A method, system, device, and medium for electric vehicle control based on power distribution networks.

Abstract

This invention discloses a method, system, device, and medium for electric vehicle (EV) control based on a power distribution network. By responding to received power difference values ​​from the power distribution network, the power state of the distribution network is determined. When the power state is excessive, the operating data of multiple initial charging stations corresponding to the power distribution network are acquired. Based on the initial charging station operating data and the power difference values, multiple initial EVs corresponding to the power distribution network are determined. Finally, based on preset EV constraints and the initial EVs, a particle swarm optimization algorithm is used to determine the target EVs corresponding to the power distribution network. By using EVs as the specific implementation object for flexible control on the distribution network side of the new power system, optimized scheduling of the power distribution network is achieved. Combined with preset EV constraints, the normal operation of EVs is ensured.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle control technology based on power distribution networks, and in particular to a method, system, device and medium for electric vehicle control based on power distribution networks. Background Technology

[0002] With the continuous development of modern society and economy, topics such as environmental protection and energy conservation have gradually received widespread attention from the international community, and countries around the world are moving towards the goal of carbon peaking and carbon neutrality. The current structure of the power system is also gradually changing. In order to adapt to the emergence of various intelligent electrical devices and the promotion and use of various distributed new energy power generation technologies, a new type of power system is gradually taking shape.

[0003] Against this backdrop, the widespread adoption of distributed renewable energy generator sets has led to their more extensive and dispersed distribution. Furthermore, as the proportion of these distributed renewable energy sources directly connected to the distribution network gradually increases, the difficulty of regulating the distribution network also grows. In addition to various distributed renewable energy generator sets, the uncertainties surrounding the rapidly developing electric vehicle charging system also pose a significant challenge to distribution network regulation.

[0004] To reduce the impact of the uncertainty of electric vehicle charging on the regulation of the power distribution network, existing electric vehicle regulation methods based on the power distribution network optimize the scheduling of the power distribution network by setting up energy storage devices at electric vehicle charging stations. However, this requires the transformation of existing charging stations, increases construction costs, takes a long time, and results in low scheduling efficiency. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for electric vehicle control based on power distribution networks. It solves the technical problem that existing methods for controlling electric vehicles based on power distribution networks, which achieve optimized scheduling of power distribution networks by setting up energy storage devices at electric vehicle charging stations, require the modification of existing charging stations, increase construction costs, take a long time, and result in low scheduling efficiency.

[0006] This invention provides a method for controlling electric vehicles based on a power distribution network, comprising:

[0007] The system responds to the received power difference in the distribution network and determines the power state of the distribution network based on the power difference.

[0008] When the power state is a power surplus state, obtain the operating data of multiple initial charging stations corresponding to the power distribution network;

[0009] Based on the initial charging station operation data and the power difference of the distribution network, multiple initial electric vehicles corresponding to the distribution network are determined;

[0010] Based on preset electric vehicle constraints and the initial electric vehicle, the target electric vehicle corresponding to the power distribution network is determined using a particle swarm optimization algorithm.

[0011] Optionally, the step of determining the multiple initial electric vehicles corresponding to the distribution network based on the initial charging station operating data and the power difference of the distribution network includes:

[0012] Based on the power difference of the distribution network, the selection threshold corresponding to the distribution network is determined;

[0013] Based on the initial charging station operation data, a greedy algorithm is used to select the electric vehicles corresponding to the power distribution network and the number of selections is counted in real time.

[0014] Remove the operating data corresponding to the electric vehicle from the initial charging station operating data, and generate the target charging station operating data corresponding to the power distribution network;

[0015] When the number of selections equals the selection threshold, all the electric vehicles selected at the current moment are taken as the multiple initial electric vehicles corresponding to the power distribution network.

[0016] When the number of selections is not equal to the selection threshold, the target charging station operation data is used as the initial charging station operation data, and the process jumps to the step of selecting the electric vehicle corresponding to the power distribution network based on the initial charging station operation data using a greedy algorithm and counting the number of selections in real time.

[0017] Optionally, the initial charging station operation data includes charging station capacity, charging pile charging power, electric vehicle charging power, and current electric vehicle battery level; the step of selecting the electric vehicles corresponding to the power distribution network using a greedy algorithm based on the initial charging station operation data and counting the number of selections in real time includes:

[0018] Based on the charging station capacity, a greedy algorithm is used to select the charging station with the largest capacity in the power distribution network.

[0019] Based on the charging power of multiple charging piles corresponding to the charging station, a greedy algorithm is used to select the bidirectional charging pile with the highest charging power in the charging station.

[0020] Based on the bidirectional charging pile, a greedy algorithm is used to select the first electric vehicle with the highest charging power corresponding to the charging station.

[0021] A greedy algorithm is used to select the first electric vehicle with the largest absorption capacity among all the first electric vehicles corresponding to the power distribution network as the second electric vehicle;

[0022] The second electric vehicle is designated as the electric vehicle corresponding to the power distribution network, and the number of selections is counted in real time.

