Method and apparatus for determining mobile charging pile delivery strategy

Abstract

This application discloses a method and apparatus for determining a mobile charging pile delivery strategy. The method includes: determining K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network; where K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route; predicting multiple delivery strategies for delivering mobile charging piles to the V charging stations based on the K driving routes and the delivery requirements of each charging station, according to the principle of minimizing delivery costs; determining the impact value corresponding to each delivery strategy, where the impact value reflects the degree of impact on the power distribution network when delivering mobile charging piles to the V charging stations according to the delivery strategy; and determining a target delivery strategy from the multiple delivery strategies based on the impact value corresponding to each delivery strategy, thereby achieving efficient delivery of mobile charging piles to each charging station while considering the impact on the power distribution network.

Description

Technical Field

[0001] This application relates to the field of power data processing, and in particular to a method and apparatus for determining a mobile charging pile delivery strategy. Background Technology

[0002] The uneven spatial and temporal distribution of charging demand has led to increasingly prominent shortages of regional and time-specific charging service capacity. In recent years, user charging demand has grown rapidly, but the pace of charging infrastructure deployment has lagged behind the scale of pure electric vehicle development, resulting in an insufficient total number of charging facilities. The supply-demand imbalance during peak charging periods for pure electric vehicles is becoming increasingly acute, with long queues frequently occurring. Meanwhile, the construction of fixed charging facilities faces various limitations and challenges in expansion and upgrades, drawing attention to mobile charging stations. How to distribute suitable mobile charging stations to various charging stations has become one of the current hot research topics. Summary of the Invention

[0003] This application provides a method and apparatus for determining a mobile charging pile delivery strategy. Based on dynamic road network, delivery cost, and impact on the power distribution network, a mobile charging pile delivery strategy is determined for each charging station. This can achieve reasonable delivery of mobile charging piles to each charging station with minimal impact on the power distribution network while saving costs.

[0004] On one hand, embodiments of this application provide a method for determining a mobile charging station delivery strategy, including:

[0005] Based on the dynamic road network, M driving routes are determined for M delivery vehicles to deliver mobile charging piles to N charging stations; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving route.

[0006] Based on the principle of minimizing delivery costs, multiple delivery strategies are predicted for delivering mobile charging piles to N charging stations based on M driving routes and the delivery requirements of each charging station.

[0007] For each delivery strategy, the impact value corresponding to the delivery strategy is determined according to the evaluation index. The impact value is used to reflect the degree of impact of delivering mobile charging piles to N charging stations according to the delivery strategy on the power distribution network.

[0008] The target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0009] On one hand, embodiments of this application provide a device for determining a delivery strategy, including:

[0010] The determination unit is used to determine M driving routes for M delivery vehicles to deliver mobile charging piles to N charging stations based on a dynamic road network; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving route;

[0011] The prediction unit is used to predict multiple delivery strategies for delivering mobile charging piles to N charging stations based on M driving routes and the delivery requirements of each charging station, according to the principle of minimizing delivery costs.

[0012] The determining unit is further configured to determine the impact value corresponding to each delivery strategy based on the evaluation index. The impact value is used to reflect the degree of impact of delivering mobile charging piles to N charging stations according to the delivery strategy on the power distribution network.

[0013] The determining unit is further configured to determine the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0014] On one hand, embodiments of this application provide an electronic device, including:

[0015] A processor, suitable for implementing one or more computer programs; a computer storage medium storing one or more computer programs adapted to be loaded and executed by the processor:

[0016] Based on the dynamic road network, M driving routes are determined for M delivery vehicles to deliver mobile charging piles to N charging stations; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving route.

[0017] Based on the principle of minimizing delivery costs, multiple delivery strategies are predicted for delivering mobile charging piles to N charging stations based on M driving routes and the delivery requirements of each charging station.

[0018] For each delivery strategy, the impact value corresponding to the delivery strategy is determined according to the evaluation index. The impact value is used to reflect the degree of impact of delivering mobile charging piles to N charging stations according to the delivery strategy on the power distribution network.

[0019] The target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0020] On one hand, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a processor of a data processing device, is used to perform:

[0021] Based on the dynamic road network, M driving routes are determined for M delivery vehicles to deliver mobile charging piles to N charging stations; M and N are both integers greater than 1; one delivery vehicle corresponds to one driving route.

[0022] Based on the principle of minimizing delivery costs, multiple delivery strategies are predicted for delivering mobile charging piles to N charging stations based on M driving routes and the delivery requirements of each charging station.

[0023] For each delivery strategy, the impact value corresponding to the delivery strategy is determined according to the evaluation index. The impact value is used to reflect the degree of impact of delivering mobile charging piles to N charging stations according to the delivery strategy on the power distribution network.

