An electric vehicle charging and discharging power control method and device

Abstract

The application discloses a kind of electric vehicle charging and discharging power control method and device, the method is according to the basic data of each electric vehicle in cluster, with total cost minimum value as purpose, constructs the upper optimization model of double-layer optimization model;And, with the minimum of total charging cost of aggregator as purpose, the lower optimization model of double-layer optimization model is constructed;Then in the case where satisfying preset KKT condition, the double-layer optimization model is converted into single-layer optimization model, and the single-layer model is solved, and the optimized peak-valley price and power generation plan are obtained;Finally, according to the optimized peak-valley price and the power generation plan, a dispatching plan is generated, and the dispatching plan is issued to the electric vehicle aggregator in cluster, so that the electric vehicle aggregator controls the charging and discharging power of electric vehicle according to the dispatching plan. By implementing the application, electric vehicles can be efficiently aggregated, and electric vehicles can be promoted to participate in dispatching.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method and apparatus for controlling the charging and discharging power of electric vehicles. Background Technology

[0002] Currently, when electric vehicles participate in grid dispatch, they can effectively leverage the flexibility and regulation capabilities of power batteries as mobile energy storage or controllable loads, providing crucial support for the efficient operation of new power systems. With the large-scale growth of electric vehicles, their role in grid dispatch is becoming increasingly important. However, in this process, aggregators (AGGs) are typically used as intermediaries between the grid and electric vehicle owners. Aggregators integrate electric vehicles and communicate the aggregated power and energy feasibility range to distribution system operators. Subsequently, the distribution system operators issue dispatch instructions to the electric vehicle aggregators based on the reported feasible areas. Therefore, there is an urgent need to propose a new processing method that can accurately aggregate electric vehicles and promote their participation in grid dispatch. Summary of the Invention

[0003] This invention provides a method and apparatus for controlling the charging and discharging power of electric vehicles. The method constructs a two-layer optimization model and solves it to obtain optimized peak-valley prices and power generation plans. Based on the optimized peak-valley prices and power generation plans, a scheduling plan is obtained, enabling the aggregator to schedule the charging and discharging power of electric vehicles according to the scheduling plan, so as to realize the full participation of electric vehicles in grid scheduling.

[0004] An embodiment of the present invention provides a method for controlling the charging and discharging power of an electric vehicle, comprising:

[0005] Determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster;

[0006] Based on the basic data of each electric vehicle in the cluster, a higher-level optimization model of the two-level optimization model is constructed with the goal of minimizing the total cost. Based on the basic data, constraints on active and reactive power of branches, active and reactive power of nodes, upper and lower limits of active and reactive power of nodes and lines, upper and lower limits of active and reactive power output of distributed generators, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints are constructed.

[0007] Based on the basic data of each electric vehicle in the cluster, a lower-level optimization model of the two-layer optimization model is constructed with the goal of minimizing the total charging cost of the aggregator; and an aggregation feasible region constraint is constructed based on the aggregation polyhedron.

[0008] Under the condition of satisfying the preset KKT conditions, the two-layer optimization model is converted into a single-layer optimization model;

[0009] Under the constraints of active and reactive power of the branches, active and reactive power of the nodes, upper and lower limits of active and reactive power of the nodes and lines, upper and lower limits of active and reactive power output of distributed generators, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, the single-layer model is solved to obtain the optimized peak-valley price and power generation plan; wherein, the power generation plan includes the power wholesale market energy power generation plan, the distributed generator power generation plan, and the wind turbine and photovoltaic power generation plan.

[0010] Based on the optimized peak-valley price and the power generation plan, a scheduling plan is generated and distributed to the electric vehicle aggregator within the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

[0011] Furthermore, the process of determining the electric vehicle cluster includes:

[0012] Obtain samples of each electric vehicle;

[0013] Repeat the cluster center calculation operation until each pre-selected cluster center no longer moves, and use each pre-selected cluster center that no longer moves as the final cluster center;

[0014] For each cluster center, the electric vehicles assigned to the current cluster center are divided into an electric vehicle cluster;

[0015] Several electric vehicle clusters were obtained;

[0016] Specifically, the cluster center calculation operation is as follows:

[0017] For each electric vehicle sample, based on the current pre-selected cluster centers, the distance between the current electric vehicle sample and each pre-selected cluster center is calculated, and the current electric vehicle sample is assigned to the nearest pre-selected cluster center; wherein, the pre-selected cluster centers for the first clustering are randomly selected from each electric vehicle sample;

[0018] After all electric vehicle samples have been assigned, for each pre-selected cluster center, the average value of all electric vehicle samples assigned to the current pre-selected cluster center is calculated. It is then determined whether the current pre-selected cluster center coincides with the average value. If so, the current pre-selected cluster center remains unchanged. If not, the current pre-selected cluster center is moved to the average value to form a new pre-selected cluster center.