[0023] Optionally, the step of determining the target electric vehicle corresponding to the distribution network using a particle swarm optimization algorithm based on preset electric vehicle constraints and the initial electric vehicle includes:

[0024] Based on the preset electric vehicle constraints and the initial electric vehicle, determine the multiple intermediate electric vehicles corresponding to the power distribution network;

[0025] All the aforementioned intermediate electric vehicles are considered as an individual population;

[0026] Calculate the initial individual fitness value for each initial population individual within the individual population;

[0027] Based on the initial individual fitness value, the particle swarm optimization algorithm is used to obtain multiple target population individuals corresponding to the individual population.

[0028] The intermediate electric vehicles corresponding to the individuals in the target population are taken as the target electric vehicles corresponding to the power distribution network.

[0029] Optionally, the preset electric vehicle constraints include battery capacity constraints and charging / discharging power constraints; the step of determining multiple intermediate electric vehicles corresponding to the distribution network based on the preset electric vehicle constraints and the initial electric vehicle includes:

[0030] Obtain the battery capacity and charging / discharging power corresponding to the initial electric vehicle;

[0031] The initial electric vehicle whose battery capacity and charging / discharging power respectively meet the battery capacity constraint and the charging / discharging power constraint is used as the intermediate electric vehicle corresponding to the power distribution network.

[0032] Optionally, the step of obtaining multiple target population individuals corresponding to the individual population using the particle swarm optimization algorithm based on the initial individual fitness value includes:

[0033] The initial particle velocities of the individuals in the initial population are updated using preset velocity update formulas to generate corresponding target particle velocities.

[0034] The initial particle positions corresponding to the individuals in the initial population are updated using preset position update formulas to generate corresponding target particle positions.

[0035] The target particle velocity and the target particle position are compared with the corresponding boundary thresholds, and the initial individual fitness value is updated based on the comparison results to generate the target individual fitness value.

[0036] Based on the difference between the fitness value of the target individual and the corresponding fitness value of the initial individual, intermediate population individuals are determined and the number of iterations is counted in real time.

[0037] When the number of iterations and the fitness value of the target individual meet the preset iteration threshold, all the intermediate population individuals at the current moment are taken as the target population individuals corresponding to the individual population.

[0038] When the number of iterations and the fitness value of the target individual do not meet the preset iteration threshold, the target particle velocity and the target particle position are respectively used as the initial particle velocity and the initial particle velocity, and the process jumps to execute the step of calculating the initial individual fitness value corresponding to each initial population individual in the individual population.

[0039] Optionally, the method further includes:

[0040] When the power status is in a state of deficiency, a greedy algorithm is used to obtain the target electric vehicle corresponding to the power distribution network based on the initial charging station operation data.

[0041] The present invention also provides an electric vehicle control system based on a power distribution network, comprising:

[0042] A power status determination module is used to respond to the received power difference in the distribution network and determine the power status of the distribution network based on the power difference in the distribution network.

[0043] The initial charging station operation data acquisition module is used to acquire the operation data of multiple initial charging stations corresponding to the power grid when the power state is in an excess state.

[0044] The initial electric vehicle determination module is used to determine multiple initial electric vehicles corresponding to the distribution network based on the initial charging station operation data and the power difference of the distribution network.

[0045] The target electric vehicle determination module is used to determine the target electric vehicle corresponding to the power distribution network based on preset electric vehicle constraints and the initial electric vehicle, using a particle swarm optimization algorithm.

[0046] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs an electric vehicle control method based on a power distribution network as described above.

[0047] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements any of the above-described electric vehicle control methods based on a power distribution network.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] This invention determines the power state of a distribution network based on the received power difference. When the power state is excessive, it acquires the operating data of multiple initial charging stations corresponding to the distribution network and determines multiple initial electric vehicles (EVs) corresponding to the distribution network based on the initial charging station operating data and the power difference. Finally, based on preset EV constraints and the initial EVs, a particle swarm optimization algorithm is used to determine the target EVs corresponding to the distribution network. This solves the technical problem of existing EV control methods based on distribution networks, which require the modification of existing charging stations to achieve optimized distribution network scheduling by setting up energy storage devices at charging stations, increasing construction costs, and taking a long time, resulting in low scheduling efficiency. By using EVs as the specific implementation object for flexible control on the distribution network side of the new power system, it achieves optimized distribution network scheduling and ensures the normal operation of EVs by combining preset EV constraints. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating the steps of an electric vehicle control method based on a power distribution network, provided in Embodiment 1 of the present invention;

[0052] Figure 2 This is a flowchart illustrating the steps of an electric vehicle control method based on a power distribution network, provided in Embodiment 2 of the present invention.

[0053] Figure 3 The flowchart for selecting target electric vehicles corresponding to the power distribution network using the particle swarm optimization algorithm is provided in Embodiment 2 of the present invention.

[0054] Figure 4 This is a flowchart of obtaining the target electric vehicle corresponding to the power distribution network according to Embodiment 2 of the present invention;

[0055] Figure 5 This is a flowchart illustrating the connection relationship between an electric vehicle, a bidirectional charging pile, a car charging station, and a power distribution network, as provided in Embodiment 2 of the present invention.

[0056] Figure 6 This is a structural block diagram of an electric vehicle control system based on a power distribution network, provided in Embodiment 3 of the present invention. Detailed Implementation

[0057] This invention provides a method, system, device, and medium for electric vehicle control based on a power distribution network. It addresses the technical problem that existing methods for controlling electric vehicles based on power distribution networks, which optimize power distribution network scheduling by installing energy storage devices at charging stations, require modifications to existing charging stations, increasing construction costs and time, resulting in low scheduling efficiency.