[0024] The target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0025] On one hand, embodiments of this application provide a computer program product or a computer program, the computer program product including a computer program, which may refer to a computer program, and the computer program is stored in a computer storage medium; the processor of the electronic device reads the computer program from the computer storage medium, the processor executes the computer program, and causes the electronic device to execute the above-mentioned method for determining the mobile charging pile delivery strategy.

[0026] In this embodiment, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the delivery strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this delivery strategy. Then, based on the impact value of each delivery strategy, a target delivery strategy is selected from multiple delivery strategies. In this way, the reasonableness of the delivery strategy can be ensured while minimizing the impact on the distribution network. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating a method for determining a mobile charging pile delivery strategy according to an embodiment of this application.

[0029] Figure 2 This application provides a schematic diagram of a traffic network topology represented by a weighted directed graph.

[0030] Figure 3This is a flowchart of determining the driving route for a delivery vehicle, provided in an embodiment of this application;

[0031] Figure 4 This is a network topology diagram of power-transportation coupling provided in an embodiment of this application;

[0032] Figure 5 This is a schematic diagram of the structure of a delivery strategy determination device provided in an embodiment of this application;

[0033] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that all information related to the object in social applications, such as avatars, virtual images, and names, in the following description of this application has been obtained with the permission of the respective object.

[0035] Existing mobile charging station route optimization technologies primarily target power battery delivery or mobile charging vehicle dispatching. However, currently, battery swapping charging has a limited application scope, with only a few electric vehicle brands using it; while mobile charging vehicles are scarce, expensive, and have limited charging power, failing to meet the large-scale charging demands in cities. Furthermore, the routes for both battery swapping and mobile charging vehicles are set to the customer's location, leading to significant uncertainty. Due to their off-grid nature, route planning for both methods typically disregards the power distribution network's status, further exacerbating the supply shortage of charging infrastructure for both battery swapping and mobile charging vehicles due to uneven distribution of the network load.

[0036] To address the aforementioned issues, this application proposes a method for determining a mobile charging pile delivery strategy. This method is designed for scenarios where charging infrastructure is in short supply, specifically addressing urban mobile charging pile scheduling path optimization. Its key features include delivery targets being mobile charging piles that support smart sockets, and delivery destinations being various charging stations. Delivery path optimization considers factors such as road conditions, cost, and power distribution network.

[0037] Specifically, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the distribution strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this distribution strategy. Then, based on the impact value of each distribution strategy, a target distribution strategy is selected from multiple distribution strategies. In this way, the reasonableness of the distribution strategy can be ensured while minimizing the impact on the distribution network.

[0038] The method for determining the mobile charging pile delivery strategy provided in this application embodiment can be executed by an electronic device, which may include a terminal or a server. The terminal may include mobile phones, laptops, vehicle terminals, smart wearable devices, and other terminal devices. The server may refer to an independent physical server, a server cluster composed of multiple servers, or a cloud server capable of cloud computing.

[0039] It should be noted that, unless otherwise specified, the method for determining the mobile charging pile delivery strategy provided in this application is based on the following assumptions: (1) All delivery vehicles are completely homogeneous, the start and end points of the delivery route are service midpoints, and the delivery vehicles are powered by fuel, ignoring a series of issues such as range anxiety caused by refueling; (2) Considering road conditions, service route planning is performed based on a dynamic road network model and an optimal route selection algorithm; (3) Each delivery vehicle has only one service route in each delivery cycle and can serve multiple charging stations; (4) The demand of each charging station is served by only one delivery vehicle, and there are no multiple delivery vehicles serving one charging station simultaneously. The delivery status of mobile charging piles; (5) Service centers can dispatch multiple delivery vehicles to serve multiple charging stations, but the total number of delivery facilities cannot exceed the total number of delivery vehicles of the service center; (6) The total number of mobile charging piles delivered by each delivery vehicle shall not exceed the rated load capacity of the delivery vehicle, and the demand of any charging station shall be less than the rated load capacity of the delivery vehicle; (7) Assume that the demand, service time and service soft window of each charging station are known; (8) The time when each delivery vehicle departs from the service center is the start time of the delivery cycle; (9) Mobile charging pile delivery service should be provided to the charging station within the soft time window of the charging station's situation. Early arrivals must wait, and exceeding the time window constraint will be penalized.

[0040] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0041] See Figure 1 This is a flowchart illustrating a method for determining a mobile charging pile delivery strategy according to an embodiment of this application. Figure 1 The determination method shown can be executed by an electronic device, specifically by the processor of the electronic device. Figure 1 The method for determining the mobile charging station delivery strategy may include the following steps:

[0042] Step S101: Based on the dynamic road network, determine the K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0043] Where K and V are both integers greater than 1, and one delivery vehicle corresponds to one driving route.

