An electric vehicle charging and discharging strategy optimization method and related device

Abstract

The application discloses an electric vehicle charging and discharging strategy optimization method and related device, comprising: constructing a mathematical optimization model of power distribution network dynamic reconstruction according to the load data of the target system of joint optimization, and solving to obtain the dynamic reconstruction result; creating a probability distribution function of the travel characteristic quantity of the electric vehicle based on the travel chain theory, and obtaining the travel chain parameters of the electric vehicle by random sampling of the Monte Carlo method according to each probability distribution function; determining the electric vehicle network access area according to the travel chain parameters, and determining the charging and battery swapping station of the electric vehicle according to the reconstructed power grid voltage condition; constructing an electric vehicle charging and discharging strategy optimization model according to the reconstructed network structure and the travel chain parameters, and solving to obtain the joint optimization result; thereby solving the problem that the prior art ignores the characteristics of power distribution network reconstruction and electric vehicle charging and discharging, and cannot reasonably realize the coordination between the two optimization methods and maximize the advantages of the two.

Description

Technical Field

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

[0002] In recent years, with the integration of renewable energy sources such as wind and solar power generation and large-scale electric vehicle charging loads into the power grid, the power flow of the power system has been adversely affected, posing a severe challenge to the safe and stable operation of the distribution network.

[0003] Distribution network reconfiguration, as an effective power flow optimization method, alters the network structure of the distribution network by switching switches, thereby changing the power flow direction, optimizing power distribution, and achieving the goals of reducing network losses and improving voltage quality. Electric vehicles, as highly flexible mobile energy storage units, possess both source and load attributes. They have enormous potential in smoothing grid load fluctuations, peak shaving and valley filling, participating in grid ancillary services, and providing flexibility supplements to the grid. Through reasonable charging and discharging control strategies, the negative impact of large-scale electric vehicle charging loads on the grid can be effectively mitigated, and the safety and economy of power system operation can be improved.

[0004] Current research on the joint optimization of distribution network reconfiguration and electric vehicle (EV) charging and discharging mainly focuses on the dynamic reconfiguration of the distribution network considering EVs. One approach involves first scheduling EV charging in an orderly manner, and then dynamically reconfiguring the distribution network based on the scheduling results. Another approach simultaneously considers EV optimization and distribution network reconfiguration, ultimately obtaining both EV charging / discharging strategies and reconfiguration results. However, these methods neglect the characteristics of each approach and fail to reasonably coordinate the two optimization methods to maximize their advantages. Specifically, network reconfiguration, due to the inability of switches to be adjusted in real time, is typically optimized and the switching scheme determined on a long-term scale (usually day-ahead). In contrast, EV charging and battery swapping stations possess short-term power regulation capabilities, allowing for optimization of network power flow distribution on a short-term scale by optimizing charging and discharging power. Summary of the Invention

[0005] This application provides a method and related apparatus for optimizing electric vehicle charging and discharging strategies, which addresses the problem that existing technologies neglect the characteristics of both power distribution network reconfiguration and electric vehicle charging and discharging, and cannot reasonably coordinate the two optimization methods to maximize their advantages.

[0006] In view of this, the first aspect of this application provides a method for optimizing the charging and discharging strategy of an electric vehicle, the method comprising:

[0007] The target system for joint optimization is determined. A mathematical optimization model for dynamic reconfiguration of the distribution network is constructed based on the load data of the target system for several time periods within a day and the model is solved to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include: the reconfigured network structure and the reconfigured grid voltage status.

[0008] Based on the travel chain theory, a probability distribution function for the travel characteristics of electric vehicles within a day is created. The travel chain parameters of electric vehicles within a day are obtained by random sampling using the Monte Carlo method according to the probability distribution function.

[0009] The regions where electric vehicles can be connected to the grid are determined based on the travel chain parameters, and the charging and battery swapping stations for electric vehicles are determined based on the reconstructed grid voltage conditions.

[0010] Based on the reconstructed network structure and the travel chain parameters, an electric vehicle charging and discharging strategy optimization model is constructed and solved to obtain the jointly optimized result.

[0011] Optionally, the step of constructing a mathematical optimization model for dynamic reconfiguration of the distribution network based on load data from several time periods within a day of the target system includes:

[0012] Using the maximum sum of voltage margins of load nodes as the objective function and determining the first constraint, a mathematical optimization model for dynamic reconfiguration of the distribution network is constructed based on historical load data or predicted load data of the target system for several time periods within a day.

[0013] Optionally, the first constraint conditions include: node power balance constraint, branch power flow constraint, node voltage constraint, branch capacity constraint, generator power constraint, and network structure constraint.