[0058] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of an electric vehicle control method based on a power distribution network, as provided in Embodiment 1 of the present invention.

[0060] This invention provides a method for controlling electric vehicles based on a power distribution network, comprising:

[0061] Step 101: Response to the received power difference of the distribution network, and determine the corresponding power state of the distribution network based on the power difference of the distribution network.

[0062] The power difference in the distribution network refers to the difference between the current power of the distribution network and the preset power of the distribution network.

[0063] In this embodiment of the invention, the power difference in the distribution network is compared with a preset difference threshold, where the preset difference threshold is a critical value used to determine the power state type of the distribution network. When the power difference in the distribution network is greater than the preset difference threshold, the distribution network is in a power surplus state. When the power difference in the distribution network is less than the preset difference threshold, the distribution network is in a power deficit state.

[0064] Step 102: When the power state is in excess, obtain the operating data of multiple initial charging stations corresponding to the distribution network.

[0065] In this embodiment of the invention, when the power difference of the distribution network is greater than a preset difference threshold, the distribution network is in a state of power surplus. The distribution network operation data corresponding to the power difference is obtained, and multiple initial charging station operation data are extracted from the distribution network operation data.

[0066] Step 103: Based on the initial charging station operation data and the power difference of the distribution network, determine the multiple initial electric vehicles corresponding to the distribution network.

[0067] In this embodiment of the invention, a selection threshold corresponding to the distribution network is determined based on the power difference of the distribution network. The selection threshold refers to a critical value corresponding to the number of selections set based on the actual situation of the distribution network. Based on the initial charging station operation data, a greedy algorithm is used to select the initial electric vehicles corresponding to the distribution network and the selection count is counted in real time. The operation data corresponding to the electric vehicles in the initial charging station operation data is removed to generate the target charging station operation data. When the selection count equals the selection threshold, all electric vehicles selected at the current moment are used as multiple initial electric vehicles corresponding to the distribution network. When the selection count does not equal the selection threshold, the target charging station operation data is used as the initial charging station operation data, and the process jumps to execute the step of selecting the initial electric vehicles corresponding to the distribution network using a greedy algorithm based on the initial charging station operation data and counting the selection count in real time.

[0068] Step 104: Based on the preset electric vehicle constraints and the initial electric vehicles, the particle swarm optimization algorithm is used to determine the target electric vehicles corresponding to the distribution network.

[0069] In this embodiment of the invention, multiple intermediate electric vehicles corresponding to the distribution network are determined based on preset electric vehicle constraints and initial electric vehicles. All intermediate electric vehicles are treated as an individual population, and the initial individual fitness value corresponding to each initial population individual within each individual population is calculated. Based on the initial individual fitness values, a particle swarm optimization algorithm is used to obtain multiple target population individuals corresponding to the individual population. The intermediate electric vehicles corresponding to the target population individuals are used as the target electric vehicles corresponding to the distribution network.

[0070] In this embodiment of the invention, the power state of the distribution network is determined based on the received power difference in response to the distribution network. When the power state is in an excess state, the operating data of multiple initial charging stations corresponding to the distribution network are acquired, and multiple initial electric vehicles corresponding to the distribution network are determined based on the initial charging station operating data and the power difference in the distribution network. Finally, based on preset electric vehicle constraints and the initial electric vehicles, a particle swarm optimization algorithm is used to determine the target electric vehicles corresponding to the distribution network. This solves the technical problem that existing electric vehicle control methods based on the distribution network require the modification of existing charging stations to achieve optimized scheduling of the distribution network by setting up energy storage devices at charging stations, which increases construction costs, takes a long time, and results in low scheduling efficiency. By using electric vehicles as the specific implementation object for flexible control on the distribution network side of the new power system, optimized scheduling of the distribution network is achieved, and the normal operation of electric vehicles is guaranteed by combining preset electric vehicle constraints.

[0071] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of an electric vehicle control method based on a power distribution network, as provided in Embodiment 2 of the present invention.

[0072] Another method for controlling electric vehicles based on a power distribution network provided by the present invention includes:

[0073] Step 201: Response to the received power difference of the distribution network, and determine the corresponding power state of the distribution network based on the power difference of the distribution network.

[0074] In this embodiment of the invention, the power difference of the distribution network is determined based on the operation data of the distribution network, and the regulation of electric vehicles is triggered by the power difference. When the power difference of the distribution network is received, the power difference is compared with a preset difference threshold to determine whether the power state of the distribution network is a power surplus state or a power shortage state.

[0075] Step 202: When the power state is in excess, obtain the operating data of multiple initial charging stations corresponding to the distribution network.

[0076] In this embodiment of the invention, if the power state corresponding to the distribution network is a power surplus state, then the initial charging station operation data corresponding to all charging stations in the distribution network under this state are obtained.

[0077] Step 203: Based on the initial charging station operation data and the power difference of the distribution network, determine the multiple initial electric vehicles corresponding to the distribution network.

[0078] Furthermore, step 203 may include the following sub-steps S11-S15:

[0079] S11. Based on the power difference of the distribution network, determine the selection threshold corresponding to the distribution network.