[0044] In one embodiment, determining K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network may include: constructing a traffic road network model and obtaining real-time road network weights based on the traffic road network model; and determining M driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on the real-time road network weights and optimal path calculation rules.

[0045] In specific implementation, the real-time road network weights include the real-time road network weights between any two nodes in traffic, and the nodes in traffic include the node corresponding to the service center and the node corresponding to the charging station; obtaining the real-time road network weights based on the traffic road network model includes:

[0046] If a path exists between any two nodes according to the traffic network model, then the real-time network weight between the two nodes is calculated based on the node impedance of the two nodes and the road segment impedance between the two nodes.

[0047] Assuming any two nodes include a first node and a second node, the segment impedance between the first node and the second node can be calculated based on the traffic conditions between the first node and the second node, impedance influencing factors, and zero-flow travel time. The node impedance of the first node can be calculated based on the traffic conditions between the first node and the second node, the signal cycle of the first node, the green light ratio, and the segment vehicle arrival rate.

[0048] The following examples illustrate in detail how to construct a dynamic road network and how to determine real-time road network weights based on the dynamic road network. It should be understood that a dynamic road network, also known as a dynamic road network model or a traffic road network model, is constructed using graph theory analysis methods, considering factors such as road conditions and road resistance. In one embodiment, the construction of a traffic road network model may include the following:

[0049] Using graph theory analysis for modeling, the mathematical model of the traffic network adopted in this application can be represented by the following formula:

[0050]

[0051] Wherein, G is the traffic network topology; V represents the set of all nodes in the traffic, which may include nodes corresponding to charging stations and service midpoints, i.e., road intersections or start and end points; L represents the set of all bidirectional road segments; and W is the set of road network weights. These weights are assigned to the connecting edges between nodes based on the connection status of each node in the actual traffic network and the distance and length of road segments. The specific implementation method will be introduced in subsequent embodiments.

[0052] If a weighted directed graph is used to represent the traffic network topology, please refer to [reference needed]. Figure 2 As shown, in Figure 2 Assume that the traffic includes four nodes, v1-v4, and the values ​​on the connecting lines between the nodes represent the road network weights between the two nodes.

[0053] In this embodiment of the application, the road network weights can be stored using an adjacency matrix structure, which can be seen from the following formula:

[0054]

[0055] element w in the matrix ij It can be represented as:

[0056]

[0057] Among them, w ij Represents node v i and node v j The road network weights between, w inf This indicates that there is no road segment between the two nodes, and can generally be denoted as ∞. When i and j are equal, the road network weight is 0; when there is a road segment between the two nodes, the road network weight is a. ij a ij It can be specifically represented using nodal impedance (also called nodal impedance model) and segment impedance (also called segment impedance model). Nodal impedance and segment impedance can be further quantified using weights such as segment length and traffic conditions to represent travel costs.

[0058] Based on this, in the embodiments of this application, any two nodes (unless otherwise specified, any two nodes can be represented as node v) i and node v j, these two nodes are mapped to the actual traffic and can correspond to any two charging stations. The road network weight between them (corresponding to the aforementioned first node and second node) can be expressed by the following formula:

[0059]

[0060] Where, Cv i represents the node impedance of node v i and Rv ij represents the section impedance. The traffic condition is divided by S. When the traffic condition is smooth (0 < S ≤ 0.6); when the traffic condition is slow (0.6 < S ≤ 0.8); when the traffic condition is congested (0.8 < S ≤ 1.0); when the traffic condition is severely congested (1.0 < S ≤ 2.0), the section impedance Rv ij can be calculated by the following formula: [[ID=Q16]]

[0061]

[0062] Where, t0 represents the zero-flow travel time, and α and β are impedance influence factors. Generally, α = 0.15 and β = 4 can be taken. That is to say, in the case of known traffic conditions and impedance influence factors, substituting into the above formula can calculate the section impedance between the first node and the second node. For example, if the traffic condition between the first node and the second node is smooth, it can be known that the value of the traffic condition S falls into the upper branch of the above formula. Substituting the impedance influence factor and the zero-flow travel time into the upper branch for calculation, the section impedance can be obtained. It should be known that

[0063] the node impedance of node v i can be calculated by the following formula:

[0064] Q30]

[0065] Where, c represents the signal cycle, λ represents the green signal ratio, and q represents the vehicle arrival rate of the section.