[0014] Optionally, the travel characteristics include: the user's first travel time, the travel time between different areas, the stay time in each area, and the real-time SOC of the electric vehicle.

[0015] Optionally, obtaining the travel chain parameters of electric vehicles within a day by randomly sampling using the Monte Carlo method according to each of the probability distribution functions includes:

[0016] Based on the probability distribution function of the user's first trip time, the first trip time of each electric vehicle is sampled and generated;

[0017] Based on the probability distribution function of driving time and initial SOC between the regions, the initial SOC and first trip driving time of the electric vehicle are sampled and generated.

[0018] Based on the travel chain structure, the probability distribution function of the travel time between the regions and the dwell time in each region, the travel time of electric vehicles between the regions and the dwell time in each region are randomly sampled and generated.

[0019] The SOC of each electric vehicle when it arrives at zone W is calculated based on the initial SOC of the electric vehicle generated by sampling, the first trip driving time, the average driving speed, and the power consumption per unit mileage.

[0020] Based on the sampled travel time of the electric vehicles between different areas and the dwell time in each area, the arrival time of the electric vehicles in each area is calculated.

[0021] Optionally, the step of constructing an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and the travel chain parameters includes:

[0022] Using the minimum power balance of branch transmission as the objective function and determining the second constraint, an optimization model for electric vehicle charging and discharging strategies is constructed based on the reconstructed network structure and the travel chain parameters.

[0023] Optionally, the second constraint is:

[0024]

[0025] In the formula, Let be the charging power and discharging power of the nth electric vehicle at time t, respectively. These are the upper and lower limits of electric vehicle charging power and discharging power, respectively. The charging / discharging indicator for electric vehicles has a value between 0 and 1; T n,in T n,out For the time of electric vehicle grid connection and disconnection; SOC Tn,out State of Charge (SOC) of an electric vehicle when it is off-grid; SOC wash This represents the expected SOC when the network is offline.

[0026] A second aspect of this application provides an electric vehicle charging and discharging strategy optimization system, the system comprising:

[0027] The reconfiguration unit is used to determine the target system for joint optimization, construct a mathematical optimization model for dynamic reconfiguration of the distribution network based on the load data of the target system for several time periods within a day, and solve it to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include: the reconfigured network structure and the reconfigured grid voltage status.

[0028] The sampling unit is used to create a probability distribution function of the travel characteristics of electric vehicles within a day based on the travel chain theory, and to obtain the travel chain parameters of electric vehicles within a day by random sampling using the Monte Carlo method according to the probability distribution function.

[0029] The analysis unit is used to determine the area where electric vehicles can enter the grid based on the travel chain parameters, and to determine the charging and battery swapping stations for electric vehicles based on the reconstructed grid voltage conditions.

[0030] The modeling unit is used to construct an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and the travel chain parameters, and solve it to obtain the jointly optimized result.

[0031] A third aspect of this application provides an electric vehicle charging and discharging strategy optimization device, the device comprising a processor and a memory:

[0032] The memory is used to store program code and transmit the program code to the processor;

[0033] The processor is configured to execute the steps of the electric vehicle charging and discharging strategy optimization method as described in the first aspect above, according to the instructions in the program code.

[0034] A fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the electric vehicle charging and discharging strategy optimization method described in the first aspect above.

[0035] As can be seen from the above technical solutions, this application has the following advantages:

[0036] This application provides a method for optimizing electric vehicle (EV) charging and discharging strategies. Based on the characteristics and advantages of both reconfiguration optimization and EV charging and discharging optimization, it aims to improve the flexibility and security of the distribution network by optimizing network losses, voltage margin, and branch transmission power balance. This method achieves joint optimization of distribution network reconfiguration and EV charging and discharging strategies. Compared to traditional dynamic reconfiguration of the distribution network that only considers the orderly scheduling of EV charging loads, this application uses a joint optimization method that first determines the network structure at different time periods within a day through dynamic reconfiguration, and then optimizes the EV charging and discharging strategy. This approach fully utilizes the characteristics of their respective adjustment time scales and achieves complementary advantages. Furthermore, it uses the voltage situation after reconfiguration to provide a basis for selecting charging and swapping stations, and considers the EV travel chain during the EV charging and discharging optimization stage, making it closer to actual conditions. The final optimization results show that this method is significant for improving the flexibility and safe and stable operation of the distribution network. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating an electric vehicle charging and discharging strategy optimization method provided in an embodiment of this application.

