Charging regulation method, computer device, readable storage medium and program product

Abstract

The application relates to a charging regulation method, computer equipment, a readable storage medium and a program product. The method comprises the following steps: obtaining charging parameter information of a plurality of electric vehicles accessing a power distribution node in a target power distribution network; constructing a power feasible region of each electric vehicle according to the charging parameter information of the electric vehicle; for each power distribution node, aggregating the power feasible regions of the electric vehicles accessing the power distribution node to obtain a cluster power feasible region of the power distribution node; determining a cluster allocation power based on the cluster power feasible regions of the power distribution nodes, the cluster allocation power being a power when a target load function of the target power distribution network reaches a minimum value, and the target load function being determined based on a power grid peak-valley difference and a power grid load fluctuation rate of the target power distribution network; and controlling the electric vehicles in the power distribution node to charge based on the cluster allocation power. The method can reduce the load pressure of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution regulation, in particular to a charging regulation method, computer equipment, a readable storage medium and a program product. BACKGROUND

[0002] With the rapid increase of the number of electric vehicles (EV), the large-scale and random charging behavior of the electric vehicles poses a serious challenge to the safe and economic operation of the power distribution network.

[0003] Existing charging regulation strategies mainly focus on the optimization of a single vehicle or a single charging station, such as regulating the charging power of a single vehicle or implementing time-of-use pricing guidance at a charging station, in order to reduce the load of the power distribution network.

[0004] However, in the absence of effective optimization of the charging behavior of a large number of electric vehicles in a region, the disordered charging behavior of the electric vehicles may still add to the load peak, increasing the load of the power distribution network. SUMMARY

[0005] Therefore, it is necessary to provide a charging regulation method, computer equipment, readable storage medium and program product capable of reducing the load of the power distribution network.

[0006] In a first aspect, the present application provides a charging regulation method, comprising:

[0007] obtaining charging parameter information of a plurality of electric vehicles accessing a power distribution node in a target power distribution network, and constructing a power feasible region of each electric vehicle based on the charging parameter information of the electric vehicle;

[0008] for each power distribution node, aggregating the power feasible regions of the electric vehicles accessing the power distribution node to obtain a cluster power feasible region of the power distribution node;

[0009] determining a cluster allocation power that makes a target load function of the target power distribution network reach a minimum value based on the cluster power feasible regions of the power distribution nodes, the target load function being determined based on a peak-valley difference and a load fluctuation rate of the target power distribution network;

[0010] controlling the electric vehicles in the power distribution nodes to charge based on the cluster allocation power.

[0011] In a second aspect, the present application further provides a charging regulation device, comprising:

[0012] a construction module configured to obtain charging parameter information of a plurality of electric vehicles accessing a power distribution node in a target power distribution network, and construct a power feasible region of each electric vehicle based on the charging parameter information of the electric vehicle;

[0013] aggregating, for each power distribution node, power feasible regions of each electric vehicle accessing the power distribution node to obtain a cluster power feasible region of the power distribution node;

[0014] determining, based on the cluster power feasible region of each power distribution node, a cluster dispatch power at which a target load function of the target power distribution network reaches a minimum value, the target load function being determined based on a peak-valley difference and a load fluctuation rate of the target power distribution network;

[0015] controlling, based on the cluster dispatch power, each electric vehicle in the power distribution node to charge.

[0016] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the charging regulation method when executing the computer program.

[0017] In a fourth aspect, the present application also provides a computer readable storage medium, storing a computer program, and the computer program implementing the steps of the charging regulation method when executed by a processor.

[0018] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, and the computer program implementing the steps of the charging regulation method when executed by a processor.

[0019] The charging regulation method, the computer device, the readable storage medium and the program product, by constructing a power feasible region of a single electric vehicle, aggregating power feasible regions of each electric vehicle in each power distribution node in the target power distribution network to obtain a cluster power feasible region corresponding to each power distribution node, and then optimizing a cluster dispatch power at which a target load function of the target power distribution network reaches a minimum value based on the cluster power feasible region of each power distribution node, and controlling each electric vehicle in the power distribution node to charge based on the cluster dispatch power, the above scheme regards electric vehicles under the same power distribution node as a cluster, converts a high-dimensional optimization problem of a large number of electric vehicles into a solvable cluster optimization problem, makes the charging optimization of a large number of electric vehicles feasible, effectively reduces the optimization difficulty and the optimization efficiency, and realizes effective regulation of disordered charging behaviors of large-scale electric vehicles, and reduces the load pressure on the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0021] Figure 1 An application environment diagram of the charging regulation method in an embodiment;

[0022] Figure 2 A flowchart of the charging regulation method in an embodiment;

[0023] Figure 3 A flowchart of the step of constructing the power feasible region of each electric vehicle in an embodiment;

[0024] Figure 4 A diagram of the power feasible region in an embodiment;

[0025] Figure 5 A diagram of the scenario of calculating the Minkowski sum in an embodiment;

[0026] Figure 6 A flowchart of the charging regulation method in another embodiment;

[0027] Figure 7 A flowchart of the charging regulation method in yet another embodiment;

[0028] Figure 8 A structural block diagram of the charging regulation device in an embodiment;

[0029] Figure 9 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0030] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0031] The charging regulation method provided by the embodiments of the present application can be applied in an application environment as shown in Figure 1 For example, the charging regulation method is executed by a server, and the electric vehicle 102 communicates with the server 104 through a network. A data storage system can store data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on a cloud or other network server. For example, the data storage system can be used to store the charging parameter information of the plurality of electric vehicles accessing the target power distribution network. There are a plurality of power distribution nodes in the target power distribution network, and each power distribution node can access a plurality of electric vehicles 102.

[0032] The electric vehicles 102 can be, but are not limited to, various plug-in hybrid vehicles, extended-range hybrid vehicles, pure electric vehicles, and the like. The server 104 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0033] In an exemplary embodiment, as shown in Figure 2 A charging regulation method is provided, which is exemplified by being applied to a terminal. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction of the terminal and the server. The method includes the following steps 202 to 208. Among them:

[0034] Step 202, obtaining charging parameter information of a plurality of electric vehicles accessing a power distribution node in a target power distribution network, and constructing a power feasible region of each electric vehicle according to the charging parameter information of each electric vehicle.

