine transformation on the reference set using a structure-preserving transformation method to obtain an H polyhedron for internal approximation.

[0115] The polyhedron optimization module 405 is used to solve the polyhedron linear optimization model with the goal of maximizing the volume of the H polyhedron to obtain the overall flexibility set of the electric vehicle cluster.

[0116] The charging model building module 406 is used to build an objective function and charging power constraints according to the overall flexibility set, charging characteristics and upper and lower limits of power, electric vehicle charging power and renewable energy generation power.

[0117] The charging strategy optimization module 407 is used to solve the objective function under the constraint of the charging power constraint condition to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

[0118] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store electric vehicle parameters of an electric vehicle cluster. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing a charging strategy for an electric vehicle cluster is implemented.

[0119] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0124] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing charging strategy of electric vehicle clusters, characterized in that: The electric vehicle cluster charging strategy optimization method comprises: Determine electric vehicle parameters for the electric vehicle cluster; Modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model of each electric vehicle; Constructing a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics, and upper and lower limits of power, and calculating a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster; Performing an affine transformation on the reference set using a structure-preserving transformation method to obtain an H polyhedron for internal approximation; Taking the maximum volume of the H polyhedron as the goal, solving the polyhedron linear optimization model to obtain the overall flexibility set of the electric vehicle cluster; Constructing an objective function and charging power constraints based on the overall flexibility set, charging characteristics and power upper and lower limits, electric vehicle charging power, and renewable energy generation power; Under the constraint of the charging power constraint, the objective function is solved to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

2. The electric vehicle cluster charging strategy optimization method according to claim 1 is characterized in that: Determine the electric vehicle parameters for the electric vehicle cluster, including: Sampling data of the electric vehicle cluster to obtain sampling data; Monte Carlo sampling is performed on the sampled data to generate electric vehicle parameters; the electric vehicle parameters include: the number of electric vehicles, the time when the electric vehicle arrives at the charging pile, the time when the electric vehicle leaves the charging pile, the capacity of the electric vehicle, the maximum charging power of the electric vehicle, the minimum charging power of the electric vehicle, the initial charging state of the electric vehicle and the final charging state of the electric vehicle.

3. The electric vehicle cluster charging strategy optimization method according to claim 1 is characterized in that: Modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters to obtain a flexibility model of each electric vehicle, specifically including: According to the electric vehicle parameters, random sampling is used to simulate the behavior of each electric vehicle in the electric vehicle cluster, so as to achieve modeling of the charging dynamic process of each electric vehicle in the electric vehicle cluster and obtain a flexibility model of each electric vehicle; the expression of the flexibility model is: in, represents the flexibility feasible domain of the i-th electric vehicle; u i represents the lower limit of the charging characteristics of the i-th electric vehicle, represents the upper limit of the charging characteristics of the i-th electric vehicle, represents a T×1 dimensional matrix; x i represents the lower limit of the power of the i-th electric vehicle, represents the upper limit of the power of the i-th electric vehicle; L represents the lower triangular matrix, represents a T×T dimensional matrix; u represents the charging characteristics of the electric vehicle.

4. The electric vehicle cluster charging strategy optimization method according to claim 1 is characterized in that: The flexibility feasible domain of each electric vehicle is constructed according to the flexibility model, the charging characteristics and the upper and lower limits of the power, and the benchmark set of the electric vehicle cluster is calculated according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster, specifically including: Determine the charging characteristics and the upper and lower limits of the power; the expressions of the charging characteristics and the upper and lower limits of the power are: in, represents the upper limit of the charging characteristics of the i-th electric vehicle at time t; u i (t) represents the lower limit of the charging characteristic of the i-th electric vehicle at time t; x i (t) represents the lower limit of the power of the i-th electric car at time t, represents the upper limit of the power of the i-th electric car at time t; represents the maximum charging power of the i-th electric vehicle; represents the minimum charging power of the i-th electric vehicle; I represents the T×T dimensional unit matrix; a i represents the charging start time of the i-th electric vehicle; d i represents the charging end time of the i-th electric vehicle; represents the maximum battery capacity of the i-th electric vehicle; represents the initial state of the i-th electric vehicle when charging begins; Indicates the state of the i-th electric vehicle when charging is completed; Substituting the charging characteristics and the upper and lower limits of power into the flexibility model, the flexibility feasible domain of each electric vehicle is obtained; the expression of the flexibility feasible domain is: in, represents the flexibility feasible domain of the i-th electric vehicle; u represents the charging characteristics of the electric vehicle; represents a T×1-dimensional matrix; H represents a polyhedral matrix, H:=(L,-L,I T ,-I T ), L represents the lower triangular matrix, I T represents the T-dimensional identity matrix; h i represents the flexibility set parameter of the i-th electric vehicle, represents the upper limit of the power of the ith electric car, x i represents the lower limit of the power of the i-th electric vehicle, represents the upper limit of the charging characteristics of the i-th electric vehicle, u i represents the lower limit of the charging characteristics of the i-th electric vehicle; An average calculation is performed on the flexibility feasible regions of all electric vehicles in the electric vehicle cluster to obtain a benchmark set of the electric vehicle cluster.