[0080] S12. Based on the initial charging station operation data, a greedy algorithm is used to select the initial electric vehicles corresponding to the power distribution network and the number of selections is counted in real time.

[0081] S13. Remove the operating data corresponding to electric vehicles from the initial charging station operating data and generate the target charging station operating data corresponding to the power distribution network.

[0082] S14. When the number of selections equals the selection threshold, all electric vehicles selected at the current moment are used as multiple initial electric vehicles corresponding to the distribution network.

[0083] S15. When the number of selections is not equal to the selection threshold, the target charging station operation data is used as the initial charging station operation data, and the process jumps to execute the step of selecting the initial electric vehicle corresponding to the distribution network based on the initial charging station operation data using a greedy algorithm and counting the number of selections in real time.

[0084] In this embodiment of the invention, the selection threshold corresponding to the current selection count is determined by the power difference of the distribution network. A greedy algorithm is used to select the target electric vehicles corresponding to the distribution network based on the initial charging station operating data for each charging station, and the selection count is counted in real time. For each selected electric vehicle, all operating data related to that electric vehicle in the initial charging station operating data is removed, and the resulting initial charging station operating data is used as the target charging station operating data for the next selection. It is determined whether the current selection count equals the selection threshold. If so, all electric vehicles selected at the current time are considered as multiple initial electric vehicles corresponding to the distribution network. If not, the target charging station operating data is used as the initial charging station operating data, and the process jumps to the step of selecting the initial electric vehicles corresponding to the distribution network using a greedy algorithm based on the initial charging station operating data and counting the selection count in real time.

[0085] Furthermore, the initial charging station operation data includes the charging station capacity, charging pile charging power, electric vehicle charging power, and the current battery level of the electric vehicle. Step S12 may include the following sub-steps S121-S125:

[0086] S121. Based on the charging station capacity, a greedy algorithm is used to select the charging station with the largest capacity in the distribution network.

[0087] S122. Based on the charging power of multiple charging piles corresponding to the charging station, a greedy algorithm is used to select the bidirectional charging pile with the largest charging power in the charging station.

[0088] S123. Based on bidirectional charging piles, a greedy algorithm is used to select the first electric vehicle with the highest charging power corresponding to the charging station.

[0089] S124. Using a greedy algorithm, select the first electric vehicle with the largest absorption capacity among all the first electric vehicles corresponding to the distribution network as the second electric vehicle.

[0090] S125. The second electric vehicle is designated as the electric vehicle corresponding to the power distribution network, and the number of selections is counted in real time.

[0091] In this embodiment of the invention, based on the charging station capacity corresponding to each charging station within the distribution network, a greedy algorithm is used to select the charging station with the largest capacity within the distribution network. The charging power of each bidirectional charging pile within the charging station is obtained, and a greedy algorithm is used to select the bidirectional charging pile with the highest charging power within the charging station. A greedy algorithm is then used to select the first electric vehicle within the charging station with the highest charging power that can be used in conjunction with this bidirectional charging pile. Finally, a greedy algorithm is used to select the first electric vehicle with the largest absorption capacity from all the first electric vehicles corresponding to the distribution network as the second electric vehicle, i.e., the electric vehicle with the lowest current battery level. The selected second electric vehicle is then designated as the electric vehicle corresponding to the distribution network, and the number of selections is counted in real time.

[0092] Step 204: Based on the preset electric vehicle constraints and the initial electric vehicle, the particle swarm algorithm is used to determine the target electric vehicle corresponding to the distribution network.

[0093] Furthermore, step 204 may include the following sub-steps S21-S25:

[0094] S21. Based on the preset electric vehicle constraints and the initial electric vehicle, determine the multiple intermediate electric vehicles corresponding to the distribution network.

[0095] S22. Treat all intermediate electric vehicles as an individual population.

[0096] S23. Calculate the initial individual fitness value for each initial population individual within the individual population.

[0097] S24. Based on the initial individual fitness value, the particle swarm optimization algorithm is used to obtain multiple target population individuals corresponding to the individual population.

[0098] S25. The intermediate electric vehicles corresponding to the target population individuals are taken as the target electric vehicles corresponding to the distribution network.

[0099] In this embodiment of the invention, based on preset electric vehicle constraints and initial electric vehicles, multiple intermediate electric vehicles corresponding to the distribution network are determined, and an individual population corresponding to the distribution network is constructed using all intermediate electric vehicles. The initial individual fitness value corresponding to each initial population individual within the individual population is calculated using a preset fitness calculation formula. Based on the initial individual fitness values ​​corresponding to each initial population individual, a particle swarm optimization algorithm is used to obtain multiple target population individuals corresponding to the individual population, and the intermediate electric vehicles corresponding to the target population individuals are used as the target electric vehicles corresponding to the distribution network.

[0100] Furthermore, the preset electric vehicle constraints include battery capacity constraints and charging / discharging power constraints. Step S21 may include the following sub-steps S211-S212:

[0101] S211. Obtain the battery charge and charging / discharging power corresponding to the initial electric vehicle.

[0102] S212. The initial electric vehicle whose battery capacity and charging / discharging power respectively meet the battery capacity constraint and charging / discharging power constraint is taken as the intermediate electric vehicle corresponding to the distribution network.