[0035] The target power distribution network is a power network expected to be regulated for charging, and the power distribution node is a node in the target power distribution network for power transformation, distribution and control. The power distribution node can be a specific physical device such as a substation or a distribution transformer, or an abstract topological node (such as a collection of multiple physical devices in a certain area). The electric vehicle is a vehicle driven by electric energy, such as a plug-in hybrid vehicle, an extended-range hybrid vehicle, a pure electric vehicle, a pure electric truck, etc. The charging parameter information is information describing the charging condition parameters of the electric vehicle, such as the maximum state of charge, the desired state of charge, the network access state of charge, the minimum state of charge, the network access time, the desired network exit time, the maximum charging power, etc. The power feasible region of the electric vehicle is used to describe the charging power flexibility (i.e. the adjustable interval of the charging power) of the electric vehicle, which can be described in the form of a function, a chart, etc.

[0036] Exemplarily, the terminal can read the charging parameter information of the plurality of electric vehicles accessing the power distribution node of the target power distribution network through accessing the server, or directly read the charging parameter information of the electric vehicle which has established a communication connection with the terminal. Further, according to the charging parameter information of each electric vehicle, the power feasible region of each electric vehicle is constructed. Exemplarily, the embodiment can determine the state of charge interval constraint based on the state of charge parameter, determine the charging time constraint based on the network access time and the expected network exit time, and determine the slope constraint of the state of charge and the charging time based on the maximum charging power, to obtain the power feasible region of the electric vehicle. The power feasible region is constructed based on the state of charge interval constraint, the charging time constraint and the slope constraint, which can be described in a functional form or in a chart form, such as an area chart, a line chart, etc. In an example, the power feasible region in the chart form can take the charging time as the horizontal coordinate and the state of charge as the vertical coordinate, the lower limit value of the charging time constraint is the network access time, the upper limit value of the charging time constraint is the expected network exit time, the lower limit value of the state of charge interval constraint is the minimum state of charge, the upper limit value of the state of charge interval constraint is the maximum state of charge, and the slope constraint is the maximum charging power. A first intersection point of the network access time and the state of charge as the network access state of charge in the coordinate system is determined; a second intersection point of the maximum charging power as the slope, passing through the first node and the state of charge as the maximum state of charge is determined; a third intersection point of the expected network exit time and the state of charge as the maximum state of charge in the coordinate system is determined; a fourth intersection point of the expected network exit time and the state of charge as the expected state of charge in the coordinate system is determined; a fifth intersection point of the maximum charging power as the slope, passing through the fourth node and the state of charge as the network access state of charge is determined; and the first intersection point, the second intersection point, the third intersection point, the fourth intersection point and the fifth intersection point are connected to form a polygonal region, which is the power feasible region of the electric vehicle.

[0037] In another example, the power feasible region in the functional form can be expressed as follows:

[0038]

[0039] wherein, Ω DCEV is the power feasible region of the electric vehicle, E N is the rated capacity of the battery of the electric vehicle, y is the state of charge, P c max is the maximum charging power, x is the charging time, SOC in is the network access state of charge, t in is the network access time, SOC ep is the expected state of charge, t out is the expected network exit time, SOC max is the maximum state of charge.

[0040] At step 204, the power feasible region of each electric vehicle connected to the power distribution node is aggregated to obtain a cluster power feasible region of the power distribution node.

[0041] Since the charging behavior of a large number of electric vehicles is faced, if the data of each electric vehicle is directly aggregated to construct a global optimization model for solving, the high dimensionality of the data is easy to cause the curse of dimensionality, which leads to the requirement of computing resources being difficult to be met, and even cannot be solved. Therefore, in the embodiment, the electric vehicles connected to one power distribution node can be regarded as a cluster, and the terminal can aggregate the power feasible region of each electric vehicle connected to the power distribution node to obtain the cluster power feasible region of the power distribution node. The cluster power feasible region of the power distribution node is the aggregation result of the power feasible region of each electric vehicle connected to the power distribution node. Exemplarily, since the cluster power feasible region is essentially the exhaustive combination of the displacement of the power feasible region of each electric vehicle connected to the power distribution node, which is basically consistent with the definition of Minkowski sum, the Minkowski sum can be used to aggregate the power feasible region to obtain the cluster power feasible region. Of course, the embodiment can also train a related neural network model to obtain a target aggregation model that can realize the aggregation of the power feasible region. The input of the target aggregation model is the power feasible region of each electric vehicle, and the output is the cluster power feasible region of the power distribution node.

[0042] At step 206, based on the cluster power feasible region of each power distribution node, the cluster deployment power when the target load function of the target power distribution network reaches the minimum value is determined. The target load function is determined based on the power grid peak-valley difference and the power grid load fluctuation rate of the target power distribution network.

[0043] The target load function is determined based on the power grid peak-valley difference and the power grid load fluctuation rate of the target power distribution network, and is used to describe the load brought by the power grid peak-valley difference and the power grid load fluctuation rate to the target power distribution network. Exemplarily, the target load function can include a power grid peak-valley difference term and a power grid load fluctuation rate term. The power grid peak-valley difference term is used to describe the power grid peak-valley difference of the target power distribution network, and the power grid load fluctuation rate term is used to describe the power grid load fluctuation rate of the target power distribution network.

[0044] In this embodiment, the terminal can construct a target load function of the target power distribution network based on the cluster power feasible region of each power distribution node, and then solve the target load function to obtain the function value fluctuation interval of the target load function under the cluster power feasible region of each power distribution node (i.e. the load fluctuation of the target power distribution network under different combinations of cluster power values in the cluster power feasible region of each power distribution node). Then, based on the function value fluctuation interval, the cluster power value of each power distribution node when the target load function reaches the minimum value (i.e. representing the minimum load of the target power distribution network) is selected as the cluster allocation power. Further, since different power distribution nodes also have a certain basic load power, i.e. the load power of other devices in addition to the power demand of the connected electric vehicles. Therefore, the embodiment can also obtain the basic load power of each power distribution node without connecting electric vehicles, and then construct a target load function of the target power distribution network according to the cluster power feasible region and the basic load power. After solving the target load function, the function value fluctuation interval of the target load function under the cluster power feasible region of each power distribution node is obtained. Then, based on the function value fluctuation interval, the cluster power value of each power distribution node when the target load function reaches the minimum value is selected as the cluster allocation power.

[0045] In this embodiment, the cluster allocation power is determined based on the cluster power feasible region of each power distribution node, so that the target load function of the target power distribution network reaches the minimum value. Thus, the cluster power value of each power distribution node under the condition that the load of the target power distribution network is minimum is obtained as the cluster allocation power, i.e. when each power distribution node operates according to the corresponding cluster allocation power, the power demand of the cluster of electric vehicles connected to the power distribution node is met, and at the same time, the load of the target power distribution network is the lowest.