[0038] Figure 2 This is a schematic diagram of the travel chain provided in the embodiments of this application;

[0039] Figure 3 This is a topology diagram of the 33-node system provided in the embodiments of this application;

[0040] Figure 4 This application provides the system load for 96 time periods per day in the embodiments of this application;

[0041] Figure 5 This is a system structure diagram for time periods 1-33 after reconstruction, provided in the embodiments of this application;

[0042] Figure 6 This is a system structure diagram for time periods 34-96 after reconstruction, provided in the embodiments of this application;

[0043] Figure 7 The reconfigured system node voltage provided in the embodiments of this application;

[0044] Figure 8 This is a comparison of the average system voltage at various times before and after reconstruction, as provided in the embodiments of this application.

[0045] Figure 9 This application provides a comparison of the average voltage values ​​of each node in the two intervals before and after reconstruction in the embodiments of this application.

[0046] Figure 10 The travel times provided in the embodiments of this application;

[0047] Figure 11 This is the initial SOC provided in the embodiments of this application;

[0048] Figure 12 The HW driving time provided in the embodiments of this application;

[0049] Figure 13 The driving time of WO provided in the embodiments of this application;

[0050] Figure 14 The OH / WH driving time provided in the embodiments of this application;

[0051] Figure 15 This refers to the dwell time in area W provided in the embodiments of this application;

[0052] Figure 16 This refers to the dwell time in zone O provided in this application embodiment;

[0053] Figure 17 The charging and discharging results of the electric vehicle provided in the embodiments of this application;

[0054] Figure 18 This application provides a comparison of the active power load of the system before and after regulation in the embodiments of this application.

[0055] Figure 19This is a comparison between the jointly optimized average voltage at each time step and the reconstructed voltage provided in the embodiments of this application;

[0056] Figure 20 This is a comparison between the jointly optimized average voltage of each node and the reconstructed voltage in the embodiments of this application.

[0057] Figure 21 This document compares the joint optimization and single electric vehicle optimization provided in the embodiments of this application in terms of time-voltage.

[0058] Figure 22 This application provides a comparison of the combined optimization and single electric vehicle optimization in terms of node voltage in the embodiments of this application.

[0059] Figure 23 This is a schematic diagram of the structure of an electric vehicle charging and discharging strategy optimization system provided in the embodiments of this application. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0061] Please see Figure 1 The present application provides an electric vehicle charging and discharging strategy optimization method, which includes:

[0062] Step 101: Determine the target system for joint optimization. Based on the load data of the target system for several time periods within a day, construct a mathematical optimization model for dynamic reconfiguration of the distribution network and solve it to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include: the reconfigured network structure and the reconfigured grid voltage status.

[0063] Step 102: Based on the travel chain theory, create a probability distribution function for the travel characteristics of electric vehicles within a day, and use the Monte Carlo method to randomly sample and obtain the travel chain parameters of electric vehicles within a day according to each probability distribution function.

[0064] Step 103: Determine the areas where electric vehicles can be connected to the grid based on the travel chain parameters, and determine the charging and battery swapping stations for electric vehicles based on the reconstructed grid voltage conditions.

[0065] Step 104: Based on the reconstructed network structure and travel chain parameters, construct an electric vehicle charging and discharging strategy optimization model and solve it to obtain the joint optimization result.

[0066] In one embodiment, step 101, which involves constructing a mathematical optimization model for dynamic reconfiguration of the distribution network based on load data from several time periods throughout the day of the target system, includes:

[0067] Using the maximum sum of voltage margins of load nodes as the objective function and determining the first constraint, a mathematical optimization model for dynamic reconfiguration of the distribution network is constructed based on historical or predicted load data for several time periods within a day of the target system.

[0068] The first set of constraints includes: node power balance constraints, branch power flow constraints, node voltage constraints, branch capacity constraints, generator power constraints, and network structure constraints.

[0069] It should be noted that,

[0070] 1. Objective function:

[0071] With the objective function of maximizing the sum of voltage margins of load nodes, the voltage safety margin reflects the system's safety performance, i.e., the node's ability to withstand disturbances. The expression shows that the larger the node voltage is than the critical stability voltage, the greater the voltage safety margin, indicating a stronger ability of the node to withstand power fluctuations, a safer node, and higher overall node flexibility in the distribution network.

[0072]

[0073] In the above formula, I represents the total number of nodes in the network; T represents the total reconstruction time period; U i,t U represents the voltage at time t of the i-th node; cr,i This represents the critical stable voltage of the i-th node.

[0074] 2. Constraints (First Constraint):

[0075] (1) Node power balance constraints:

[0076]

[0077]

[0078] In the above formula, P ij,(t) and Q ij,(t) These represent the active power and reactive power at the beginning of branch ij at time t, respectively; I ij,(t) R represents the current in branch ij at time t; ij and X ij P represents the resistance and reactance of branch ij, respectively; g,j(t) and Q g,j(t) P represents the active and reactive power supplied by the generator to node j at time t, respectively. DG,j(t) and Q DG,j(t)P represents the active and reactive power supplied by the distributed power source to node j at time t, respectively. jload(t) and Q jload(t) P represents the active and reactive loads at node j at time t, respectively; ESS,j(t) and Q ESS,j(t) Let f(j) and z(j) represent the active and reactive power outputs of the energy storage device at node j at time t, respectively, with charging being positive and discharging being negative; f(j) and z(j) represent the upstream and downstream nodes of node j, respectively.