[0103] In this embodiment of the invention, the electric vehicle serves as a means of transportation, but when idle, its battery, which simultaneously charges and discharges, can participate in the flexible regulation of the power distribution network. To enable electric vehicles to participate in power distribution network regulation, the following aspects need to be considered:

[0104] (1) Battery SOC of electric vehicles EV Constraints. Since electric vehicles are intended to facilitate travel, they must first and foremost maintain their function as a means of transportation. Therefore, a threshold needs to be set to ensure that electric vehicles have a certain basic range. Here, the State of Charge (SOC) of the electric vehicle's battery is set. EV Constraints:

[0105]

[0106] Since the driving range of commonly used urban family electric vehicles on the market is 120km, the minimum discharge limit is stipulated to be 30% of the battery's rated capacity. 30% of a 120km driving range is 36km. This 36km figure can cover the commuting distance of most urban commuters, ensuring the functionality of electric vehicles as a means of transportation.

[0107] (2) The charging and discharging power P of electric vehicles EV Constraints. The charging and discharging power of an electric vehicle should take into account the power limitations of its own battery and the power between interconnects. The charging and discharging power P is set here. EV Constraints:

[0108] P EV <P 波动max

[0109] Where: P 波动max This indicates the maximum power range that can be accepted during the charging and discharging of an electric vehicle's battery.

[0110] Therefore, the initial electric vehicle whose battery capacity and charging / discharging power satisfy the battery capacity constraint and charging / discharging power constraint respectively is regarded as the intermediate electric vehicle corresponding to the distribution network.

[0111] Furthermore, the preset electric vehicle constraints include battery capacity constraints and charging / discharging power constraints. Step S24 may include the following sub-steps S241-S246:

[0112] S241. Update the initial particle velocity of each individual in the initial population using a preset velocity update formula, and generate the corresponding target particle velocity.

[0113] S242. Update the initial particle positions corresponding to the individuals in the initial population using the preset position update formula, and generate the corresponding target particle positions.

[0114] S243. Compare the target particle velocity and target particle position with the corresponding boundary thresholds respectively, and update the initial individual fitness value based on the comparison results to generate the target individual fitness value.

[0115] S244. Based on the difference between the fitness value of the target individual and the fitness value of the corresponding initial individual, determine the intermediate population individuals and count the number of iterations in real time.

[0116] S245. When the number of iterations and the fitness value of the target individual meet the preset iteration threshold, all intermediate population individuals at the current moment are taken as the target population individuals corresponding to the individual population.

[0117] S246. When the number of iterations and the fitness value of the target individual do not meet the preset iteration threshold, the target particle velocity and the target particle position are used as the initial particle velocity and the initial particle velocity, respectively, and the process jumps to the step of calculating the initial individual fitness value corresponding to each initial population individual in the individual population.

[0118] In this embodiment of the invention, the charging cost for the i-th electric vehicle is expressed as:

[0119]

[0120] Where: C i The cost incurred when charging the i-th vehicle; T begin The start time of charging; T end The end time of charging; P i The power output when charging the i-th vehicle; C t Let t be the time-of-use electricity price for period t. Find the minimum cost (minC) for charging all electric vehicle users. all The optimization objective, i.e., the preset fitness calculation formula, is:

[0121]

[0122] The corresponding constraints are:

[0123] P i <P波动max

[0124] That is, the power of the i-th vehicle during charging should be within the maximum fluctuation range of the rated power.

[0125] The particle swarm optimization (PSO) algorithm is used here for optimization. The discrete particle swarm optimization algorithm determines the probability of a binary variable taking the value 1 using a velocity update formula. The velocity update formula for the particle swarm optimization algorithm, i.e., the preset velocity update formula, is as follows:

[0126] v i (t+1)=wv i (t)+c1r1(p i (t)-x i (t))+c2r2(g(t)-x i (t))

[0127] Where: v i (t+1) represents the particle velocity obtained in iteration t+1; w is the inertia weight, typically 0.5 to 0.8; v i (t) represents the particle velocity obtained in the t-th iteration; c1 and c2 are learning factors, usually c1 = c2 = 2; r1 and r2 are random numbers between 0 and 1; p i (t) represents the optimal solution found by particle i at time t; x i g(t) represents the particle position obtained in the t-th iteration; g(t) represents the optimal solution found by the population at time t.

[0128] The position update formula for the particle swarm optimization algorithm, i.e., the preset position update formula, is as follows:

[0129] x i (t+1)=x i (t)+v i (t+1)

[0130] Where: x i (t+1) represents the particle position obtained in the (t+1)th iteration.