[0046] Step 208, based on the cluster allocation power, control each electric vehicle in the power distribution node to charge.

[0047] Since the cluster allocation power obtained by solving is shared by each electric vehicle in the power distribution node, the cluster allocation power can be decomposed into the target allocation power of each electric vehicle under the power distribution node, and then the target allocation power is used to control each electric vehicle in the power distribution node to charge, so that the charging power of each electric vehicle is adjusted to the target allocation power. It can be understood that the sum of the target allocation power of each electric vehicle under the power distribution node is the cluster allocation power of the power distribution node. As an example, when there is only one electric vehicle accessing the power distribution node, the cluster allocation power of the power distribution node can be determined as the target allocation power of the electric vehicle. As another example, when there are multiple electric vehicles accessing the power distribution node, the embodiment can determine the minimum expected power and the maximum expected power of the electric vehicle according to the power feasible region of the electric vehicle, and then determine the target allocation power of the electric vehicle between the minimum expected power and the maximum expected power (i.e. the target allocation power is greater than or equal to the minimum expected power and less than or equal to the maximum expected power), and the sum of the target allocation power of each electric vehicle under the power distribution node is the cluster allocation power of the power distribution node. The minimum expected power is the charging power at the current state of charge at the current time to achieve the expected state of charge at the expected off-grid time, and the maximum expected power is the charging power at the current state of charge at the current time to achieve the highest state of charge at the expected off-grid time. Further, in order to further optimize the power allocation effect, a target power function can be constructed based on the ratio between the target allocation power and the maximum expected power of the electric vehicle in the power distribution node, and the target allocation power of the electric vehicle is obtained by solving the target power function of the power distribution node when the maximum value is obtained. After the cluster allocation power is decomposed into each electric vehicle, the charging power of each electric vehicle under the power distribution node is maximized.

[0048] In the above charging regulation method, by constructing the power feasible region of a single electric vehicle, aggregating each power distribution node in the target power distribution network into the cluster power feasible region corresponding to each power distribution node, and then optimizing the cluster allocation power of the target power distribution network under the minimum target load function based on the cluster power feasible region of each power distribution node, and controlling each electric vehicle in the power distribution node to charge based on the cluster allocation power, the above scheme regards the electric vehicles under the same power distribution node as a cluster, and converts the high-dimensional optimization problem of a large number of electric vehicles into a solvable cluster optimization problem, so that the charging optimization of a large number of electric vehicles has a feasible basis, and the optimization difficulty and efficiency are effectively reduced, and the effective regulation of the disordered charging behavior of large-scale electric vehicles is realized, and the load pressure on the power distribution network is reduced.

[0049] In one exemplary embodiment, as shown in Figure 3 Fig. 2, the power feasible region of each electric vehicle is constructed according to the charging parameter information of each electric vehicle, including steps 302 to 304. Wherein:

[0050] Step 302: For each electric vehicle, extract the state of charge parameters, grid connection time, expected grid disconnection time, and maximum charging power from the electric vehicle's charging parameter information.

[0051] The state of charge (SOC) parameters include at least the SOC of the electric vehicle at the time of grid connection and the vehicle's maximum SOC (e.g., 100%, 99%, 95%). Of course, if the user expects the electric vehicle to be charged to a specific SOC (i.e., the desired SOC), the SOC parameters can also include the desired SOC (e.g., 85%, 80%, 75%).

[0052] Step 304: Determine the state of charge interval constraints based on the state of charge parameters, determine the charging time constraints based on the grid connection time and the expected grid disconnection time, and determine the slope constraints of the state of charge and charging time based on the maximum charging power to obtain the power feasible region of the electric vehicle.

[0053] This embodiment can determine the state of charge (POC) interval constraint based on the POC parameters, the charging time constraint based on the grid connection time and the desired grid disconnection time, and the slope constraint between the POC and charging time based on the maximum charging power, thus obtaining the power feasible region of the electric vehicle. The POC interval constraint, charging time constraint, and slope constraint can be described in functional form or graphical form, such as area plots or line graphs. Figure 4 As shown, the feasible power region is used as an area map, and the state of charge parameters include the grid-connected state of charge (SOC). in Desired State of Charge (SOC) ep and highest state of charge (SOC) max For example, in this embodiment, charging time can be used as the horizontal axis and state of charge as the vertical axis, with the lower limit of the charging time constraint being the grid connection time t. in The upper limit of the charging time constraint is the expected off-grid time t. out The lower limit of the state of charge interval constraint is the minimum state of charge (SOC) at grid connection. in The upper limit of the state-of-charge (SOC) range constraint is the lowest value of the highest SOC. max The slope constraint is the maximum charging power P. c max Determine the network entry time t in the coordinate system. in With the state of charge being the grid-connected state of charge (SOC) in The first intersection point a; with maximum charging power P c max The slope is the highest state of charge (SOC) at the first node a. max The second intersection point b; determine the desired offline time t in the coordinate system. out With the state of charge being the highest state of charge (SOC) max The third intersection point c; determine the desired offline time t in the coordinate system.out a fourth intersection d with state of charge SOC ep of a desired state of charge SOC c max a fifth intersection e with state of charge SOC in of a desired state of charge SOC Figure 4 t esd is an earliest end time of reaching the desired state of charge, t esmax is an earliest end time of reaching the highest state of charge, t emc is a latest charging start time of reaching the desired state of charge,

[0054] In this embodiment, for each electric vehicle, the state of charge parameter, the time of entering the grid, the desired time of leaving the grid and the maximum charging power are extracted from the charging parameter information of the electric vehicle, the state of charge interval constraint is determined based on the state of charge parameter, the charging time constraint is determined based on the time of entering the grid and the desired time of leaving the grid, and the slope constraint of the state of charge and the charging time is determined based on the maximum charging power, so as to obtain the power feasible region of the electric vehicle. Therefore, the power feasible region describing the charging power flexibility of the electric vehicle can be constructed through the charging parameter information.

[0055] In an exemplary embodiment, for each power distribution node, the power feasible regions of the electric vehicles connected to the power distribution node are aggregated to obtain the cluster power feasible region of the power distribution node, including:

[0056] The power feasible region of each electric vehicle connected to the power distribution node is determined.

[0057] The Minkowski sum of the power feasible regions of the electric vehicles connected to the power distribution node is determined as the cluster power feasible region of the power distribution node.