[0079] After linearization:

[0080]

[0081]

[0082] (2) Branch flow constraints:

[0083] (U j(t) ) 2 -(U i(t) ) 2 =(R ij 2 +X ij 2 )I ij,(t) 2 -2(R ij P ij,(t) +X ij Q ij,(t) (6)

[0084] In the above formula, U i(t) and U j(t) Let i and j represent the voltages at time t, where i and j are the first and last nodes of branch ij, respectively.

[0085] After linearization using the Big M method:

[0086]

[0087]

[0088] In the formula, This represents the square of the current value of branch ij at time t; M represents the square of the voltage value of node i at time t; M is a large positive number.

[0089] (3) Node voltage constraints:

[0090] U i,min ≤U i(t) ≤U i,max (9)

[0091] In the above formula, Ui,min and U i,max These represent the minimum and maximum voltage allowed for node i, respectively.

[0092] After linearization:

[0093]

[0094] (4) Branch capacity constraints:

[0095] P ij,(t) 2 +Q ij,(t) 2 ≤Z ij,(t) (S ij,max ) 2 (11)

[0096] In the above formula, Z ij,(t) S represents the on / off state of branch ij at time t, expressed as a 0-1 variable, where 1 represents a closed branch and 0 represents an open branch; ij,max This represents the maximum transmission capacity allowed for branch ij.

[0097] (5) Generator power constraints:

[0098] P g,min ≤P g,i(t) ≤P g,max (12)

[0099] Q g,min ≤Q g,i(t) ≤Q g,max (13)

[0100] In the above formula, P g,i(t) Q g,i(t) P represents the active and reactive power generated by the generator at node i at time t, respectively; g,max P g,min Q represents the maximum and minimum active power provided by the generator, respectively; g,max Q g,min These represent the maximum and minimum reactive power provided by the generator, respectively.

[0101] (6) Network structure constraints:

[0102] After reconfiguration, the distribution network must operate radially, and the system structure must include all nodes, with no islanding or ring networks. To meet this requirement, the total number of switches disconnected in the reconfigured distribution network must equal the total number of tie switches in the grid.

[0103] In one embodiment, the travel characteristics in step 102 include: the user's first travel time, the travel time between different areas, the stay time in different areas, and the real-time SOC of the electric vehicle.

[0104] Further, in step 102, the travel chain parameters of electric vehicles within a day are obtained by random sampling using the Monte Carlo method based on each probability distribution function, including:

[0105] Based on the probability distribution function of the user's first trip time, the first trip time of each electric vehicle is sampled and generated;

[0106] Based on the probability distribution function of driving time and initial SOC between different zones, the initial SOC and first trip driving time of electric vehicles are sampled and generated.

[0107] Based on the probability distribution function of travel chain structure, travel time between areas and stay time in each area, the travel time of electric vehicles between areas and stay time in each area are randomly sampled and generated.

[0108] The SOC of each electric vehicle when it arrives at zone W is calculated based on the initial SOC, first trip duration, average driving speed, and power consumption per unit mileage of the sampled electric vehicles.

[0109] Based on the sampled travel time of electric vehicles between districts and the dwell time in each district, the arrival time of electric vehicles in each district is calculated.

[0110] It should be noted that the specific explanation of the electric vehicle behavior characteristic modeling is as follows:

[0111] 1. Travel Chain Theory:

[0112] A travel chain describes the entire journey of an electric vehicle user throughout the day, starting from a starting point, making multiple spatial and temporal transfers within a day, and finally returning to the destination. This application uses residential areas as the starting and ending points, and decomposes the daily travel process of electric vehicle users into spatiotemporal features according to the time sequence of users' journeys to their destinations. A travel chain diagram is shown below. Figure 2 As shown.

[0113] 2. Travel destinations and travel chain structure:

[0114] This application considers the frequency of users traveling to various destinations and selects the three most frequently visited destinations: residential areas (H), work areas (W), and commercial areas (O). This application assumes that the length of a private car's travel chain does not exceed 3, meaning a private car can pass through a maximum of 3 destinations per day, and that the user's starting and ending points for each day's travel are both residential areas. This application takes a weekday as an example and considers two travel chain structures: HWH and HWOH.