5. The electric vehicle cluster charging strategy optimization method according to claim 1 is characterized in that: The expression of the H polyhedron is: in, represents the H polyhedron; represents the benchmark set; represents the translation parameter; P represents the scaling parameter.

6. The electric vehicle cluster charging strategy optimization method according to claim 1 is characterized in that: Taking the maximum volume of the H polyhedron as the goal, the polyhedron linear optimization model is solved to obtain the overall flexibility set of the electric vehicle cluster, which specifically includes: Construct a polyhedron linear optimization model; the expression of the polyhedron linear optimization model is: Wherein, maximize means maximization; st means the constraint condition of the polyhedron linear optimization model; P means the scaling parameter; represents the translation parameter; N represents the number of electric vehicles in the electric vehicle cluster; γ i represents the first optimization variable when approximating the flexibility model of the i-th electric vehicle; Γ i represents the second optimization variable when approximating the flexibility model of the i-th electric vehicle; i represents the equivalent matrix after mapping the transformation of the i-th electric car; H represents the polyhedral matrix; h i represents the flexibility set parameter of the i-th electric vehicle; h0 represents the average flexibility set parameter of all electric vehicles in the electric vehicle cluster; Taking the maximum volume of the H polyhedron as the goal, solving the polyhedron linear optimization model to obtain the values ​​of the scaling parameter and the translation parameter; Substitute the values ​​of the scaling and translation parameters into the expression of the H polyhedron to obtain the value of the H polyhedron; The overall flexibility set of the electric vehicle cluster is determined according to the value of the H polyhedron.

7. The electric vehicle cluster charging strategy optimization method according to claim 1, characterized in that: The expression of the objective function is: Where T represents the total time of the scheduling cycle; η represents the charging efficiency; x i (t) x i (t) represents the power of the i-th electric car at time t, u i (t) represents the charging power of the i-th electric vehicle at time t, represents the overall flexibility set; V max Indicates the maximum voltage output by the electric vehicle; V min represents the minimum voltage output by the electric vehicle; V(t) represents the voltage output by the electric vehicle at time t; P RE (t) represents the power generated by renewable energy at time t; P charge (t) represents the charging power of the electric vehicle at time t; min(P charge (t),P RE (t)) represents the matching amount between the charging power of electric vehicles and the power generated by renewable energy at time t; The charging power constraint conditions include: P charge (t)+P load (t)≤P RE (t); Among them, P load (t) represents the line load.

8. An electric vehicle cluster charging strategy optimization device, characterized in that: The electric vehicle cluster charging strategy optimization device comprises: a parameter determination module, for determining electric vehicle parameters of an electric vehicle cluster; A flexibility model building module, used for modeling the charging dynamic process of each electric vehicle in the electric vehicle cluster according to the electric vehicle parameters, so as to obtain a flexibility model of each electric vehicle; A benchmark set calculation module, used to construct a flexibility feasible domain for each electric vehicle according to the flexibility model, charging characteristics and upper and lower limits of power, and calculate a benchmark set for the electric vehicle cluster according to the flexibility feasible domains of all electric vehicles in the electric vehicle cluster; An approximate polyhedron construction module, used for performing affine transformation on the reference set by adopting a structure-preserving transformation method to obtain an H polyhedron for internal approximation; A polyhedron optimization module, used for solving a polyhedron linear optimization model with the goal of maximizing the volume of the H polyhedron, to obtain an overall flexibility set of the electric vehicle cluster; A charging model building module, used to build an objective function and charging power constraints according to the overall flexibility set, charging characteristics and upper and lower limits of power, electric vehicle charging power, and renewable energy generation power; The charging strategy optimization module is used to solve the objective function under the constraint of the charging power constraint condition to obtain the optimal charging strategy of the electric vehicle cluster; the optimal charging strategy includes: the matching amount of the electric vehicle charging power and the renewable energy generation power corresponding to the maximum utilization rate of renewable energy.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the electric vehicle cluster charging strategy optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric vehicle cluster charging strategy optimization method described in any one of claims 1 to 7 is implemented.

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