[0058] Since each electric vehicle is actually allocated with electric energy by the power distribution node it is connected to, this embodiment can regard the electric vehicles connected to one power distribution node as a cluster, and the terminal can aggregate the power feasible regions of the electric vehicles connected to each power distribution node to obtain the cluster power feasible region of the power distribution node. Since the cluster power feasible region is essentially an exhaustive combination of the power feasible regions of each electric vehicle connected to the power distribution node, this essence is basically consistent with the definition of the Minkowski sum, so the power feasible region can be aggregated in the manner of the Minkowski sum to obtain the cluster power feasible region. As an example, the Minkowski sums of the power feasible regions of 1-n electric vehicles connected to a power distribution node are sequentially calculated to obtain the cluster power feasible region of the cluster of 1-n electric vehicles connected to the power distribution node:

[0059]

[0060] Ω is the cluster power feasible region of the cluster of 1~n electric vehicles accessing the power distribution node, is the symbol for seeking Minkowski sum, Ω DCEV,1 is the power feasible region of the 1st electric vehicle, Ω DCEV,2 is the power feasible region of the 2nd electric vehicle, Ω DCEV,n is the power feasible region of the nth electric vehicle. As shown in Figure 5 Figure 5 is a schematic diagram of a scenario for seeking Minkowski sum in an embodiment. Taking the power feasible region of 2 electric vehicles accessing a certain power distribution node as an example, the power feasible region of the 1st electric vehicle is P1, and the power feasible region of the 2nd electric vehicle is P2. The Minkowski sum of the power feasible region P1 and the power feasible region P2 can be the union of the region swept by the power feasible region P2 along the marginal continuous motion of the power feasible region P1 for one round and the power feasible region P1 itself.

[0061] In this embodiment, the power feasible region of the electric vehicle accessing each power distribution node is determined, and the Minkowski sum of the power feasible region of the electric vehicle accessing the power distribution node is determined as the cluster power feasible region of the power distribution node. The Minkowski sum is basically consistent with the mathematical definition of the cluster power feasible region, and the power feasible regions of the electric vehicles are aggregated, so that the accuracy of the cluster power feasible region of the power distribution node is ensured.

[0062] In an exemplary embodiment, as shown in Figure 6 , the cluster dispatching power that makes the target load function of the target power distribution network reach the minimum value is determined based on the cluster power feasible region of each power distribution node, which includes steps 402 to 406. Among them:

[0063] Step 402, obtaining the basic load power of each power distribution node without accessing the electric vehicle.

[0064] Among them, the basic load power is the load power of the power distribution node without accessing the electric vehicle.

[0065] This embodiment can read the sum of the operating power of other devices of the power distribution node except the electric vehicle as the basic load power of the power distribution node without accessing the electric vehicle.

[0066] Step 404, constructing the target load function of the target power distribution network according to the cluster power feasible region and the basic load power.

[0067] ​The load of the power distribution network can be evaluated by peak-valley value and load fluctuation rate, and thus the target load function can include a power grid peak-valley difference term and a power grid load fluctuation rate term. In the presence of electric vehicles, the total load power of the power distribution node can be obtained by adding the basic load power to the cluster power feasible region based on the time axis. For example, the calculation formula of the total load power is as follows:

[0068]

[0069] wherein, is the total load power of the jth power distribution node at time t, is the cluster power value of the jth power distribution node at time t under the cluster power feasible region, is the basic load power of the jth power distribution node at time t.

[0070] Then, the maximum and minimum values of the total load power when different cluster power values are selected for the power distribution node in the presence of electric vehicles under the cluster power feasible region are obtained, and the difference between the maximum and minimum values of the total load power is taken as the node peak-valley difference of the power distribution node. The node peak-valley differences of the power distribution nodes in the presence of electric vehicles are summed to obtain the power grid peak-valley difference term. For example, the formula of the power grid peak-valley difference term is as follows:

[0071]

[0072] wherein, Y1 is the power grid peak-valley difference term, is the total load power of the jth power distribution node at time t, and the jth power distribution node is a power distribution node in the presence of electric vehicles, and r represents the number of power distribution nodes in the presence of electric vehicles.

[0073] Then, the average load power can be obtained by calculating the average value of the total load power within a predetermined statistical time length (such as 12 hours, 24 hours, etc.).

[0074]

[0075] wherein, is the average load power of the jth power distribution node within the predetermined statistical time length T, is the total load power of the jth power distribution node at time t.

[0076] Then, the node load fluctuation rate of the power distribution node can be determined according to the average load power and the total load power, and the node load fluctuation rates of the power distribution nodes in the presence of electric vehicles are summed to obtain the power grid load fluctuation rate term.

[0077]

[0078]

[0079] wherein, is the power standard deviation of the jth power distribution node at time t, Y2 is the grid load fluctuation rate term, and r represents the number of power distribution nodes with electric vehicles connected.

[0080] Since the optimization objectives include the grid peak-valley difference term and the grid load fluctuation rate term, in order to improve the optimization efficiency, the grid peak-valley difference term and the grid load fluctuation rate term can be normalized to obtain the target load function. Exemplarily, the formula of the target load function is as follows:

[0081]

[0082] wherein, Y 12 is the target function, Y1 is the grid peak-valley difference term, λ1 is the weight coefficient of the grid peak-valley difference term, Y 1max is the grid peak-valley difference value of the target power distribution network without electric vehicles connected, Y2 is the grid load fluctuation rate, λ2 is the weight coefficient of the grid load fluctuation rate, Y 2max is the grid load fluctuation rate of the target power distribution network without electric vehicles connected, λ1≥0 and λ2≥0, and λ1+λ2=1.

[0083] Step 406, based on the target load function, the cluster power value of the target power distribution network when the target load function reaches the minimum value is determined as the cluster deployment power.

[0084] In this embodiment, the function value fluctuation interval of the target load function in the cluster power feasible region of each power distribution node (i.e. the load fluctuation of the target power distribution network under different combinations of cluster power values in the cluster power feasible region of each power distribution node) can be obtained by solving the target load function. Then, based on the function value fluctuation interval, the cluster power value of each power distribution node when the target load function reaches the minimum value (i.e. representing the minimum load of the target power distribution network) is selected as the cluster deployment power.