[0131] like Figure 3As shown, the process of determining the target electric vehicle corresponding to the distribution network using the particle swarm optimization algorithm is as follows: Initialize the individual population x, which consists of all the intermediate electric vehicles selected from the greedy algorithm in the previous step. Calculate the initial fitness value for each individual within the individual population using a preset fitness calculation formula; the smaller the calculated function value, the higher the fitness value. Update the initial particle velocity for each individual within the initial population using a preset velocity update formula to generate the corresponding target particle velocity. Update the initial particle position for each individual within the initial population using a preset position update formula to generate the corresponding target particle position. After updating the velocity and position, velocity boundary detection and position boundary detection are performed separately. The velocity boundary is the given maximum velocity value, i.e., the maximum distance a particle can move in the next iteration. It is generally set to 10%-20% of the particle's variation range and can be adjusted according to actual conditions. The position boundary is the position of the intermediate electric vehicle within the selected individual population. When the position value obtained from the iterative update exceeds the position of the individual population, it exceeds the position boundary. For particles exceeding the position boundary, a poor fitness value is assigned. The target particle velocity and position are compared with their corresponding boundary thresholds, and the initial individual fitness value is updated based on the comparison results, generating a target individual fitness value. Based on the difference between the target individual fitness value and the corresponding initial individual fitness value, if the target individual fitness value is higher, the current particle position is updated; otherwise, it is not updated. This process determines intermediate population individuals and counts the iteration count in real time. A termination condition is checked: the iteration count and the target individual fitness value must satisfy a preset iteration threshold. The preset iteration threshold is when the iteration count is greater than 1000 or the fitness value no longer changes. If satisfied, all intermediate population individuals at the current moment are designated as target population individuals for their respective populations, and the intermediate electric vehicles corresponding to these target population individuals are designated as target electric vehicles for the power distribution network. Charging is then performed on the selected target electric vehicles. If not satisfied, the target particle velocity and target particle position are used as the initial particle velocity and initial particle position, respectively, and the process jumps to the step of calculating the initial individual fitness value for each initial population individual within the individual population.

[0132] Step 205: When the power status is insufficient, based on the initial charging station operation data, a greedy algorithm is used to obtain the target electric vehicle corresponding to the distribution network.

[0133] In this embodiment of the invention, when the regional power grid dispatch issues a power shortage (A2) control request, i.e., the power state of the distribution network is in a shortage state, the specific execution steps are similar to those in the excess state (A1). However, considering the need for rapid response to the power shortage, the discharge operation is performed immediately after selecting the vehicle to discharge. The steps for obtaining the target electric vehicle corresponding to the distribution network under different power states are as follows: Figure 4As shown in the diagram. A1: indicates excess grid power; A2: indicates insufficient grid power; B1: indicates a charging station with a small installed capacity; B2: indicates a charging station with a large installed capacity; C1: indicates a low-power bidirectional charging pile; C2: indicates a high-power bidirectional charging pile; D1: indicates a low-power electric vehicle; D2: indicates a high-power electric vehicle; E1: indicates an electric vehicle with a low current battery level; E2: indicates an electric vehicle with a high current battery level; i: indicates an electric vehicle selected for charging; G: indicates an electric vehicle prioritized for discharging.

[0134] In this embodiment of the invention, the connection relationship between the electric vehicle, the bidirectional charging pile, the electric vehicle charging station, and the power distribution network is as follows: Figure 5 As shown, the power distribution network is connected to multiple charging stations, enabling electricity purchase and sale between the network and the stations. Each charging station has multiple bidirectional charging piles, allowing charging and discharging between the station and these piles. A single bidirectional charging pile can connect to multiple electric vehicles, allowing charging and discharging between the vehicles and the charging piles.

[0135] By responding to the received power difference in the distribution network, the power state of the distribution network is determined based on the power difference. When the power state is in a power surplus state, the operating data of multiple initial charging stations corresponding to the distribution network are acquired. Based on the operating data of the initial charging stations and the power difference in the distribution network, multiple initial electric vehicles corresponding to the distribution network are determined. Based on the preset electric vehicle constraints and the initial electric vehicles, a particle swarm optimization algorithm is used to determine the target electric vehicle corresponding to the distribution network. When the power state is in a power deficit state, a greedy algorithm is directly used to obtain the target electric vehicle corresponding to the distribution network based on the operating data of the initial charging stations. Considering the above constraints such as power and capacity, the principle of minimizing the time for flexible adjustment is followed as much as possible. The greedy algorithm first selects the electric vehicle with the optimal current capacity and power, i.e., the intermediate electric vehicle; then, among the selected series of intermediate electric vehicles, the particle swarm optimization algorithm is used to select the electric vehicle with the lowest current charging cost for priority charging, so that the charging cost for electric vehicle users is minimized; while the discharging vehicles directly follow the vehicles selected by the greedy algorithm in the previous step to discharge, so as to replenish the power deficit of the grid as quickly as possible. This is how to handle the flexible adjustment of electric vehicles in the distribution network.

[0136] Please see Figure 6 , Figure 6 This is a structural block diagram of an electric vehicle control system based on a power distribution network, provided in Embodiment 3 of the present invention.

[0137] This invention provides an electric vehicle control system based on a power distribution network, comprising:

[0138] The power status determination module 601 is used to respond to the received power difference of the distribution network and determine the corresponding power status of the distribution network based on the power difference.

[0139] The initial charging station operation data acquisition module 602 is used to acquire the operation data of multiple initial charging stations corresponding to the power grid when the power state is in an excess state.

[0140] The initial electric vehicle determination module 603 is used to determine multiple initial electric vehicles corresponding to the distribution network based on the initial charging station operation data and the power difference of the distribution network.

[0141] The first target electric vehicle determination module 604 is used to determine the target electric vehicle corresponding to the distribution network based on preset electric vehicle constraints and initial electric vehicles using a particle swarm algorithm.

[0142] Optionally, the initial electric vehicle determination module 603 includes:

[0143] The threshold selection module is used to determine the selection threshold corresponding to the distribution network based on the power difference of the distribution network.