[0019] Furthermore, the process of determining the aggregated feasible region corresponding to the current electric vehicle cluster includes:

[0020] Select the current electric vehicle cluster from several electric vehicle clusters;

[0021] Based on the basic data of each electric vehicle in the current electric vehicle cluster, under the constraints of energy and power boundaries, a corresponding polyhedron is constructed to characterize the feasible region.

[0022] Based on the polyhedron of each electric vehicle in the current electric vehicle cluster, a polyhedron prototype for characterizing the feasible region of the current electric vehicle cluster is obtained.

[0023] Based on the polyhedron prototype and the plurality of polyhedra, the homogeneous polyhedron corresponding to each electric vehicle in the current cluster for characterizing the feasible region is calculated.

[0024] Based on all the obtained homogeneous polyhedra, and under the condition of satisfying the preset feasible region constraints, the aggregated polyhedra used to characterize the aggregated feasible region of the current electric vehicle cluster are obtained.

[0025] Furthermore, the energy and power boundary constraints include:

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] in, It is a battery exist The energy of a moment; It is a battery exist The energy of a moment; yes Time and The time difference between moments; It is the charging and discharging power; It refers to charge / discharge efficiency; This refers to the moment when the charging head is inserted and removed; This refers to the maximum charging and discharging power; It is between and At some point in the middle; These are the maximum and minimum battery capacities; Is the battery i in The upper and lower limits of energy at any given moment; yes The target battery energy at which charging stops; It is a battery exist The target battery energy at the start of charging.

[0032] Furthermore, based on the basic data of each electric vehicle in the current electric vehicle cluster, and under energy and power boundary constraints, the construction of corresponding polyhedra to characterize feasible regions includes:

[0033] ;

[0034] ;

[0035] ;

[0036] in, Let represent the polyhedron used to characterize the feasible region corresponding to the i-th electric vehicle; Represents a T-dimensional identity matrix; , It is a row vector that arranges the charging power of battery i at each time step; , It is a row vector that arranges the discharge power of battery i at each time step; It is a matrix that combines the charging and discharging power of battery i at all times; yes The coefficient matrix; and It is a lower triangular matrix, and its lower triangular elements are respectively and , yes Time and The time difference between moments; yes The upper limit, of which , It is all 1s 3D column vector, , , , ,and .

[0037] Furthermore, the preset feasible region constraint includes:

[0038] ;

[0039] ;

[0040] ;

[0041] in, Let the objective function be denoted as , and the goal be to minimize it; the variables are . And satisfy .

[0042] Furthermore, the process of determining the aggregated polyhedron corresponding to the current electric vehicle cluster includes:

[0043] The aggregated polyhedron corresponding to the current electric vehicle cluster is calculated using the following formula:

[0044] ;

[0045] ;

[0046] in, Represents the polyhedral prototype within the cluster; and These represent all electric vehicles within the cluster. and The mean; It is the charging power of all electric vehicles within the cluster. The mean; It is the power generation capacity of all electric vehicles within the cluster. The mean; yes and The combination matrix, , express The dimension of a matrix; Represents the aggregated polyhedron of the current electric vehicle cluster; It represents the number of electric vehicles in the cluster; It is the scaling factor. It is the translation coefficient; and The optimal equals and ; , , They are The corresponding coefficient matrix, charge / discharge power matrix, and upper limit matrix.

[0047] Furthermore, the upper-level optimization model of the two-level optimization model includes:

[0048] ;

[0049] in, It refers to energy prices in the wholesale electricity market; Energy purchased from the wholesale electricity market; It is the number of aggregators; It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It is the total number of nodes in the circuit; This refers to the reactive power of distributed generators (DG). It is the secondary / primary / zero-time coefficient for power generation cost.

[0050] Furthermore, the lower-level optimization model of the two-level optimization model includes:

[0051] ;

[0052] in, It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It represents the number of aggregators.

[0053] This application also provides an electric vehicle charging and discharging power control device, including: a polyhedron determination module, an upper-level optimization model and constraint determination module, a lower-level optimization model and constraint determination module, a model conversion module, a calculation module, and a scheduling module;

[0054] The aggregation polyhedron determination module is used to determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster;

[0055] The upper-level optimization model and constraint determination module are used to construct the upper-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the goal of minimizing the total cost; and to construct the following constraints based on the basic data: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints.

[0056] The lower-level optimization model and constraint determination module is used to construct the lower-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the goal of minimizing the total charging cost of the aggregator; and to construct the aggregation feasible region constraint based on the aggregation polyhedron.

[0057] The model conversion module is used to convert the two-layer optimization model into a single-layer optimization model when the preset KKT conditions are met.