[0115] 3. Spatiotemporal characteristics of the travel chain:

[0116] (1) User's first trip time:

[0117] For electric vehicles choosing the HWH or HWOH travel chain, according to the NHTS survey results, the initial travel time of the electric vehicle satisfies N(μ e1 ,σ e1 The probability density function of a normal distribution is defined as follows:

[0118]

[0119] In the formula, The initial departure time is for the first destination being the work area; μ e1 σ represents the expected initial travel time. e1 This represents the standard deviation of the initial departure time.

[0120] (2) Travel time between different zones:

[0121] Assuming the travel time of an electric private car depends on the type of destination before and after the trip, it can be divided into three categories: Category I represents traveling from a residential area to a non-residential area; Category II represents traveling from a non-residential area to a non-residential area; and Category III represents traveling from a non-residential area to a residential area. The travel times for all three categories can be considered to follow a log-normal distribution, with the following probability density function:

[0122]

[0123] In the formula, t dj The travel time between different zones; μ dj σ represents the expected travel time between different zones. dj This represents the standard deviation of travel time between different zones.

[0124] (3) Duration of stay in each area:

[0125] According to relevant literature, the dwell time in the W / O zone can be considered to follow a generalized extreme value distribution, and its probability density function is:

[0126]

[0127] In the formula, t pi Duration of stay in different zones; μ i σ is the position parameter; i k is the scale parameter. i For shape parameters.

[0128] Based on the start time of travel, travel time, and dwell time, the arrival times of users in different travel chains to each area and their final return times to their residential areas can be determined. Since electric vehicles spend a relatively long time in the H / W zone in real life, it is assumed that electric vehicles will be connected to the power grid in the H / W zone for charging and discharging regulation. 4. Real-time SOC of Electric Vehicles:

[0129]

[0130] In the formula, SOC0 represents the initial state of charge of each electric vehicle, following a normal distribution N(0.8, 0.04); v di Let τ be the average speed of the electric vehicle between different zones; τ be the power consumption per unit distance of the electric vehicle; and E be the rated capacity of the electric vehicle battery. This formula allows us to determine the state of charge of each electric vehicle upon arrival at each zone, thus enabling us to determine which zone it should connect to the power grid for regulation.

[0131] 5. Travel behavior simulation based on the Monte Carlo method:

[0132] Based on the probability distribution function of the above travel characteristics, Monte Carlo sampling is used to obtain various travel characteristic data, and user travel behavior is simulated. The specific steps are as follows:

[0133] (1) Taking a weekday as an example, the first travel time of each electric vehicle is generated by sampling according to the probability distribution function of the first travel time;

[0134] (2) Generate the initial SOC and first trip driving time of electric vehicle by sampling based on the driving time and the initial SOC probability distribution function;

[0135] (3) Randomly sample and generate the driving time of electric vehicles between different areas and the dwell time in each area based on the probability distribution function of the travel chain structure, driving time and dwell time;

[0136] (4) Calculate the SOC of each electric vehicle when it arrives at the W zone based on the initial SOC generated by sampling, the first trip driving time, the average driving speed and the power consumption per unit mileage.

[0137] (5) Calculate the time when the electric vehicle arrives at each area based on the travel time between the sampled areas and the dwell time in each area.

[0138] The Monte Carlo method can be used to obtain the travel characteristics of each electric vehicle. Assuming that each electric vehicle only undergoes charging and discharging regulation once, the SOC upon arrival in area W is used to determine whether to connect to the power grid in area W. If not connected, the SOC after returning to area H will be calculated, and the vehicle will connect in area H. Based on the arrival time in each area and the choice of the electric vehicle's access area, the time period during which each electric vehicle receives regulation can be determined.

[0139] In one embodiment, step 104 involves constructing an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and travel chain parameters, including:

[0140] With the objective function of minimizing the power balance of branch transmission and the second constraint condition determined, an optimization model for electric vehicle charging and discharging strategies is constructed based on the reconstructed network structure and travel chain parameters.

[0141] It should be noted that the electric vehicle charging and discharging optimization model is explained in detail below:

[0142] 1. Input parameters:

[0143] The input parameters are the network structure after dynamic reconfiguration of the distribution network and the characteristics of electric vehicle travel. The optimized grid node voltage in different sections will provide a basis for the selection of electric vehicle charging stations. Since the access of a large number of electric vehicle charging loads will cause a drop in voltage, the voltage quality improvement effect of reconfiguration is used to neutralize some of the negative impacts brought by electric vehicles.