[0066] Based on the calculation formula of the node impedance and the calculation formula of the section impedance, the calculation formula of the road network weight a ij is rewritten, and the calculation formula of the road network weight a ij is:

[0067]

[0068] Combined with the above embodiments and specific examples, the real-time road network weight can be determined. Further, based on the real-time road network weight and the optimal path calculation rule, K driving paths for K distribution vehicles to distribute mobile charging piles to V charging stations are determined. Q45]

[0069] In one embodiment, the optimal path calculation rule instructs the determination of a driving route for each delivery vehicle according to the shortest path. Based on the traffic network model and the optimal path calculation rule, K driving routes are determined for K delivery vehicles to deliver mobile charging piles to V charging stations. Specifically, this may include:

[0070] The service center and V charging stations are mapped as nodes, and for each delivery vehicle, the starting and ending nodes that the delivery vehicle needs to pass through are determined; the service center refers to the provider of the delivery vehicle; temporary markers are added to L nodes based on the real-time road network weights, and the temporary marker of each node includes the sum of the road network weights of the shortest path from the starting node to the node, and the node preceding the node in the shortest path; the L nodes refer to the nodes corresponding to the charging stations among the V charging stations that have not been assigned to delivery vehicles; the driving path of the delivery vehicle is determined based on the temporary markers of the L nodes.

[0071] Determining a travel route for each delivery vehicle can be understood as selecting the shortest path from the starting node to the ending node for each delivery vehicle.

[0072] Specifically, determining the driving route for the delivery vehicle based on the temporary markers of L charging stations may include the following steps: converting the temporary marker of the starting node into a permanent marker and adding the starting node to the permanent marker set; determining the temporary marker with the smallest sum of road network weights as the target temporary marker based on the temporary markers of the L nodes, and updating the starting node using the previous node in the target temporary marker; if the updated starting node is the same as the ending node, then determining the driving route corresponding to the delivery vehicle using the path composed of the nodes in the permanent set.

[0073] If the updated starting node is different from the ending node, then repeat the process of adding temporary markers to L charging stations based on the real-time road network weights and subsequent steps.

[0074] In this embodiment, the shortest path can be determined using a typical shortest path optimization algorithm, namely Dijkstra's algorithm. This algorithm expands outwards from the starting point, employing a greedy strategy. It iterates through the nearest unvisited neighboring node to the starting point each time, continuing until the destination is reached, thus obtaining the desired optimal solution. The following example illustrates how to determine the driving route for a delivery vehicle.

[0075] Assuming each charging station and service center can be considered a node, and the temporary tag set for a node j can be represented as (D ij v k ), D ijLet v be the sum of the road network weights of the shortest paths from the starting node i to the current node j. It should be noted that the road network weight from each node to itself is 0, and the road network weight between nodes with no road segments is infinite. k Let k be the node preceding node j in the shortest path. The algorithm steps for determining the shortest path for a delivery vehicle using Dijkstra's algorithm are as follows:

[0076] (1) Algorithm initialization, determining the starting node (which can be denoted as the starting mother node v). i ) and the ending node (i.e., the path endpoint v) e In this embodiment of the application, it is assumed that the starting node and the ending node of each delivery vehicle are the same, which is the service center.

[0077] (2) Temporarily mark nodes that have not been assigned delivery vehicles. Set the temporary mark of the starting parent node to (0, -1), and set the temporary mark of all other nodes to (D). ij v i If v i and v j If directly connected, then D ij =a ij If they are not directly connected, then D ij =w inf =∞, and at the same time convert the temporary tag of the parent node to a permanent tag and store it in the permanent tag set S;

[0078] (3) with v i As the parent node, for the D of K nodes ij Compare the values ​​and select the smallest point v. k Convert to permanent tags and store in the permanent tag set S. Then adjust the temporary tags of other nodes. At this time, the temporary tags of other nodes are set to (D). ij v k If a new node v is added k With any node v j The road network weights D between kj ≠w inf Then D ij =D ik +w kj At this point, the parent node is updated to v. k ;

[0079] (4) Repeat step (3) until the endpoint v is reached. e If a node v appears in the permanent tag set S, then set S is the starting node v. i to the end point of the path v e The optimal path.

[0080] The above example illustrates how the optimal route is determined for each delivery vehicle. For any single vehicle, the method is the same. See also... Figure 3 This is a schematic diagram illustrating the setting of a driving route for each delivery vehicle, provided in an embodiment of this application.

[0081] Step S102: Based on the principle of minimizing delivery costs, predict multiple delivery strategies for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station.

[0082] In one embodiment, predicting multiple delivery strategies for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station may include: Step S1, constructing an objective function based on delivery costs and the delivery requirements of each charging station; Step S2, setting solution constraints for the objective function, including any one or more of flow constraints, load capacity constraints of delivery vehicles, and time window constraints for delivery vehicles to arrive at the delivery charging stations; Step S3, solving the objective function according to the principle of minimizing delivery costs and based on the solution constraints to obtain multiple delivery strategies.

[0083] In step S1, the delivery cost may include the service center's fixed fees, total transportation costs, and early arrival waiting costs and late arrival penalty costs within the soft time window. Specifically, it may include the unit-time waiting cost of the delivery vehicle arriving at the charging station early, which can be calculated using FW (Fulfilled Wi-Fi). tm This indicates that the unit time penalty cost for delivery vehicles arriving late at charging stations can be represented by LW. tm The total transportation cost can be represented by FC.