[0085] In addition, it can be understood that the constraint condition of the cluster deployment power (i.e. the apparent power) of the ith power distribution node at time t also includes the grid power flow balance constraint. The formula of the grid power flow balance constraint is as follows:

[0086]

[0087] In the formula, and represent the active power and the reactive power of the power distribution node i at time t, respectively, and the square of the active power and the square of the reactive power are equal to the square of the apparent power; represents the voltage phase angle difference between the power distribution node i and the node j at time t; and respectively represent the upper and lower limits of the voltage amplitude of the power distribution node i; and respectively represent the upper and lower limits of the voltage amplitude of the power distribution node i; and respectively represent the upper and lower limits of the active power of the power distribution node i; and respectively represent the conductance and susceptance between the power distribution node i and the power distribution node j; and respectively represent the upper and lower limits of the reactive power of the power distribution node i; represent the maximum power transmission between the power distribution node i and the power distribution node j; n represents the total number of nodes of the power distribution nodes in the target power distribution network; r represents the number of nodes of the power distribution nodes to which the electric vehicles are connected.

[0088] In the embodiment, the basic load power of each power distribution node without connecting the electric vehicles is obtained, the target load function of the target power distribution network is constructed according to the cluster power feasible region and the basic load power, and the cluster power value when the target load function of the target power distribution network is minimum is determined as the cluster deployment power based on the target load function. The embodiment considers the basic load power of the power distribution node itself, compared with simply considering the power of the electric vehicle, considers the basic load power difference of different power distribution nodes in the entire target power distribution network from the global perspective, and improves the accuracy of the cluster deployment power obtained by solving.

[0089] In an exemplary embodiment, the target load function includes a power grid node peak-valley difference term and a power grid load fluctuation rate term; determining the cluster power value when the target load function of the target power distribution network is minimum as the cluster deployment power based on the target load function includes:

[0090] normalizing the fluctuation interval of the power grid peak-valley difference term and the fluctuation interval of the power grid load fluctuation rate term in the target load function to obtain a fluctuation interval of the target load function value;

[0091] determining the cluster power value when the target load function value is minimum as the cluster deployment power based on the fluctuation interval of the target load function value.

[0092] Since the target load function includes the grid peak valley difference term and the grid load fluctuation rate term, in order to improve the optimization efficiency, the grid peak valley difference term and the grid load fluctuation rate term can be solved by double target optimization, and converted into single target optimization by normalization processing. In this embodiment, the fluctuation interval of the target load function value (i.e. the normalized value of the grid peak valley difference and the grid load fluctuation rate) is obtained by normalizing the fluctuation interval of the grid peak valley difference term and the fluctuation interval of the grid load fluctuation rate term in the target load function. Then, the cluster power value when the target load function value is the minimum value can be selected as the cluster deployment power based on the fluctuation interval of the target load function value.

[0093] In this embodiment, the double target optimization problem is converted into a single target optimization problem, which effectively improves the optimization efficiency of the target load function and speeds up the search speed of the cluster deployment power.

[0094] In an exemplary embodiment, the value constraint of the cluster deployment power is updated based on the real-time access number and the charged duration of the electric vehicles accessing the target distribution network at each distribution node.

[0095] In addition to satisfying the cluster power feasible region, the constraint condition of the value constraint of the cluster deployment power also needs to consider the charging expectation demand of each electric vehicle at the distribution node, otherwise the phenomenon that the target load function on the target distribution network side is optimal, but the charging power of each electric vehicle at the distribution node does not meet the expectation demand may occur. Therefore, the value constraint of the cluster deployment power can be described in the following formula form:

[0096]

[0097]

[0098]

[0099] In the above formula, n is the real-time access number of the electric vehicles accessing the distribution node; 、 and are the expected state of charge, the network state of charge and the maximum state of charge of the i-th electric vehicle in the distribution node. In the above formula, indicates the network time of the cluster of each electric vehicle at the distribution node. indicates the off-network time corresponding to the cluster of each electric vehicle at the distribution node. and are the maximum charging capacity (i.e. the sum of the difference between the rated capacity of the battery of each electric vehicle and the battery capacity at the network time) and the minimum charging capacity of the cluster of n electric vehicles in the distribution node. represents the charged time length of the cluster of the n electric vehicles in the power distribution node. The essence of this embodiment is to make maximum and minimum constraints on the charging expected power of the cluster of the n electric vehicles in the power distribution node. As the charging proceeds, and the values gradually decrease, and the constraint conditions are updated in time with the change of the real-time access number n of the electric vehicles accessed by the power distribution node.

[0100] The embodiment updates the value of the cluster allocation power based on the real-time access number and the charged time length of the electric vehicles accessed by each power distribution node in the target power distribution network, so that the value of the cluster allocation power is constrained with the change of the real-time access number and the charged time length, so that the charging power of each electric vehicle under the power distribution node can meet the expected demand while the load on the target power distribution network side is minimized.

[0101] In an exemplary embodiment, as shown in Figure 7 , controlling the charging of each electric vehicle in the power distribution node based on the cluster allocation power includes steps 502 to 506. Among them:

[0102] Step 502, determining the minimum expected power and the maximum expected power of the electric vehicle according to the power feasible region of the electric vehicle.

[0103] Among them, the minimum expected power is the charging power at the current state of charge at the current time, which makes the expected off-network time reach the expected state of charge, and the maximum expected power is the charging power at the current state of charge at the current time, which makes the expected off-network time reach the highest state of charge.

[0104] Since the electric vehicle needs to meet the constraints of the power feasible region of the electric vehicle, during the process of charging the electric vehicle from the current time to the expected off-network time, the charging power of the electric vehicle will exist at a certain minimum value (i.e. the minimum expected power) within a certain Therefore, when allocating power, the target allocation power allocated to the electric vehicle must be greater than or equal to the minimum expected power to meet the expected charging demand of the electric vehicle. Exemplarily, the calculation formula of the minimum expected power is as follows:

[0105]

[0106] In the formula, represents the expected off-network time of the i-th electric vehicle in the power distribution node; represents the current time; E Ni is the rated capacity of the battery of the i-th electric vehicle; represents the maximum charging power of the i-th electric vehicle. If the minimum expected power is negative, it means that the electric vehicle can be in idle state at this time period. It can be understood that the sum of all positive minimum expected powers of the electric vehicles in the power distribution node is a lower bound constraint of the cluster allocation power of the power distribution node, i.e. the cluster allocation power is greater than the sum of all positive minimum expected powers of the electric vehicles in the power distribution node. In addition, since the state of charge of the electric vehicle after charging in a time period cannot exceed the highest state of charge , there is also a maximum value of the target allocation power for the electric vehicle (i.e. the maximum expected power). Exemplarily, the calculation formula of the maximum expected power is as follows:

[0107]

[0108] Step 504, determining the target allocation power of the electric vehicle when the target power function of the power distribution node reaches the maximum value, the target power function being determined based on the ratio between the target allocation power of the electric vehicle in the power distribution node and the maximum expected power, the target allocation power being greater than or equal to the minimum expected power and less than or equal to the maximum expected power, and the sum of the target allocation powers of the electric vehicles under the power distribution node being the cluster allocation power of the power distribution node.