[0144] 2. Objective function:

[0145] With the objective function of minimizing branch power transmission balance, in a distribution network, although branches cannot directly meet power demand, they provide channels for the transmission of flexible resources and are an important support platform for the responsiveness of these resources. Ensuring the unobstructed flow of network branches is a prerequisite for improving system flexibility. Branch power transmission balance is related to the network's flexibility and availability. The lower the branch power transmission balance in the distribution network, the more balanced the branch power transmission, the more balanced the load, and the higher the network stability. Furthermore, load balancing improves the branch power transmission margin during peak hours, reduces the possibility of branch congestion during peak times, and enhances the overall ability of the distribution network to withstand load power fluctuations.

[0146]

[0147] Where N represents the total number of branches in the power grid; S l,t S is the complex power injected into the l-th branch at time t; lmax The maximum permissible complex power injected into the l-th branch.

[0148] 3. Constraints (Second Constraints):

[0149] Electric vehicle constraints:

[0150] Power flow constraints are created separately based on the network structure at different reconfiguration time periods. For power flow constraints corresponding to the same network structure, see Distribution Network Reconfiguration.

[0151] Furthermore, in one embodiment, step 103 is followed by: verifying the effectiveness of the proposed method by comparing the final joint optimization results with the single optimization results.

[0152] The following is an analysis of the example:

[0153] To demonstrate the validity of this application, this section uses a 33-node standard system as a case study for simulation verification. The system contains 33 nodes, 32 segmented branches, and 5 connecting branches. All nodes are divided into regions according to H / W / O areas, as shown in the structure below. Figure 3 As shown. The active power load of the system nodes during the simulation period is as follows: Figure 4 As shown.

[0154] Based on the system's active power load, the 33-node system was first dynamically reconfigured, with reconfiguration intervals of [1-33] and [34-96]. The reconfiguration results are as follows: Figures 5-9 As shown:

[0155] The reconstructed system operated with different network structures in two time periods. Based on the comparison of the average node voltage before and after reconstruction, the nodes with higher voltage values ​​in the residential area of ​​the system during the reconstructed time period [1-33] were 19, 20, 26, 29, and 30. The nodes with lower voltage values ​​in the residential area of ​​the system during the reconstructed time period [34-96] were 22, 32, and 33. The nodes with higher voltage values ​​in the working area of ​​the system during the reconstructed time period [34-96] were 7, 9, 11, 12, and 13.

[0156] Since electric vehicles connect to the grid in either work areas or residential areas, based on the time interval of the reconfiguration interval and the results of Monte Carlo simulation, the connection time and the period of receiving charge / discharge regulation in work areas are almost all within the time interval [34-96]. However, the period of receiving charge / discharge regulation in residential areas is distributed across two time intervals. Due to the user's expected SOC of the electric vehicle at the time of disconnection, the total charge / discharge load of the electric vehicle during the regulation period will exhibit a "load" attribute. To further reduce the adverse impact of charging load on the system node voltage under reasonable regulation optimization, the average system node voltage after reconfiguration is used to guide electric vehicles to select charging / battery swapping stations with higher average node voltages for connection. Therefore, electric vehicles connected to the grid in work areas connect to nodes 7, 9, and 12 sequentially based on their arrival time in the work area and the number of charging / battery swapping stations included in each node. Since the regulation periods for electric vehicles connected to the grid in residential areas are distributed across two time periods, and the load value is higher during the [34-96] time period, the characteristic of electric vehicles acting as a "source" is utilized. Based on the order in which electric vehicles connect to the grid, they are guided to connect to nodes with lower voltage values ​​during the [34-96] time period to amplify the voltage, and to connect to nodes with higher voltage values ​​during the [1-33] time period to minimize the voltage reduction effect of charging load. Therefore, electric vehicles receiving regulation in residential areas are guided to connect to nodes 22, 33, 29, 26, and 19 in chronological order.

[0157] Before optimizing electric vehicle charging and discharging, travel characteristics of 200 electric vehicles were first generated using random sampling via the Monte Carlo method. Then, the SOC (State of Charge) upon arrival at the work area was used to determine whether to select the work area for charging and discharging regulation. The simulation of relevant travel characteristics is as follows: Figures 10-16 As shown. The SOC upon arrival at the working area is calculated based on the initial SOC, HW driving time, average driving speed, and power consumption per unit mileage. The user's willingness to accept regulation is determined based on the SOC. The arrival time at each area and the time period for connecting to the power grid and accepting regulation can be calculated based on the departure time, driving time, and dwell time.