[0058] The calculation module is used to solve the single-layer model under the following constraints: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, to obtain optimized peak and valley prices and power generation plans; wherein, the power generation plans include power wholesale market energy generation plans, distributed generator power generation plans, and wind turbine and photovoltaic power generation plans.

[0059] The scheduling module is used to generate a scheduling plan based on the optimized peak-valley price and the power generation plan, and to distribute the scheduling plan to the electric vehicle aggregator in the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

[0060] The following benefits can be obtained by implementing the present invention:

[0061] This invention provides a method and apparatus for controlling the charging and discharging power of electric vehicles. The method involves: determining the aggregate polyhedron of the current electric vehicle cluster; constructing an upper-level optimization model of a two-layer optimization model with the goal of minimizing total cost based on the basic data of each electric vehicle within the cluster; constructing a lower-level optimization model of the two-layer optimization model with the goal of minimizing the total charging cost of the aggregator; constructing corresponding constraints based on the basic data of each electric vehicle; converting the two-layer optimization model into a single-layer optimization model under preset KKT conditions; and solving the single-layer model while satisfying the constructed constraints to obtain the optimized peak-valley price. The system generates and distributes electricity to electric vehicles (EVs). Based on the optimized peak-valley prices and the power generation plan, EVs are scheduled. By constructing a two-layer model and converting it into a single-layer model under preset KKT conditions, the optimized peak-valley prices and power generation plan are obtained. A scheduling plan is generated based on these plans and distributed to EV aggregators within the cluster. These aggregators then control the charging time of EVs according to the scheduling plan, thereby achieving the goal of accurately aggregating EVs and ensuring their full participation in grid scheduling. Attached Figure Description

[0062] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Figure 1 This is a schematic flowchart of an electric vehicle charging and discharging power control method provided in a certain embodiment of this application;

[0064] Figure 2 This is a schematic diagram of the structure of an electric vehicle charging and discharging power control device provided in a certain embodiment of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0067] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0068] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0069] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0070] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0071] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0072] See Figure 1 This is a flowchart illustrating a method for controlling the charging and discharging power of an electric vehicle according to an embodiment of the present invention, comprising:

[0073] S1. Determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster;

[0074] In a preferred embodiment, the process of determining the aggregated feasible region corresponding to the current electric vehicle cluster includes:

[0075] Select the current electric vehicle cluster from several electric vehicle clusters;

[0076] Based on the basic data of each electric vehicle in the current electric vehicle cluster, under the constraints of energy and power boundaries, a corresponding polyhedron is constructed to characterize the feasible region.

[0077] Based on the polyhedron of each electric vehicle in the current electric vehicle cluster, a polyhedron prototype for characterizing the feasible region of the current electric vehicle cluster is obtained.

[0078] Based on the polyhedron prototype and the plurality of polyhedra, the homogeneous polyhedron corresponding to each electric vehicle in the current cluster for characterizing the feasible region is calculated.

[0079] Based on all the obtained homogeneous polyhedra, and under the condition of satisfying the preset feasible region constraints, the aggregated polyhedra used to characterize the aggregated feasible region of the current electric vehicle cluster are obtained.

[0080] Indicatively, the feasible region of an electric vehicle can be modeled by its energy and power boundary constraints, which specify the feasible set of all possible power trajectories.

[0081] Specifically, firstly, based on the basic data of each electric vehicle in the current electric vehicle cluster, under the constraints of energy and power boundaries, corresponding polyhedra for characterizing feasible regions are constructed respectively;

[0082] In a preferred embodiment, the energy and power boundary constraints include:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] in, It is a battery exist The energy of a moment; It is a battery exist The energy of a moment; yes Time and The time difference between moments; It is the charging and discharging power; It refers to charge / discharge efficiency; This refers to the moment when the charging head is inserted and removed; This refers to the maximum charging and discharging power; It is between and At some point in the middle; These are the maximum and minimum battery capacities; Is the battery i in The upper and lower limits of energy at any given moment; yes The target battery energy at which charging stops; It is a battery exist The target battery energy at the start of charging;

[0089] In a preferred embodiment, the step of constructing corresponding polyhedra to characterize feasible regions based on the basic data of each electric vehicle in the current electric vehicle cluster, under energy and power boundary constraints, includes:

[0090] ;

[0091] ;

[0092] ;

[0093] in, Let represent the polyhedron used to characterize the feasible region corresponding to the i-th electric vehicle; Represents a T-dimensional identity matrix; , It is a row vector that arranges the charging power of battery i at each time step; , It is a row vector that arranges the discharge power of battery i at each time step; It is a matrix that combines the charging and discharging power of battery i at all times; yes The coefficient matrix; and It is a lower triangular matrix, and its lower triangular elements are respectively and , yes Time and The time difference between moments; yes The upper limit, of which , It is all 1s 3D column vector, , , , ,and ;

[0094] In a preferred embodiment, the process of determining the electric vehicle cluster includes:

[0095] Obtain samples of each electric vehicle;

[0096] Repeat the cluster center calculation operation until each pre-selected cluster center no longer moves, and use each pre-selected cluster center that no longer moves as the final cluster center;

[0097] For each cluster center, the electric vehicles assigned to the current cluster center are divided into an electric vehicle cluster;

[0098] Several electric vehicle clusters were obtained;

[0099] Specifically, the cluster center calculation operation is as follows:

[0100] For each electric vehicle sample, based on the current pre-selected cluster centers, the distance between the current electric vehicle sample and each pre-selected cluster center is calculated, and the current electric vehicle sample is assigned to the nearest pre-selected cluster center; wherein, the pre-selected cluster centers for the first clustering are randomly selected from each electric vehicle sample;

[0101] After all electric vehicle samples have been assigned, for each pre-selected cluster center, the average value of all electric vehicle samples assigned to the current pre-selected cluster center is calculated. It is then determined whether the current pre-selected cluster center coincides with the average value. If so, the current pre-selected cluster center remains unchanged. If not, the current pre-selected cluster center is moved to the average value to form a new pre-selected cluster center.

[0102] Specifically, polygon-based aggregation methods require consistency across the feasible region dimension among different electric vehicles; therefore, achieving this consistency necessitates using the k-means algorithm to perform aggregation based on polygons. to Electric vehicles within this period are clustered to obtain various electric vehicle clusters;

[0103] Specifically, the k-means clustering process is as follows: First, randomly select... The first step involves using one electric vehicle sample as the initial cluster center, i.e., the pre-selected cluster centers. The second step involves calculating the distance between each electric vehicle sample and each pre-selected cluster center, and assigning each electric vehicle sample to the pre-selected cluster center closest to it. The third step involves moving the new cluster center to the average value of all samples in this cluster. The fourth step involves repeating the second and third steps until the pre-selected cluster centers no longer move, and determining the current pre-selected cluster centers as the final cluster centers. The fifth step involves grouping all electric vehicles assigned to the same cluster center into a cluster, and aggregating them to obtain various electric vehicle clusters.

[0104] Indicatively, after obtaining the polyhedra of each electric vehicle in the cluster, the polyhedron of the cluster is determined based on the polyhedron of each electric vehicle in the cluster; then, based on the polyhedron and the polyhedra, the homogeneous polyhedron corresponding to each electric vehicle in the current cluster for characterizing the feasible region is calculated.

[0105] Specifically, the polyhedral prototype within the cluster is:

[0106] ;

[0107] in, and By analyzing the polyhedra corresponding to all electric vehicles within the cluster and Determined by averaging. Charging power for all electric vehicles within the cluster The mean, Power generation for all electric vehicles within the cluster The mean, yes and The combination matrix, , express The dimension of a matrix;

[0108] In a preferred embodiment, the process of determining the aggregated polyhedron corresponding to the current electric vehicle cluster includes:

[0109] The aggregated polyhedron corresponding to the current electric vehicle cluster is calculated using the following formula:

[0110] ;

[0111] ;

[0112] in, Represents the polyhedral prototype within the cluster; and These represent all electric vehicles within the cluster. and The mean; It is the charging power of all electric vehicles within the cluster. The mean; It is the power generation capacity of all electric vehicles within the cluster. The mean; yes and The combination matrix, , express The dimension of a matrix; Represents the aggregated polyhedron of the current electric vehicle cluster; It represents the number of electric vehicles in the cluster; It is the scaling factor. It is the translation coefficient; and The optimal equals and ; , , They are The corresponding coefficient matrix, charge / discharge power matrix, and upper limit matrix;

[0113] The following is a detailed explanation of the aggregated polyhedron corresponding to the current electric vehicle cluster:

[0114] Indicatively, based on the polyhedron prototype and the plurality of polyhedra, a homogeneous polyhedron for characterizing a feasible region corresponding to each electric vehicle in the current cluster is calculated. Then, based on all the obtained homogeneous polyhedra, when the preset feasible region constraint is satisfied, an aggregated polyhedron for characterizing an aggregated feasible region of the current electric vehicle cluster is obtained.

[0115] Specifically, through Scaling and translation to approximate the polyhedrons of each electric vehicle within the cluster This generates a homogeneous polyhedron. .if Aggregated polyhedra within the cluster The internal approximation can be obtained by summing the translation and scaling factors:

[0116] ;

[0117] in, It refers to the number of electric vehicles within the cluster. It is the scaling factor. It is the translation coefficient;

[0118] In a preferred embodiment, the preset feasible domain constraint includes:

[0119] ;

[0120] ;

[0121] ;

[0122] in, Let the objective function be denoted as , and the goal be to minimize it; the variables are . And satisfy ;

[0123] Indicative, if It is the optimal solution under the pre-defined feasible region constraints. and The optimal is and ; Let the objective function be denoted as , and the goal is to minimize it. The variables are . And satisfy , and It is the feasible region of the linear programming problem, i.e., the constraints that need to be satisfied;

[0124] Specifically, in obtaining the optimal and after, It is expressed as follows:

[0125] ;

[0126] , , They are The corresponding coefficient matrix, charge / discharge power matrix, and upper limit matrix.