[0109] wherein the target power function is used to describe the degree of satisfaction of the power allocation of the electric vehicle under the power distribution node, and the target power function includes the ratio between the target allocation power of the electric vehicle in the power distribution node and the maximum expected power. The target allocation power is greater than or equal to the minimum expected power and less than or equal to the maximum expected power, so as to avoid the sum of the target allocation powers of the electric vehicles under the power distribution node being the cluster allocation power of the power distribution node.

[0110] Exemplarily, the calculation formula of the target power function is as follows:

[0111]

[0112]

[0113]

[0114] wherein A is a power matrix of the target allocation power of the electric vehicle under the power distribution node, and B is a power matrix of the maximum charging power of the electric vehicle under the power distribution node. A i is the target allocation power of the i-th electric vehicle, i.e. . B i ​​​​Assigning the target distribution power for the i-th electric vehicle. Assigning the cluster distribution power for the j-th distribution node. Assigning the target distribution power for the 1st electric vehicle at time t, Assigning the target distribution power for the 2nd electric vehicle at time t, Assigning the target distribution power for the n-th electric vehicle at time t. Assigning the maximum charging power for the 1st electric vehicle at time t, Assigning the maximum charging power for the 2nd electric vehicle at time t, Assigning the maximum charging power for the n-th electric vehicle at time t.

[0115] Step 506: Charging the electric vehicle according to the target distribution power.

[0116] The embodiment can adjust the charging power of the electric vehicle to the target distribution power according to the target distribution power, so that when the electric vehicle is charged, the charging power of each electric vehicle under the distribution node is maximized after the cluster distribution power is decomposed to each electric vehicle.

[0117] In the embodiment, by constructing a target power function based on the ratio between the target distribution power and the maximum expected power of the electric vehicle in the distribution node, the target distribution power of the electric vehicle is obtained by solving the maximum value of the target power function of the distribution node, so that the charging power of each electric vehicle under the distribution node is maximized after the cluster distribution power is decomposed to each electric vehicle. While optimizing the load on the side of the target distribution network, the charging efficiency on the side of the electric vehicle is also optimized.

[0118] In order to make the charging regulation method provided by the present application more clearly, a specific embodiment will be described below, in which a server is taken as an example, and the embodiment includes the following contents:

[0119] Step 1: Obtaining the charging parameter information of each electric vehicle accessing the distribution node.

[0120] The charging parameter information includes: the maximum state of charge , the expected state of charge , the network access state of charge , the minimum state of charge , the network access time , the expected off-network time , the maximum charging power .

[0121] Step 2: Establishing the power feasible region of each electric vehicle;

[0122] The power feasible region can be represented as the intersection of the following inequalities:

[0123]

[0124] In the above formula: is the rated capacity of the battery of the electric vehicle.

[0125] Step 3: Obtain the cluster power feasible region of the cluster of multiple electric vehicles at the power distribution node by Minkowski sum of the power feasible regions of each electric vehicle at the power distribution node.

[0126] Taking the number of electric vehicles accessing a certain power distribution node as n, the Minkowski sum of the power feasible regions of 1-n electric vehicles is obtained in sequence to obtain the cluster power feasible region of the cluster of n electric vehicles at the power distribution node:

[0127]

[0128] In the above formula: represents the Minkowski sum.

[0129] Step 4: Establish an ordered charging optimization model corresponding to the cluster of multiple electric vehicles at the power distribution node, and obtain the cluster deployment power by solving the ordered charging optimization model.

[0130] (4-1) The objective load function of the ordered charging optimization model includes:

[0131] ① Minimum peak-valley difference of power grid:

[0132] The embodiment can reduce the overlap with the load peak value by transferring the charging load of the electric vehicle to the valley period of the entire target power distribution network, reduce the peak-valley difference of the target power distribution network, thereby reducing the peak regulation pressure of the power distribution network and improving the operation efficiency of the power grid. The minimum peak-valley difference term in the objective load function can be expressed as:

[0133]

[0134]

[0135] In the formula: is the charging power (i.e. cluster deployment power) of the cluster of electric vehicles at the jth power distribution node of the target power distribution network in the tth period; is the basic load power of the jth power distribution node of the target power distribution network in the tth period; is the total system load power of the jth node of the power distribution network in the tth period; The grid peak-valley difference of the target distribution network. The selected predetermined statistical duration is 24 hours, i.e. T = 24h. In the predetermined statistical duration T, each distribution node of the target distribution network has a node peak-valley difference. The node peak-valley differences of the r distribution nodes with electric vehicles are summed up to obtain the grid peak-valley difference of the target distribution network. Here, the cluster power value at which the grid peak-valley difference of the target distribution network is the smallest after the electric vehicles are connected to the target distribution network is solved, i.e. the cluster deployment power.

[0136] ② The node load fluctuation rate is the smallest:

[0137]

[0138]

[0139]

[0140] In the formula: represents the average load power of the jth distribution node of the target distribution network in the T period; represents the overall standard deviation of the load power of the distribution network in the T period; represents the grid load fluctuation rate of the target distribution network.

[0141] In order to improve the optimization efficiency, the optimal solution of the above double target is converted into a single target problem, and the final target load function can be obtained through normalization processing:

[0142]

[0143] In the formula: represents the grid peak-valley difference of the target distribution network without connecting electric vehicles; represents the grid load fluctuation rate of the target distribution network without connecting electric vehicles; and are weight coefficients corresponding to the grid peak-valley difference and the grid load fluctuation rate, satisfying , and , .

[0144] The constraint conditions of the ordered charging optimization model also include:

[0145] ① Grid flow balance constraint

[0146]

[0147] In the formula, and respectively represent the active power and the reactive power of the distribution node i at time t, and the square of the active power is equal to the square of the reactive power. represents the phase angle difference between the voltage of distribution node i and the voltage of distribution node j at time t; and respectively represent the voltage amplitude of distribution node i and distribution node j at time t; and respectively represent the upper limit and the lower limit of the voltage amplitude of distribution node i; and respectively represent the upper limit and the lower limit of the active power of distribution node i; and respectively represent the conductance and the susceptance between distribution node i and distribution node j; and respectively represent the upper limit and the lower limit of the reactive power of distribution node i; represents the maximum power transmission between distribution node i and distribution node j; n represents the total number of distribution nodes in the target distribution network; r represents the number of distribution nodes with electric vehicles connected.