[0158] Based on the reconstructed network structure and electric vehicle travel parameters, a reconstructed mathematical model for electric vehicle charging and discharging optimization was created and solved. The final optimization results and comparison with the reconstructed voltage are shown below. Figures 17-20 As shown (positive values ​​represent charging behavior, negative values ​​represent discharging behavior):

[0159] Based on the optimization of dynamic reconfiguration, the electric vehicle charging and discharging strategy was further optimized. Due to the result of electric vehicles discharging during peak hours and charging during off-peak hours, the net load of the system at peak times is reduced, the power margin of branch transmission is increased, the overall load is more balanced, the network flexibility of the system is improved, and the ability to cope with load power fluctuations is enhanced. The network loss after joint optimization is also reduced from 6.6102MW under the original structure to 4.4540MW. In terms of voltage quality, due to the low active load of the system during the period [1-33], the electric vehicle regulation is mainly based on charging. The average node voltage after joint optimization is lower than that after reconfiguration. However, due to the guiding effect of charging and swapping stations before regulation, the voltage after regulation during the period [1-33] is still significantly higher than the original voltage. During the period [34-96], due to the discharge of electric vehicles, the average node voltage after regulation is slightly higher than that after reconfiguration. In addition, the average voltage is also higher during the period when the original load is high. Data shows that after joint optimization, the average minimum node voltage value across all time periods is 0.9648 (per unit), a 1.7% improvement compared to the original 0.9486. Along with the increased voltage margin, the node flexibility of the distribution network also improves. Overall, after dual optimization, the system's network losses, voltage, and branch power transmission balance have all significantly improved compared to the original state. The flexibility and security of the distribution network have also been enhanced.

[0160] To further verify the effect of the dual optimization, the charging and discharging of electric vehicles was optimized on the original network structure. Electric vehicles were uniformly and randomly connected to charging and battery swapping stations. Simulation results are as follows: Figures 21-22 As shown:

[0161] The voltage comparison results show that the jointly optimized voltage level is significantly improved compared to the single-electric vehicle charging and discharging optimization in both time periods [1-33] and [34-96]. The average minimum node voltage value in each time period is 1.8% higher than that of the single-electric vehicle optimization (0.9475). Furthermore, the network loss after single-electric vehicle optimization is 6.2418 MW. In summary, single-electric vehicle scheduling cannot provide good optimization results in terms of voltage margin and network loss.

[0162] This application leverages the characteristics and advantages of both distribution network reconfiguration optimization and electric vehicle (EV) charging / discharging optimization to improve the flexibility and security of the distribution network by optimizing network losses, voltage margin, and branch power transmission balance. It achieves joint optimization of distribution network reconfiguration and EV charging / discharging strategies. Compared to traditional dynamic distribution network reconfiguration that only considers the orderly scheduling of EV charging loads, this application employs a joint optimization method: first, dynamic reconfiguring the distribution network to determine the network structure for different time periods within a day; then, optimizing the EV charging / discharging strategy. This approach fully utilizes the characteristics of each method's adjustment time scale and achieves complementary advantages. Furthermore, it uses the voltage situation after reconfiguration to provide a basis for selecting charging / swapping stations. Additionally, it considers the EV travel chain during the EV charging / discharging optimization phase, making it more closely aligned with actual conditions. The final optimization results demonstrate that this method is significant for improving the flexibility and ensuring the safe and stable operation of the distribution network.

[0163] The above is an electric vehicle charging and discharging strategy optimization method provided in the embodiments of this application. The following is an electric vehicle charging and discharging strategy optimization system provided in the embodiments of this application.

[0164] Please see Figure 23 The electric vehicle charging and discharging strategy optimization system provided in this application includes:

[0165] The reconfiguration unit 201 is used to determine the target system for joint optimization. It constructs a mathematical optimization model for dynamic reconfiguration of the distribution network based on the load data of the target system for several time periods within a day and solves the model to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include the reconfigured network structure and the reconfigured grid voltage status.

[0166] Sampling unit 202 is used to create a probability distribution function of the travel characteristics of electric vehicles within a day based on the travel chain theory, and to obtain the travel chain parameters of electric vehicles within a day by random sampling using the Monte Carlo method according to each probability distribution function.

[0167] Analysis unit 203 is used to determine the areas where electric vehicles can be connected to the grid based on travel chain parameters, and to determine the charging and battery swapping stations for electric vehicles based on the reconstructed grid voltage conditions.

[0168] Modeling unit 204 is used to construct an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and travel chain parameters, and solve it to obtain the joint optimization result.

[0169] Furthermore, this application embodiment also provides an electric vehicle charging and discharging strategy optimization device, the device including a processor and a memory:

[0170] The memory is used to store program code and transmit the program code to the processor;

[0171] The processor is used to execute the steps of the electric vehicle charging and discharging strategy optimization method as described in the above method embodiments, according to the instructions in the program code.

[0172] Furthermore, this application embodiment also provides a computer-readable storage medium for storing program code, which is used to execute the electric vehicle charging and discharging strategy optimization method described in the above method embodiment.