[0084] The delivery requirements for each charging station may include, but are not limited to, the earliest acceptable service time and the latest acceptable service time. Taking charging station j as an example, the earliest acceptable service time can be expressed as follows:

[0085] ET j The latest acceptable service time is denoted as LT. j The discrete time points at which delivery vehicles arrive at each charging station, taking charging station i as an example, can be represented as T. jk .

[0086] The objective function, constructed based on delivery costs and the delivery requirements of each charging station, can be expressed by the following formula:

[0087]

[0088] Where V and N are the same, representing the number of charging stations; K and M are the same, representing the number of delivery vehicles; c ijThe weighted distance between charging station i and charging station j can be set as the road segment distance in this embodiment. ijk It is a variable between 0 and 1, representing a value indicating that if there is a direct path between charging station i and charging station j, and the delivery vehicle's sequence number is k (meaning that among the K delivery vehicles, the vehicle with sequence number k's travel path includes the route from charging station i to charging station j), then x ijk =1, otherwise 0.

[0089] In step S2, the constraints to be solved may include any one or more of the following: flow constraints, load capacity constraints of delivery vehicles, and time window constraints of delivery vehicles arriving at the delivery charging station.

[0090] First, flow constraints are used to define the following:

[0091] (1) There is one and only one path between any two charging stations, which can be expressed by the following formula:

[0092]

[0093] (2) There are no paths between each charging station, which can be represented as: Where K represents the number of delivery vehicles and V represents the number of charging stations;

[0094] (3) Ensure that the number of delivery vehicles entering and leaving the service center is equal, which can be expressed by the following formula:

[0095]

[0096] (4) Ensure that each delivery vehicle has at most one route, which can be expressed by the following formula:

[0097]

[0098] Where V0 represents the service center, V c This represents a set of V charging stations. It's important to note that in the Vehicle Routing Problem (VRP), when a loop is split into two or more loops, each loop is called a sub-loop. The flow constraints also include sub-loop elimination constraints, which prevent the final dispatching result of delivery vehicles from including loops that do not pass through service centers, i.e., sub-loop problems. Sub-loop elimination constraints can be expressed by the following formula:

[0099]

[0100] Among them, u ik and u jkLet x be the continuous variable to be determined, set up to eliminate sub-loops. If delivery vehicle k has a path from charging station i to j, then x ijk =1, the constraint becomes u ik -u jk ≤-1, which is u jk ≥u ik +1, along this word loop, u ik It keeps increasing. Assuming there exists a sub-loop that satisfies this constraint, if we find a sub-loop that does not contain the starting point 1, such as traffic nodes 2→4→6→2, then u appears. 2k ≥u 6k +1≥u 4k +2≥u 2k +3, which is clearly a contradiction.

[0101] Second, the load capacity constraints of delivery vehicles, which constrain the following:

[0102] (1) Constrain the load variation of the slow-charging and fast-charging mobile charging piles configured for each delivery vehicle, which can be expressed by the following formula:

[0103]

[0104] Where, d fh Representing the demand for slow-charging mobile charging stations (h), d sh Q represents the demand for fast-charging mobile charging stations, specifically charging station h. sihk Q represents the remaining load capacity of slow-charging mobile charging piles when a delivery vehicle k delivers a mobile charging pile to charging station i and then delivers a mobile charging pile to charging station h. fihk This represents the remaining load capacity of the fast-charging mobile charging pile when delivery vehicle k delivers a mobile charging pile to charging station i and then delivers charging facilities to charging station h.

[0105] (2) Constrain the load capacity of slow-charging and fast-charging mobile charging piles for each delivery vehicle. The total number of mobile charging piles for all delivery vehicles must not exceed the total number of existing facilities at the service center. This constraint can be expressed by the following formula:

[0106]

[0107] Among them, Q sijk Q represents the remaining load capacity of the slow-charging mobile charging piles for the vehicle k that has finished serving charging station i and is now serving charging station j. fijk CVs represents the remaining fast-charging mobile charging capacity of the vehicle that serves charging station i after delivery vehicle k finishes serving charging station i and then serves charging station j. CVs represents the total number of slow-charging mobile charging stations owned by the service center. f This indicates the total number of fast-charging mobile charging stations owned by the service center.

[0108] (3) The load on each delivery vehicle must not exceed its rated load capacity, which can be expressed by the following formula:

[0109]

[0110] Among them, Q ijk CV represents the remaining total load capacity of mobile charging stations after delivery vehicle k finishes serving charging station i and then serves charging station j.