[0127] S2. Based on the basic data of each electric vehicle in the cluster, construct the upper-level optimization model of the two-layer optimization model with the goal of minimizing the total cost; and construct the following constraints based on the basic data: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints.

[0128] In a preferred embodiment, the upper-level optimization model of the two-level optimization model includes:

[0129] ;

[0130] in, It refers to energy prices in the wholesale electricity market; Energy purchased from the wholesale electricity market; It is the number of aggregators; It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It is the total number of nodes in the circuit; This refers to the reactive power of distributed generators (DG). It is the secondary / primary / zero-order coefficient of power generation cost;

[0131] Schematic representation of the objective function of the higher-level optimization model This includes the cost of purchasing energy from the wholesale market, the cost of generating electricity using distributed generators, and the cost of purchasing and storing energy from aggregators; the upper-level model for distribution system operators is as follows:

[0132]

[0133] in, It refers to energy prices in the wholesale electricity market. The energy was purchased from the wholesale electricity market. It is the number of aggregators. It is the price of energy purchased from the aggregator. / These are the discharge power and charging power of the k-th electric vehicle at time t. It is the total number of nodes in the circuit. The reactive power of distributed generators (DG) It is the coefficient for secondary / primary / zero-order power generation costs.

[0134] Specifically, the active and reactive power constraints of the branches, the active and reactive power constraints of the nodes, the upper and lower limits constraints of the node voltage and line active and reactive power, and the upper and lower limits constraints of the output active and reactive power of the distributed generators are as follows:

[0135] Branch line active and reactive constraints: ;

[0136] ;

[0137] Active and reactive power constraints of nodes: ;

[0138] ;

[0139] ;

[0140] ;

[0141] Node voltage and upper and lower limits of active and reactive power on lines:

[0142] ;

[0143] Upper and lower limits constraints on active and reactive power output of distributed generators:

[0144] ;

[0145] in, And i=1 represents the active and reactive power entering node 1 of the electricity wholesale market; It refers to the active and reactive power of distributed generators (DG); Let t represent the active and reactive power transmitted between node i and node j. , , It is a branch reactor. It is the branch resistance; These are the voltage magnitude and voltage phase angle of node i / j at time t; It refers to the net active and reactive power injected into node i at time t, where i=1 refers to the node that is connected to the upper-level power grid and enters the electricity wholesale market; It is the active power generated by wind turbines (WT) and photovoltaics; It consists of active and reactive loads; These are the lower and upper limits of the node voltage amplitude; These are the lower and upper limits of the active power transmitted through the line; These are the lower and upper limits of reactive power transmitted through the line; These are the lower and upper limits of the active power generated by distributed generators; These are the lower and upper limits of the reactive power generated by distributed generators;

[0146] Specifically, the opportunity constraints of wind turbines and photovoltaic power, the price constraint at time t, the peak / flat / valley state constraints, and the peak / flat / valley time length constraints are as follows;

[0147] Opportunity Constraints of Wind Turbines and Solar Power ;

[0148] ;

[0149] Price constraint at time t ;

[0150] Peak / flat / valley state constraints ;

[0151] Peak / flat / valley time length constraints ;

[0152] The opportunity constraints for wind turbines and photovoltaic power represent the probability of their shortage, respectively. and The peak / flat / valley state constraint means that only one state is 1 at any given time. This refers to the predicted power output of photovoltaic and wind turbines; These are peak / flat / valley prices; It is a 0-1 variable that indicates the peak / flat / valley status; / / It refers to the duration of peaks, plateaus, and valleys;

[0153] Specifically, the wind turbines and photovoltaic power lack the opportunity constraints for explicit expressions, making the model unsolvable. Therefore, it is represented by the following compact form:

[0154] ;

[0155] in It is a decision variable. This is a predicted value.

[0156] By using the one-sided Vysochanskij-Petunin inequality, using The current information can be converted into the following linear form:

[0157] ;

[0158] in, and yes The average value and deviation.

[0159] S3. Based on the basic data of each electric vehicle in the cluster, construct the lower-level optimization model of the two-layer optimization model with the goal of minimizing the total charging cost of the aggregator; and construct the aggregation feasible region constraint based on the aggregation polyhedron.