[0144] The electric vehicle and selection count determination module is used to select the initial electric vehicle corresponding to the power distribution network based on the initial charging station operation data and to count the selection count in real time.

[0145] The target charging station operation data generation module is used to remove the operation data corresponding to electric vehicles from the initial charging station operation data and generate the target charging station operation data corresponding to the power distribution network.

[0146] The initial electric vehicle determination first submodule is used to select all electric vehicles selected at the current moment as multiple initial electric vehicles corresponding to the distribution network when the number of selections equals the selection threshold.

[0147] The initial electric vehicle determination second submodule is used to take the target charging station operation data as the initial charging station operation data when the number of selections is not equal to the selection threshold, and then jump to execute the step of selecting the initial electric vehicle corresponding to the distribution network based on the initial charging station operation data using a greedy algorithm and counting the number of selections in real time.

[0148] Optionally, the initial charging station operating data includes the charging station capacity, charging pile charging power, electric vehicle charging power, and the current battery level of the electric vehicle. The electric vehicle and selection count determination module can perform the following steps:

[0149] Based on the charging station capacity, a greedy algorithm is used to select the charging station with the largest capacity in the distribution network.

[0150] Based on the charging power of multiple charging piles corresponding to a charging station, a greedy algorithm is used to select the bidirectional charging pile with the highest charging power in the charging station.

[0151] Based on bidirectional charging piles, a greedy algorithm is used to select the first electric vehicle with the highest charging power at the corresponding charging station.

[0152] A greedy algorithm is used to select the first electric vehicle with the largest absorption capacity among all the first electric vehicles corresponding to the distribution network as the second electric vehicle.

[0153] The second electric vehicle is designated as the electric vehicle corresponding to the power distribution network, and the number of selections is counted in real time.

[0154] Optionally, the target electric vehicle first determination module 604 includes:

[0155] The intermediate electric vehicle determination module is used to determine multiple intermediate electric vehicles corresponding to the distribution network based on preset electric vehicle constraints and the initial electric vehicles.

[0156] The individual population generation module is used to treat all intermediate electric vehicles as an individual population.

[0157] The initial individual fitness calculation module is used to calculate the initial individual fitness value for each individual in the initial population.

[0158] The target population individual acquisition module is used to acquire multiple target population individuals corresponding to the individual population based on the initial individual fitness value using the particle swarm optimization algorithm.

[0159] The first target electric vehicle determination submodule is used to identify the intermediate electric vehicles corresponding to individuals in the target population as the target electric vehicles corresponding to the distribution network.

[0160] Optionally, the preset electric vehicle constraints include battery capacity constraints and charge / discharge power constraints. The intermediate electric vehicle determination module can perform the following steps:

[0161] Obtain the initial battery capacity and charging / discharging power of the electric vehicle;

[0162] The initial electric vehicle whose battery capacity and charging / discharging power respectively meet the battery capacity constraint and charging / discharging power constraint is used as the intermediate electric vehicle corresponding to the distribution network.

[0163] Optionally, the target population individual acquisition module may perform the following steps:

[0164] The initial particle velocities of individuals in the initial population are updated using preset velocity update formulas to generate corresponding target particle velocities.

[0165] The initial particle positions corresponding to individuals in the initial population are updated using preset position update formulas to generate the corresponding target particle positions.

[0166] The target particle velocity and target particle position are compared with the corresponding boundary thresholds, and the initial individual fitness value is updated based on the comparison results to generate the target individual fitness value.

[0167] Based on the difference between the fitness value of the target individual and the fitness value of the corresponding initial individual, the intermediate population individuals are determined and the number of iterations is counted in real time;

[0168] When the number of iterations and the fitness value of the target individual meet the preset iteration threshold, all intermediate population individuals at the current moment are taken as the target population individuals corresponding to the individual population.

[0169] When the number of iterations and the fitness value of the target individual do not meet the preset iteration threshold, the target particle velocity and the target particle position are used as the initial particle velocity and the initial particle position, respectively, and the process jumps to the step of calculating the initial individual fitness value corresponding to each initial population individual in the individual population.

[0170] Optionally, the system also includes:

[0171] The second target electric vehicle determination module is used to obtain the target electric vehicle corresponding to the distribution network based on the initial charging station operation data when the power state is in a state of insufficient power.

[0172] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the electric vehicle control method based on the power distribution network as described in any of the above embodiments.

[0173] The memory can be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory has storage space for program code used to perform any of the method steps described above. For example, the storage space for program code may include individual program codes for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the electric vehicle control method based on the power distribution network described above.

[0174] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the electric vehicle control method based on a power distribution network as described in any of the above embodiments.

[0175] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[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 through some interfaces, or indirect coupling or communication connection between apparatuses or units, 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 the present invention 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 the present invention, 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 of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 the present invention.