[0111] Third, time window constraints can be used to constrain the following:

[0112] (1) The arrival time of each delivery vehicle delivering mobile charging piles to the charging station must be within the scheduling service cycle, as expressed by the following formula:

[0113]

[0114] Among them, T t T represents a scheduling service period. jk This represents the discrete-time point at which delivery vehicle k arrives at charging station i.

[0115] (2) The time it takes for each delivery vehicle to depart from the service center and arrive at a charging station is related to the travel time from the service center to the charging station, which can be expressed by the following formula:

[0116]

[0117] Among them, c ij The weighted distance between node i and node j can be equal to the road segment between the two points. For example, node i can refer to the service center and node j can refer to the charging station j.

[0118] (3) Constrain the time when a delivery vehicle departs from charging station i and arrives at charging station j, i.e., the sum of the time when it starts serving charging station i, the time it takes to serve charging station i, and the time it takes to travel from charging station i to charging station j. This includes the constraint that the delivery vehicle must wait if it arrives at the charging station early. This part of the constraint can be expressed by the following formula:

[0119]

[0120] Among them, t ik ET represents the service time of delivery vehicle k at charging station i. i This indicates the earliest acceptable arrival time for charging station i.

[0121] (4) The service time of a delivery vehicle at a charging station is related to the number of mobile charging piles delivered to that charging station. This constraint can be reflected by the following formula:

[0122]

[0123] Where, d fi d represents the demand for slow-charging mobile charging stations at charging node i. si T represents the demand for fast-charging mobile charging stations at charging node i. se This represents the time it takes to deliver a single mobile charging station to a charging station.

[0124] Step S103: For each delivery strategy, determine the corresponding impact value of the delivery strategy.

[0125] A delivery strategy may include when each of the K delivery vehicles departs to deliver mobile charging stations, the number of slow-charging mobile charging stations configured in each delivery vehicle, the number of fast-charging mobile charging stations, etc. The specific facilities can be configured according to the actual application scenario, and this application embodiment does not impose any restrictions.

[0126] It should be understood that mobile charging setups without energy storage need to be connected to the power distribution network to provide services. In order to minimize the impact on the power distribution network, after determining multiple distribution strategies in step S102, it is necessary to measure the impact value of each distribution strategy on the power distribution network. Finally, based on the impact value, the optimal distribution strategy is selected from multiple distribution strategies, and mobile charging piles are distributed to V charging stations according to the optimal distribution strategy.

[0127] In one embodiment, determining the impact value corresponding to the delivery strategy may include: obtaining a network topology diagram of the power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes, and the connection relationships between each node; and determining the impact value corresponding to the delivery strategy based on the network topology diagram.

[0128] The topology diagram for the integration of power and transportation can be shown as follows: Figure 4 As shown, in Figure 4 China Mobile Charging Service Center refers to the service center mentioned above.

[0129] Optionally, determining the impact value corresponding to the delivery strategy based on the network topology diagram may include: determining the resistance of each branch, the current of each branch, and the voltage of each charging node under the delivery strategy based on the network topology diagram; and determining the impact value corresponding to the delivery strategy based on the resistance of each branch, the current of each branch, and the voltage of each charging node.

[0130] Specifically, the impact value corresponding to the delivery strategy is determined based on the resistance of each branch, the current of each branch, and the voltage of each charging node, including: calculating the active power loss corresponding to the delivery strategy based on the resistance of each branch, the current of each branch, and the time change.

[0131] Based on the voltage of each charging node and the preset node voltage offset extreme value calculation rule, calculate the node voltage offset extreme value under the delivery strategy; based on the voltage of each charging node and the preset daily voltage offset calculation formula, calculate the daily voltage offset of each charging node, and calculate the cumulative daily voltage offset value corresponding to the delivery strategy based on the daily voltage offset of each charging node; determine the impact value corresponding to the delivery strategy based on any one or more of the active power loss, node voltage offset extreme value, and cumulative daily voltage offset value corresponding to the delivery strategy.

[0132] For example, suppose r ij Indicates branch l ij The resistance, i ij,t branch l at time t ij The current flowing through it, Δt represents the change over time, and T represents a scheduling service cycle. The active power loss in a delivery strategy can be calculated using the following formula:

[0133]

[0134] Assume u j,t Let represent the voltage of charging node j during time period t. The formula for calculating the voltage offset of each charging node is as follows:

[0135]

[0136] Substituting the voltage of charging node j at time t into the above formula yields the voltage offset of the charging node. Furthermore, calculating the daily voltage offset of each charging node yields the cumulative daily voltage offset corresponding to the delivery strategy, which can be expressed as the following formula:

[0137]

[0138] Step S104: Determine the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0139] The impact value corresponding to each delivery strategy reflects the degree of impact of mobile charging pile delivery according to that strategy on the power distribution network. Furthermore, based on the impact values ​​of various delivery strategies, a target delivery strategy is selected from multiple delivery strategies. Optionally, the delivery strategy with the smallest impact value can be selected as the target delivery strategy, and then mobile charging piles can be delivered to V charging stations according to the target delivery strategy. This can achieve efficient and rapid delivery of mobile charging piles with minimal impact on the power distribution network.