[0160] In a preferred embodiment, the lower-level optimization model of the two-level optimization model includes:

[0161] ;

[0162] in, It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It is the number of aggregators;

[0163] Schematic representation of the objective function of the lower-level optimization model Total charging cost including AGG, designed to determine optimal charging / discharging power based on peak / valley prices published by DSO:

[0164] ;

[0165] ;

[0166] in, It is the number of aggregators. This represents the aggregate feasible region constraint.

[0167] S4. Under the condition of satisfying the preset KKT conditions, the two-layer optimization model is converted into a single-layer optimization model;

[0168] Indicatively, based on the KKT conditions and the strong duality theorem, the above two-layer optimization model is transformed into a single-layer optimization model;

[0169] Specifically, the Lagrangian function of the lower-level optimization model of the two-level optimization model is expressed as follows:

[0170] ;

[0171] in, and For the dual variable of the aggregate feasible region constraint;

[0172] When the preset KKT conditions are met, the objective function of the lower-level optimization model of the two-level optimization model achieves its optimal value. Can be converted into dual variables take The cost of purchasing energy from the aggregator is replaced in the objective function of the lower-level optimization model of the two-level optimization model, thereby converting the two-level optimization model into a single-level optimization model and simplifying the calculation.

[0173] Specifically, the preset KKT conditions are as follows:

[0174] ;

[0175] ;

[0176] ;

[0177] in, It is a binary variable, which is the complementary relaxation condition after transformation using the Big M method, where M is a large number;

[0178] S5. Under the constraints of active and reactive power of the branch, active and reactive power of the node, upper and lower limits of active and reactive power of the node voltage and line, upper and lower limits of active and reactive power output of the distributed generator, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, the single-layer model is solved to obtain the optimized peak-valley price and power generation plan; wherein, the power generation plan includes the power wholesale market energy power generation plan, the distributed generator power generation plan, and the wind turbine and photovoltaic power generation plan.

[0179] Specifically, the single-layer optimization model is as follows:

[0180] ;

[0181] Specifically, after solving the single-layer optimization model, the optimized peak-valley price and power generation plan will be obtained; wherein, the power generation plan includes the power wholesale market energy power generation plan, the distributed generator power generation plan, and the wind turbine and photovoltaic power generation plan.

[0182] S6. Based on the optimized peak-valley price and the power generation plan, generate a scheduling plan and send the scheduling plan to the electric vehicle aggregator in the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

[0183] Specifically, a scheduling plan is generated based on the optimized peak-valley price and the power generation plan, and the scheduling plan is distributed to the electric vehicle aggregator in the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

[0184] Specifically, the aggregator controls the charging and discharging behavior of electric vehicles according to the charging and discharging power specified in the scheduling plan. It can send corresponding instructions to each electric vehicle through the communication protocol with the electric vehicle to control its charging and discharging power.

[0185] Please see Figure 2 This application provides an electric vehicle charging and discharging power control device according to a certain embodiment, which includes: a polyhedron determination module, an upper-level optimization model and constraint determination module, a lower-level optimization model and constraint determination module, a model conversion module, a calculation module, and a scheduling module;

[0186] The aggregation polyhedron determination module is used to determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster;

[0187] The upper-level optimization model and constraint determination module are used to construct the upper-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the goal of minimizing the total cost; and to construct the following constraints based on the basic data: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints.

[0188] The lower-level optimization model and constraint determination module is used to construct the lower-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the goal of minimizing the total charging cost of the aggregator; and to construct the aggregation feasible region constraint based on the aggregation polyhedron.

[0189] The model conversion module is used to convert the two-layer optimization model into a single-layer optimization model when the preset KKT conditions are met.

[0190] The calculation module is used to solve the single-layer model under the following constraints: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, to obtain optimized peak and valley prices and power generation plans; wherein, the power generation plans include power wholesale market energy generation plans, distributed generator power generation plans, and wind turbine and photovoltaic power generation plans.

[0191] The scheduling module is used to generate a scheduling plan based on the optimized peak-valley price and the power generation plan, and to distribute the scheduling plan to the electric vehicle aggregator in the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