[0173] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0174] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0175] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0180] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the charging and discharging strategy of an electric vehicle, characterized in that, include: The target system for joint optimization is determined. A mathematical optimization model for dynamic reconfiguration of the distribution network is constructed based on the load data of the target system for several time periods within a day and the model is solved to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include: the reconfigured network structure and the reconfigured grid voltage status. Based on the travel chain theory, a probability distribution function for the travel characteristics of electric vehicles within a day is created. The travel chain parameters of electric vehicles within a day are obtained by random sampling using the Monte Carlo method according to the probability distribution function. The regions where electric vehicles can be connected to the grid are determined based on the travel chain parameters, and the charging and battery swapping stations for electric vehicles are determined based on the reconstructed grid voltage conditions. Based on the reconstructed network structure and the travel chain parameters, an electric vehicle charging and discharging strategy optimization model is constructed and solved to obtain the jointly optimized result.

2. The electric vehicle charging and discharging strategy optimization method according to claim 1, characterized in that, The mathematical optimization model for dynamic reconfiguration of the distribution network, constructed based on load data from several time periods within a day of the target system, includes: Using the maximum sum of voltage margins of load nodes as the objective function and determining the first constraint, a mathematical optimization model for dynamic reconfiguration of the distribution network is constructed based on historical load data or predicted load data of the target system for several time periods within a day.

3. The electric vehicle charging and discharging strategy optimization method according to claim 2, characterized in that, The first set of constraints includes: node power balance constraints, branch power flow constraints, node voltage constraints, branch capacity constraints, generator power constraints, and network structure constraints.

4. The electric vehicle charging and discharging strategy optimization method according to claim 1, characterized in that, The travel characteristics include: the user's first trip time, the travel time between different areas, the stay time in each area, and the real-time SOC of the electric vehicle.

5. The electric vehicle charging and discharging strategy optimization method according to claim 4, characterized in that, The step of obtaining the travel chain parameters of electric vehicles within a day by random sampling using the Monte Carlo method based on the probability distribution functions includes: Based on the probability distribution function of the user's first trip time, the first trip time of each electric vehicle is sampled and generated; Based on the probability distribution function of driving time and initial SOC between the aforementioned zones, the initial SOC and first-trip driving time of the electric vehicle are sampled and generated. Based on the travel chain structure, the probability distribution function of the travel time between the regions and the dwell time in each region, the travel time of electric vehicles between the regions and the dwell time in each region are randomly sampled and generated. The SOC of each electric vehicle when it arrives at zone W is calculated based on the initial SOC of the electric vehicle generated by sampling, the first trip driving time, the average driving speed, and the power consumption per unit mileage. Based on the sampled travel time of the electric vehicles between different areas and the dwell time in each area, the arrival time of the electric vehicles in each area is calculated.

6. The electric vehicle charging and discharging strategy optimization method according to claim 1, characterized in that, The step of constructing an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and the travel chain parameters includes: Using the minimum power balance of branch transmission as the objective function and determining the second constraint, an optimization model for electric vehicle charging and discharging strategies is constructed based on the reconstructed network structure and the travel chain parameters.

7. The electric vehicle charging and discharging strategy optimization method according to claim 6, characterized in that, The second constraint is: In the formula, Let be the charging power and discharging power of the nth electric vehicle at time t, respectively. These are the upper and lower limits of electric vehicle charging power and discharging power, respectively. The charging / discharging indicator for electric vehicles has a value between 0 and 1; T n,in T n,out For the registration and deregistration times of electric vehicles; State of Charge (SOC) of an electric vehicle when it is off-grid; SOC wash This represents the expected SOC when the network is offline.

8. An electric vehicle charging and discharging strategy optimization system, characterized in that, include: The reconfiguration unit is used to determine the target system for joint optimization, construct a mathematical optimization model for dynamic reconfiguration of the distribution network based on the load data of the target system for several time periods within a day, and solve it to obtain the dynamic reconfiguration results for several time periods within a day. The dynamic reconfiguration results include: the reconfigured network structure and the reconfigured grid voltage status. The sampling unit is used to create a probability distribution function of the travel characteristics of electric vehicles within a day based on the travel chain theory, and to obtain the travel chain parameters of electric vehicles within a day by random sampling using the Monte Carlo method according to the probability distribution function. The analysis unit is used to determine the area where electric vehicles can enter the grid based on the travel chain parameters, and to determine the charging and battery swapping stations for electric vehicles based on the reconstructed grid voltage conditions. The modeling unit is used to construct an electric vehicle charging and discharging strategy optimization model based on the reconstructed network structure and the travel chain parameters, and solve it to obtain the jointly optimized result.

9. An electric vehicle charging and discharging strategy optimization device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the electric vehicle charging and discharging strategy optimization method according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the electric vehicle charging and discharging strategy optimization method according to any one of claims 1-7.

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