[0140] In this embodiment, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the delivery strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this delivery strategy. Then, based on the impact value of each delivery strategy, a target delivery strategy is selected from multiple delivery strategies. In this way, the reasonableness of the delivery strategy can be ensured while minimizing the impact on the distribution network.

[0141] Based on the above-described method for determining mobile charging pile delivery strategies, this application provides a device for determining delivery strategies. (See attached document.) Figure 5 This is a schematic diagram of a device for determining a delivery strategy provided in an embodiment of this application. Figure 5 The device for determining the delivery strategy shown can operate the following unit:

[0142] The determining unit 501 is used to determine K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route;

[0143] The prediction unit 502 is used to predict multiple delivery strategies for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station, according to the principle of minimizing delivery costs.

[0144] The determining unit 501 is further configured to determine the impact value corresponding to each delivery strategy, wherein the impact value is used to reflect the degree of impact of delivering mobile charging piles to V charging stations according to the delivery strategy on the power distribution network.

[0145] The determining unit 501 is further configured to determine the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0146] In one embodiment, when determining the impact value corresponding to the delivery strategy, the determining unit 501 performs the following steps:

[0147] Obtain a network topology diagram of the power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes, and the connection relationships between each node; determine the impact value corresponding to the delivery strategy based on the network topology diagram.

[0148] In one embodiment, when determining the impact value corresponding to the delivery strategy based on the network topology diagram, the determining unit 501 performs the following steps:

[0149] Based on the network topology diagram, the resistance, current, and voltage of each branch and charging node under the delivery strategy are determined; the impact value corresponding to the delivery strategy is determined according to the resistance, current, and voltage of each branch and charging node.

[0150] In one embodiment, when determining the impact value corresponding to the delivery strategy based on the resistance of each branch, the current of each branch, and the voltage of each charging node, the determining unit 501 performs the following steps:

[0151] The active power loss corresponding to the delivery strategy is calculated based on the resistance of each branch, the current of each branch, and the time variation.

[0152] Based on the voltage of each charging node and the preset node voltage offset extreme value calculation rule, calculate the node voltage offset extreme value under the delivery strategy;

[0153] Based on the voltage of each charging node and the preset daily voltage offset calculation formula, the daily voltage offset of each charging node is calculated, and the cumulative value of the daily voltage offset corresponding to the delivery strategy is calculated based on the daily voltage offset of each charging node.

[0154] The impact value corresponding to the delivery strategy is determined based on any one or more of the following: active power loss, node voltage offset extreme value, and daily voltage offset cumulative value.

[0155] In one embodiment, when determining the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy, the determining unit 501 performs the following steps: determining the delivery strategy corresponding to the smallest impact value as the target delivery strategy.

[0156] In one embodiment, when determining K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network, the determining unit 501 performs the following steps:

[0157] A traffic network model is constructed, and real-time road network weights are obtained based on the traffic network model. Based on the real-time road network weights and the optimal path calculation rules, K driving paths are determined for K delivery vehicles to deliver mobile charging piles to V charging stations.

[0158] In one embodiment, the optimal path calculation rule instructs the determination of a driving route for each delivery vehicle based on the shortest path; when the determining unit 501 determines the K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on the traffic network model and the optimal path calculation rule, it performs the following steps:

[0159] The service center and V charging stations are mapped as nodes, and for each delivery vehicle, the starting and ending nodes that the delivery vehicle needs to pass through are determined; the service center refers to the provider of the delivery vehicle.

[0160] Based on the real-time road network weights, temporary markers are added to L nodes. The temporary marker for each node includes the sum of the road network weights of the shortest path from the starting node to the node, and the node preceding the node in the shortest path. The L nodes refer to the nodes corresponding to the charging stations among the V charging stations that have not been assigned to delivery vehicles. The driving path for the delivery vehicle is determined based on the temporary markers of the L nodes.

[0161] In one embodiment, when determining the driving route for the delivery vehicle based on the temporary markings of L charging stations, the determining unit 501 performs the following steps:

[0162] Convert the temporary tag of the starting node into a permanent tag, and add the starting node to the permanent tag set;

[0163] Based on the temporary point markers of L nodes, the temporary marker with the smallest sum of road network weights is determined as the target temporary point marker, and the starting node is updated using the previous node in the target temporary marker.

[0164] If the updated starting node is the same as the ending node, then the path composed of the nodes in the permanent set is used to determine the driving path corresponding to the delivery vehicle.

[0165] If the updated starting node is different from the ending node, then repeat the process of adding temporary markers to L charging stations based on the real-time road network weights and subsequent steps.