[0192] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling the charging and discharging power of an electric vehicle, characterized in that, include: Determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster; Based on the basic data of each electric vehicle in the cluster, a higher-level optimization model of the two-level optimization model is constructed with the goal of minimizing the total cost. Based on the basic data, constraints on active and reactive power of branches, active and reactive power of nodes, upper and lower limits of active and reactive power of nodes and lines, upper and lower limits of active and reactive power output of distributed generators, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints are constructed. Based on the basic data of each electric vehicle in the cluster, and with the goal of minimizing the total charging cost of the aggregator, a lower-level optimization model of the two-level optimization model is constructed. And based on the aggregated polyhedron, an aggregated feasible region constraint is constructed; wherein, the lower-level optimization model of the two-layer optimization model includes: ； ； in, It is the price of energy purchased from the aggregator; and These are the discharge power and charging power of the k-th electric vehicle at time t, respectively. It is the number of aggregators; This represents the aggregate feasible region constraint; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding coefficient matrix; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding charge / discharge power matrix; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding upper bound matrix; Under the condition of satisfying the preset KKT conditions, the two-layer optimization model is converted into a single-layer optimization model; wherein, the Lagrangian function of the lower-layer optimization model of the two-layer optimization model is expressed as follows: ； in, and For the dual variable of the aggregate feasible region constraint; The step of converting the two-layer optimization model into a single-layer optimization model under the condition of satisfying the preset KKT conditions includes: When the preset KKT conditions are met, the objective function of the lower-level optimization model of the two-level optimization model achieves its optimal value. Can be converted into dual variables Multiplying the upper bound matrix The cost of purchasing energy from the aggregator is replaced in the objective function of the lower-level optimization model of the two-level optimization model, thereby converting the two-level optimization model into a single-level optimization model. Under the constraints of active and reactive power of the branches, active and reactive power of the nodes, upper and lower limits of active and reactive power of the nodes and lines, upper and lower limits of active and reactive power output of distributed generators, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, the single-layer optimization model is solved to obtain the optimized peak-valley prices and power generation plans; wherein, the power generation plans include power wholesale market energy generation plans, distributed generator power generation plans, and wind turbine and photovoltaic power generation plans. Based on the optimized peak-valley price and the power generation plan, a scheduling plan is generated and distributed to the electric vehicle aggregator within the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

2. The electric vehicle charging and discharging power control method as described in claim 1, characterized in that, The process of determining the electric vehicle cluster includes: Obtain samples of each electric vehicle; Repeat the cluster center calculation operation until each pre-selected cluster center no longer moves, and use each pre-selected cluster center that no longer moves as the final cluster center; For each cluster center, the electric vehicles assigned to the current cluster center are divided into an electric vehicle cluster; Several electric vehicle clusters were obtained; Specifically, the cluster center calculation operation is as follows: For each electric vehicle sample, based on the current pre-selected cluster centers, the distance between the current electric vehicle sample and each pre-selected cluster center is calculated, and the current electric vehicle sample is assigned to the nearest pre-selected cluster center; wherein, the pre-selected cluster centers for the first clustering are randomly selected from each electric vehicle sample; After all electric vehicle samples have been assigned, for each pre-selected cluster center, the average value of all electric vehicle samples assigned to the current pre-selected cluster center is calculated. It is then determined whether the current pre-selected cluster center coincides with the average value. If so, the current pre-selected cluster center remains unchanged. If not, the current pre-selected cluster center is moved to the average value to form a new pre-selected cluster center.

3. The electric vehicle charging and discharging power control method as described in claim 2, characterized in that, The process of determining the aggregated feasible region corresponding to the current electric vehicle cluster includes: Select the current electric vehicle cluster from several electric vehicle clusters; Based on the basic data of each electric vehicle in the current electric vehicle cluster, under the constraints of energy and power boundaries, a corresponding polyhedron is constructed to characterize the feasible region. Based on the polyhedron of each electric vehicle in the current electric vehicle cluster, a polyhedron prototype for characterizing the feasible region of the current electric vehicle cluster is obtained. Based on the polyhedron prototype and the plurality of polyhedra, the homogeneous polyhedron corresponding to each electric vehicle in the current cluster for characterizing the feasible region is calculated. Based on all the obtained homogeneous polyhedra, and under the condition of satisfying the preset feasible region constraints, the aggregated polyhedra used to characterize the aggregated feasible region of the current electric vehicle cluster are obtained.

4. The electric vehicle charging and discharging power control method as described in claim 3, characterized in that, The energy and power boundary constraints include: ； ； ； ； ； in, It is a battery exist The energy of a moment; It is a battery exist The energy of a moment; yes Time and The time difference between moments; It is the charging and discharging power; It refers to charge / discharge efficiency; This refers to the moment when the charging head is inserted and removed; This refers to the maximum charging and discharging power; It is between and At some point in the middle; These are the maximum and minimum battery capacities; Is the battery i in The upper and lower limits of energy at any given moment; yes The target battery energy at which charging stops; It is a battery exist The target battery energy at the start of charging.

5. The electric vehicle charging and discharging power control method as described in claim 4, characterized in that, The step involves constructing corresponding polyhedra to characterize feasible regions based on the basic data of each electric vehicle within the current electric vehicle cluster, under energy and power boundary constraints. This includes: ； ； ； in, Let represent the polyhedron used to characterize the feasible region corresponding to the i-th electric vehicle; Represents a T-dimensional identity matrix; , It is a row vector that arranges the charging power of battery i at each time step; , It is a row vector that arranges the discharge power of battery i at each time step; It is a matrix that combines the charging and discharging power of battery i at all times; yes The coefficient matrix; and It is a lower triangular matrix, and its lower triangular elements are respectively and , yes Time and The time difference between moments; yes The upper limit, of which , It is all 1s 3D column vector, , , , ,and .