Claims

1. A power distribution network-based electric vehicle regulation method, characterized by, include: The system responds to the received power difference in the distribution network and determines the power state of the distribution network based on the power difference. When the power state is a power surplus state, the operation data of multiple initial charging stations corresponding to the distribution network are obtained; the initial charging station operation data includes the charging station capacity, charging pile charging power, electric vehicle charging power, and electric vehicle current power. Based on the initial charging station operating data and the power difference of the distribution network, multiple initial electric vehicles corresponding to the distribution network are determined, including: Based on the power difference of the distribution network, the selection threshold corresponding to the distribution network is determined; Based on the initial charging station operation data, a greedy algorithm is used to select the electric vehicles corresponding to the power distribution network and the number of selections is counted in real time, including: Based on the charging station capacity, a greedy algorithm is used to select the charging station with the largest capacity in the power distribution network. Based on the charging power of multiple charging piles corresponding to the charging station, a greedy algorithm is used to select the bidirectional charging pile with the highest charging power in the charging station. Based on the bidirectional charging pile, a greedy algorithm is used to select the first electric vehicle with the highest charging power corresponding to the charging station. A greedy algorithm is used to select the first electric vehicle with the largest absorption capacity among all the first electric vehicles corresponding to the power distribution network as the second electric vehicle; The second electric vehicle is designated as the electric vehicle corresponding to the power distribution network, and the number of selections is counted in real time. Remove the operating data corresponding to the electric vehicle from the initial charging station operating data, and generate the target charging station operating data corresponding to the power distribution network; When the number of selections equals the selection threshold, all the electric vehicles selected at the current moment are taken as the multiple initial electric vehicles corresponding to the power distribution network. When the number of selections is not equal to the selection threshold, the target charging station operation data is used as the initial charging station operation data, and the process jumps to the step of selecting the electric vehicle corresponding to the power distribution network based on the initial charging station operation data using a greedy algorithm and counting the number of selections in real time. Based on preset electric vehicle constraints and the initial electric vehicle, the target electric vehicle corresponding to the power distribution network is determined using a particle swarm optimization algorithm.

2. The power distribution grid based electric vehicle regulation method of claim 1, wherein, The step of determining the target electric vehicle corresponding to the distribution network using a particle swarm optimization algorithm based on preset electric vehicle constraints and the initial electric vehicle includes: Based on the preset electric vehicle constraints and the initial electric vehicle, determine the multiple intermediate electric vehicles corresponding to the power distribution network; All the aforementioned intermediate electric vehicles are considered as an individual population; Calculate the initial individual fitness value for each initial population individual within the individual population; Based on the initial individual fitness value, the particle swarm optimization algorithm is used to obtain multiple target population individuals corresponding to the individual population. The intermediate electric vehicles corresponding to the individuals in the target population are taken as the target electric vehicles corresponding to the power distribution network.

3. The electric vehicle control method based on a power distribution network according to claim 2, characterized in that, The preset electric vehicle constraints include battery capacity constraints and charging / discharging power constraints; the step of determining multiple intermediate electric vehicles corresponding to the distribution network based on the preset electric vehicle constraints and the initial electric vehicle includes: Obtain the battery capacity and charging / discharging power corresponding to the initial electric vehicle; The initial electric vehicle whose battery capacity and charging / discharging power respectively meet the battery capacity constraint and the charging / discharging power constraint is used as the intermediate electric vehicle corresponding to the power distribution network.

4. The power distribution grid based electric vehicle regulation method of claim 2, wherein, The step of obtaining multiple target population individuals corresponding to the individual population using the particle swarm optimization algorithm based on the initial individual fitness value includes: The initial particle velocities of the individuals in the initial population are updated using preset velocity update formulas to generate corresponding target particle velocities. The initial particle positions corresponding to the individuals in the initial population are updated using preset position update formulas to generate corresponding target particle positions. The target particle velocity and the target particle position are compared with the corresponding boundary thresholds, and the initial individual fitness value is updated based on the comparison results to generate the target individual fitness value. Based on the difference between the fitness value of the target individual and the corresponding fitness value of the initial individual, intermediate population individuals are determined and the number of iterations is counted in real time. When the number of iterations and the fitness value of the target individual meet the preset iteration threshold, all the intermediate population individuals at the current moment are taken as the target population individuals corresponding to the individual population. When the number of iterations and the fitness value of the target individual do not meet the preset iteration threshold, the target particle velocity and the target particle position are respectively used as the initial particle velocity and the initial particle position, and the process jumps to the step of calculating the initial individual fitness value corresponding to each initial population individual in the individual population.

5. The power distribution grid based electric vehicle regulation method of claim 1, wherein, The method further includes: When the power status is in a state of deficiency, a greedy algorithm is used to obtain the target electric vehicle corresponding to the power distribution network based on the initial charging station operation data.

6. An electric vehicle control system based on a power distribution network, applied to the electric vehicle control method based on a power distribution network as described in claim 1, characterized in that, include: A power status determination module is used to respond to the received power difference in the distribution network and determine the power status of the distribution network based on the power difference in the distribution network. The initial charging station operation data acquisition module is used to acquire the operation data of multiple initial charging stations corresponding to the power distribution network when the power state is in an excess state. The initial electric vehicle determination module is used to determine multiple initial electric vehicles corresponding to the distribution network based on the initial charging station operation data and the power difference of the distribution network. The target electric vehicle determination module is used to determine the target electric vehicle corresponding to the power distribution network based on preset electric vehicle constraints and the initial electric vehicle, using a particle swarm optimization algorithm.

7. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the electric vehicle control method based on the power distribution network as described in any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that When the computer program is executed, it implements the electric vehicle control method based on the power distribution network as described in any one of claims 1-5.

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