[0148] ② Electric vehicle cluster constraint

[0149] For the constraint of the electric vehicle cluster under the distribution node, in addition to satisfying the cluster power feasible region obtained by the Minkowski summation of the n electric vehicles under the distribution node, the expected charging demand of each electric vehicle in the electric vehicle cluster also needs to be considered, otherwise the phenomenon that the optimal target load function on the target distribution network side but the charging power of the electric vehicle cluster does not meet the expected demand may occur.

[0150] The electric vehicle cluster constraint is as follows:

[0151]

[0152]

[0153]

[0154] In the above formula, n is the real-time access number of electric vehicles connected to the distribution node; 、 and are the expected state of charge, the in-network state of charge and the highest state of charge of the i-th electric vehicle in the distribution node. In the above formula, indicates the in-network time of the cluster of each electric vehicle under the distribution node. indicates the off-network time of the cluster of each electric vehicle under the corresponding distribution node. and are the maximum charging capacity (i.e. the sum of the difference between the rated capacity of the battery of each electric vehicle and the battery capacity at the in-network time) and the minimum charging capacity of the cluster of n electric vehicles in the distribution node. This represents the charging time of the cluster of n electric vehicles in the power distribution node. Essentially, this embodiment imposes maximum and minimum constraints on the expected charging power of this cluster of n electric vehicles in the power distribution node. As charging progresses, and The value gradually decreases, and the constraints are updated in a timely manner as the number of electric vehicles connected to the power distribution node, n, changes in real time.

[0155] Step 5: Decompose the cluster dispatch power to each electric vehicle, and regulate and allocate charging power for each electric vehicle under the power distribution node.

[0156]

[0157]

[0158]

[0159] Where A is the power matrix of the target power allocation for each electric vehicle under the power distribution node, and B is the power matrix of the maximum charging power for each electric vehicle under the power distribution node. The cluster power is allocated to the j-th distribution node. The target power allocation for the first electric vehicle at time t. The target power allocation for the second electric vehicle at time t. The target power allocation is set for the nth electric vehicle at time t. Let be the maximum charging power of the first electric vehicle at time t. Let be the maximum charging power of the second electric vehicle at time t. Let be the maximum charging power of the nth electric vehicle at time t.

[0160] Because electric vehicles need to satisfy the constraints of their power feasible region, the charging power of an electric vehicle will vary during the process of charging from the current moment to the desired off-grid moment. There exists a minimum value within a certain time period. (i.e., minimum expected power). Therefore, when allocating power, the target power allocated to electric vehicles must be greater than or equal to the minimum expected power. Only in this way can the charging expectations of electric vehicles be met. For example, the minimum expected power... The calculation formula is as follows:

[0161]

[0162] In the formula, This represents the expected off-grid time of the i-th electric vehicle in the power distribution node; Indicates the current time; ENi Let be the rated capacity of the battery of the i-th electric vehicle; Let represent the maximum charging power of the i-th electric vehicle. If the minimum desired power... A negative value indicates that the electric vehicle is currently... It can be in an idle state during the time period. This can be understood as the minimum expected power of all positive numbers in the distribution node. The sum of these values ​​constitutes the lower limit constraint on the cluster dispatch power of this distribution node, i.e., the minimum expected power greater than all positive numbers. Only the sum of these can meet the charging expectations of all electric vehicles in the power distribution node.

[0163] In addition, because the state of charge of an electric vehicle after a period of charging cannot exceed its maximum state of charge. Therefore, there is also a maximum value for the target power allocation for electric vehicles. (i.e., maximum expected power). For example, maximum expected power... The calculation formula is as follows:

[0164]

[0165] Therefore, the problem of allocating cluster power to each electric vehicle is as follows:

[0166]

[0167] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0168] Based on the same inventive concept, this application also provides a charging control device for implementing the charging control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more charging control device embodiments provided below can be found in the limitations of the charging control method described above, and will not be repeated here.

[0169] In one example embodiment, as shown in FIG. 6, there is provided a charging regulation device 600, comprising a constructing module 610, an aggregating module 620, an optimizing module 630 and a charging module 640, wherein: Figure 8

[0170] The constructing module 610 is configured to acquire charging parameter information of a plurality of electric vehicles accessing a power distribution node of a target power distribution network, and construct a power feasible region of each electric vehicle according to the charging parameter information of the electric vehicle.

[0171] The aggregating module 620 is configured to aggregate the power feasible regions of the electric vehicles accessing each power distribution node to obtain a cluster power feasible region of the power distribution node.

[0172] The optimizing module 630 is configured to determine a cluster allocation power based on the cluster power feasible region of each power distribution node, the cluster allocation power being a power at which a target load function of the target power distribution network reaches a minimum value, the target load function being determined based on a power grid peak-valley difference and a power grid load fluctuation rate of the target power distribution network.

[0173] The charging module 640 is configured to control the electric vehicles in the power distribution node to charge based on the cluster allocation power.

[0174] In one example embodiment, the constructing module 610 is further configured to extract a state of charge parameter, a time of accessing the network, an expected time of leaving the network and a maximum charging power from the charging parameter information of each electric vehicle, determine a state of charge interval constraint based on the state of charge parameter, determine a charging time constraint based on the time of accessing the network and the expected time of leaving the network, and determine a slope constraint of the state of charge and the charging time based on the maximum charging power, to obtain the power feasible region of the electric vehicle.

[0175] In one example embodiment, the aggregating module 620 is further configured to determine the power feasible regions of the electric vehicles accessing each power distribution node, and determine a Minkowski sum of the power feasible regions of the electric vehicles accessing the power distribution node as the cluster power feasible region of the power distribution node.

[0176] In one example embodiment, the optimizing module 630 is further configured to acquire a basic load power of each power distribution node without accessing the electric vehicle, construct a target load function of the target power distribution network based on the cluster power feasible region and the basic load power, and determine the cluster allocation power as a cluster power value at which the target load function of the target power distribution network reaches the minimum value based on the target load function.

[0177] ​In one of the embodiments, the target load function comprises a peak-valley difference term of the power grid node and a load fluctuation rate term of the power grid, and the optimization module 630 is further configured to: perform normalization processing on a fluctuation interval of the peak-valley difference term of the target load function and a fluctuation interval of the load fluctuation rate term of the target load function to obtain a fluctuation interval of the target load function value, and determine, based on the fluctuation interval of the target load function value, a cluster deployment power value corresponding to a minimum value of the target load function value as the cluster deployment power.