6. The electric vehicle charging and discharging power control method as described in claim 5, characterized in that, The preset feasible region constraints include: ； ； ； in, Let the objective function be denoted as , and the goal be to minimize it; the variables are . And satisfy .

7. The electric vehicle charging and discharging power control method as described in claim 6, characterized in that, The process of determining the aggregated polyhedron corresponding to the current electric vehicle cluster includes: The aggregated polyhedron corresponding to the current electric vehicle cluster is calculated using the following formula: ； ； in, Represents the polyhedral prototype within the cluster; and These represent all electric vehicles within the cluster. and The mean; It is the charging power of all electric vehicles within the cluster. The mean; It is the power generation capacity of all electric vehicles within the cluster. The mean; yes and The combination matrix, , express The dimension of a matrix; Represents the aggregated polyhedron of the current electric vehicle cluster; It represents the number of electric vehicles in the cluster; It is the scaling factor. It is the translation coefficient; and The optimal equals and ; , , They are The corresponding coefficient matrix, charge / discharge power matrix, and upper limit matrix.

8. The electric vehicle charging and discharging power control method as described in claim 7, characterized in that, The upper-level optimization model of the two-level optimization model includes: ； in, It refers to energy prices in the wholesale electricity market; Energy purchased from the wholesale electricity market; It is the number of aggregators; It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It is the total number of nodes in the circuit; This refers to the reactive power of distributed generators (DG). It is the secondary / primary / zero-time coefficient for power generation cost.

9. The electric vehicle charging and discharging power control method as described in claim 8, characterized in that, The lower-level optimization model of the two-level optimization model includes: ； in, It is the price of energy purchased from the aggregator; / These are the discharge power and charging power of the k-th electric vehicle at time t; It represents the number of aggregators.

10. A charging and discharging power control device for electric vehicles, characterized in that, include: The system comprises a polyhedron determination module, an upper-level optimization model and constraint determination module, a lower-level optimization model and constraint determination module, a model conversion module, a calculation module, and a scheduling module. The aggregation polyhedron determination module is used to determine the aggregation polyhedron used to characterize the feasible aggregation region of the current electric vehicle cluster; The upper-level optimization model and constraint determination module are used to construct the upper-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the goal of minimizing the total cost; and to construct the following constraints based on the basic data: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, and peak / flat / valley time length constraints. The lower-level optimization model and constraint determination module are used to construct the lower-level optimization model of the two-level optimization model based on the basic data of each electric vehicle in the cluster, with the aim of minimizing the total charging cost of the aggregator. And based on the aggregated polyhedron, an aggregated feasible region constraint is constructed; wherein, the lower-level optimization model of the two-layer optimization model includes: ； ； in, It is the price of energy purchased from the aggregator; and These are the discharge power and charging power of the k-th electric vehicle at time t, respectively. It is the number of aggregators; This represents the aggregate feasible region constraint; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding coefficient matrix; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding charge / discharge power matrix; Represents the aggregate polyhedron of the current electric vehicle cluster The corresponding upper bound matrix; The model conversion module is used to convert the two-layer optimization model into a single-layer optimization model when the preset KKT conditions are met. The Lagrangian function of the lower-level optimization model in the two-level optimization model is expressed as follows: ； in, and For the dual variable of the aggregate feasible region constraint; The step of converting the two-layer optimization model into a single-layer optimization model under the condition of satisfying the preset KKT conditions includes: When the preset KKT conditions are met, the objective function of the lower-level optimization model of the two-level optimization model achieves its optimal value. Can be converted into dual variables Multiplying the upper bound matrix The cost of purchasing energy from the aggregator is replaced in the objective function of the lower-level optimization model of the two-level optimization model, thereby converting the two-level optimization model into a single-level optimization model. The calculation module is used to solve the single-layer optimization model under the following constraints: active and reactive power constraints of branches, active and reactive power constraints of nodes, upper and lower limits of active and reactive power constraints of node voltage and lines, upper and lower limits of active and reactive power constraints of distributed generator output, opportunity constraints of wind turbines and photovoltaic power, price constraints at time t, peak / flat / valley state constraints, peak / flat / valley time length constraints, aggregated feasible region matrix constraints, and the KKT conditions, to obtain the optimized peak-valley prices and power generation plans; wherein, the power generation plans include power wholesale market energy power generation plans, distributed generator power generation plans, and wind turbine and photovoltaic power generation plans. The scheduling module is used to generate a scheduling plan based on the optimized peak-valley price and the power generation plan, and to distribute the scheduling plan to the electric vehicle aggregator in the cluster, so that the electric vehicle aggregator controls the charging and discharging power of the electric vehicles according to the scheduling plan.

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