[0178] In one of the embodiments, the value constraint of the cluster deployment power is updated based on a real-time access number and a charged duration of the electric vehicles accessing the target distribution grid.

[0179] In one of the embodiments, the charging module 640 is further configured to: determine, according to a power feasible region of the electric vehicle, a minimum expected power and a maximum expected power of the electric vehicle, determine a target allocation power of the electric vehicle when the target power function of the distribution node reaches a maximum value, the target power function being determined based on a ratio between the target allocation power of the electric vehicle and the maximum expected power, the target allocation power being greater than or equal to the minimum expected power and less than or equal to the maximum expected power, and a sum of the target allocation powers of the electric vehicles under the distribution node being the cluster deployment power of the distribution node, and charge the electric vehicle according to the target allocation power.

[0180] The above modules in the charging regulation device can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of a processor in a computer device in a hardware form, or stored in a memory in the computer device in a software form, so as to be called and executed by a processor to perform operations corresponding to the above modules.

[0181] In one of the exemplary embodiments, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in FIG. 8. Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are 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 capability. 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 and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. The computer program is executed by the processor to implement a charging regulation method.

[0182] Those skilled in the art can understand that, Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0183] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above embodiments.

[0184] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0185] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0186] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0187] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of the technical features is deemed to be within the scope of the present application.

[0188] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A charge regulation method, characterized by, The method comprises: obtaining charging parameter information of a plurality of electric vehicles accessing power distribution nodes in a target power distribution network, and constructing a power feasible region of each electric vehicle according to the charging parameter information of each electric vehicle; for each power distribution node, aggregating the power feasible regions of the electric vehicles accessing the power distribution node to obtain a cluster power feasible region of the power distribution node; determining a cluster allocation power based on the cluster power feasible regions of each power distribution node, the cluster allocation power being determined when a target load function of the target power distribution network reaches a minimum value, the target load function being determined based on a power grid peak-valley difference and a power grid load fluctuation rate of the target power distribution network; controlling each electric vehicle in the power distribution node to charge based on the cluster allocation power; the determination of the cluster allocation power based on the cluster power feasible regions of each power distribution node comprises: obtaining a basic load power when each power distribution node does not access the electric vehicles; constructing a target load function of the target power distribution network according to the cluster power feasible region and the basic load power; determining the cluster allocation power when the target load function of the target power distribution network reaches the minimum value based on the target load function; the target load function comprises a power grid node peak-valley difference term and a power grid load fluctuation rate term; the determination of the cluster allocation power when the target load function reaches the minimum value based on the target load function comprises: normalizing fluctuation intervals of the power grid peak-valley difference term and the power grid load fluctuation rate term in the target load function to obtain a fluctuation interval of the target load function value; determining the cluster power value when the target load function value is the minimum value as the cluster allocation power based on the fluctuation interval of the target load function value.

2. The method of claim 1, wherein, the construction of the power feasible region of each electric vehicle according to the charging parameter information of each electric vehicle comprises: for each electric vehicle, extracting a state of charge parameter, a network access time, an expected network exit time, and a maximum charging power from the charging parameter information of the electric vehicle; determining a state of charge interval constraint based on the state of charge parameter, a charging time constraint based on the network access time and the expected network exit time, and a slope constraint between the state of charge and the charging time based on the maximum charging power to obtain the power feasible region of the electric vehicle.

3. The method of claim 1, wherein, the aggregation of the power feasible regions of the electric vehicles accessing each power distribution node to obtain the cluster power feasible region of the power distribution node comprises: determining the power feasible region of each electric vehicle accessing each power distribution node; determining the Minkowski sum of the power feasible regions of the electric vehicles accessing the power distribution node as the cluster power feasible region of the power distribution node.

4. The method of claim 1, wherein, the value constraint of the cluster allocation power is updated based on the real-time access number and the charged duration of the electric vehicles accessing each power distribution node in the target power distribution network.

5. The method according to any one of claims 1 to 4, characterized in that, the control of each electric vehicle in the power distribution node to charge based on the cluster allocation power comprises: determining a minimum expected power and a maximum expected power of the electric vehicles according to a power feasible region of the electric vehicles; determining a target allocation power of the electric vehicles when a target power function of the power distribution node reaches a maximum value, the target power function being determined based on a ratio between the target allocation power of the electric vehicles and the maximum expected power in the power distribution node, the target allocation power being greater than or equal to the minimum expected power and less than or equal to the maximum expected power, and a sum of the target allocation power of each electric vehicle under the power distribution node being a cluster allocation power of the power distribution node; charging the electric vehicles according to the target allocation power.

6. A charge regulation device comprising: The method comprises the following steps of: constructing a module, aggregating a module, optimizing a module, and charging a module, wherein: The constructing module is configured to acquire charging parameter information of a plurality of electric vehicles accessing a target power distribution network, and construct a power feasible region of each electric vehicle according to the charging parameter information of each electric vehicle. The aggregating module is configured to aggregate the power feasible region of each electric vehicle accessing each power distribution node for each power distribution node to obtain a cluster power feasible region of the power distribution node. The optimizing module is configured to determine a cluster allocation power when a target load function of the target power distribution network reaches a minimum value based on the cluster power feasible region of each power distribution node, the target load function being determined based on a power grid peak-valley difference and a power grid load fluctuation rate of the target power distribution network. The charging module is configured to control each electric vehicle in the power distribution node to charge based on the cluster allocation power. The optimizing module is further configured to: acquire a basic load power when each power distribution node does not access the electric vehicle, construct a target load function of the target power distribution network according to the cluster power feasible region and the basic load power, and determine a cluster allocation power when the target load function of the target power distribution network reaches a minimum value based on the target load function. The target load function comprises a power grid peak-valley difference term and a power grid load fluctuation rate term, and the optimizing module is further configured to: normalize a fluctuation interval of the power grid peak-valley difference term and a fluctuation interval of the power grid load fluctuation rate term in the target load function to obtain a fluctuation interval of a target load function value, and determine a cluster power value when the target load function value is the minimum value as the cluster allocation power based on the fluctuation interval of the target load function value.

7. The apparatus of claim 6, wherein, The constructing module is further configured to: extract a state of charge parameter, a network access time, an expected network leaving time, and a maximum charging power from the charging parameter information of each electric vehicle, determine a state of charge interval constraint based on the state of charge parameter, determine a charging time constraint based on the network access time and the expected network leaving time, and determine a slope constraint of the state of charge and the charging time based on the maximum charging power to obtain the power feasible region of the electric vehicle.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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