[0166] In one embodiment, when the determining unit 501 obtains the real-time road network weights based on the traffic network model, it performs the following steps:

[0167] If a path exists between any two nodes according to the traffic network model, then the real-time network weight between the two nodes is calculated based on the node impedance of the two nodes and the road segment impedance between the two nodes.

[0168] In one embodiment, the determining device further includes a calculation unit 503, wherein any two nodes include a first node and a second node, and the calculation unit 503 is used to calculate the segment impedance between the first node and the second node based on the traffic conditions, impedance influence factor and zero-flow travel time between the first node and the second node; and to calculate the node impedance of the first node based on the traffic conditions between the first node and the second node, the signal cycle of the first node, the green light ratio and the segment vehicle arrival rate.

[0169] In one embodiment, when the prediction unit 502 predicts multiple delivery strategies for delivering mobile charging to V charging stations based on K driving routes and the delivery requirements of each charging station, according to the principle of minimizing delivery costs, it performs the following steps:

[0170] Given K delivery vehicles delivering mobile charging piles to V charging stations according to their respective driving routes, construct an objective function based on delivery costs and delivery requirements of each charging station.

[0171] Set solution constraints for the objective function, including any one or more of the following: flow constraints, load capacity constraints of delivery vehicles, and time window constraints for delivery vehicles to arrive at the delivery charging station.

[0172] Based on the principle of minimizing delivery costs and the aforementioned constraints, the objective function is solved to obtain various delivery strategies.

[0173] In this embodiment, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the delivery strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this delivery strategy. Then, based on the impact value of each delivery strategy, a target delivery strategy is selected from multiple delivery strategies. In this way, the reasonableness of the delivery strategy can be ensured while minimizing the impact on the distribution network.

[0174] Based on the above-described method and apparatus for determining mobile charging station delivery strategies, this application also provides an electronic device, see [link to relevant documentation]. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 The illustrated electronic device may include a processor 401, an input interface 402, an output interface 403, and a computer storage medium 403. The processor 401, input interface 402, output interface 403, and computer storage medium 403 may be connected via a bus or other means.

[0175] Computer storage medium 404 can be stored in the memory of an electronic device. The computer storage medium 404 is used to store computer programs, and the processor 401 is used to execute the computer programs stored in the computer storage medium 404. The processor 401 (or CPU (Central Processing Unit)) is the computing and control core of the electronic device, suitable for implementing one or more computer programs, specifically suitable for loading and executing:

[0176] Based on the dynamic road network, K driving routes are determined for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route;

[0177] Based on the principle of minimizing delivery costs, multiple delivery strategies are predicted for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station.

[0178] For each delivery strategy, an impact value corresponding to the delivery strategy is determined. The impact value is used to reflect the degree of impact on the power distribution network of delivering mobile charging piles to V charging stations according to the delivery strategy.

[0179] The target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

[0180] In this embodiment, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the delivery strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this delivery strategy. Then, based on the impact value of each delivery strategy, a target delivery strategy is selected from multiple delivery strategies. In this way, the reasonableness of the delivery strategy can be ensured while minimizing the impact on the distribution network.

[0181] This application embodiment also provides a computer storage medium (memory), which is a memory device of an electronic device used to store programs and data. It is understood that the computer storage medium here can include the built-in storage medium of the electronic device, or it can include an extended storage medium supported by the electronic device. The computer storage medium provides storage space, which stores the operating system of the electronic device. Furthermore, the storage space also stores one or more computer programs suitable for loading and execution by the processor 401. It should be noted that the computer storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer storage medium located remotely from the aforementioned processor.

[0182] In one embodiment, one or more computer programs stored in the computer storage medium can be loaded and executed by the processor 401:

[0183] Based on the dynamic road network, K driving routes are determined for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route;

[0184] Based on the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to V charging stations are predicted based on K driving routes and the delivery requirements of each charging station. For each delivery strategy, the impact value corresponding to the delivery strategy is determined. The impact value is used to reflect the degree of impact on the power distribution network of delivering mobile charging piles to V charging stations according to the delivery strategy. Based on the impact value corresponding to each delivery strategy, the target delivery strategy is determined from the multiple delivery strategies.

[0185] In this embodiment, firstly, based on the dynamic road network, M driving routes are defined for delivering mobile charging piles to N charging stations. Then, according to the principle of minimizing delivery costs, multiple delivery strategies for delivering mobile charging piles to the N charging stations are predicted based on the M driving routes and the delivery requirements of each charging station. Thus, the determination of these multiple delivery strategies comprehensively considers traffic conditions, the delivery requirements of each charging station, and delivery costs, thereby achieving a reasonable delivery strategy while minimizing delivery costs. Furthermore, for each distribution strategy, this application also determines the impact value of the delivery strategy on the distribution network based on evaluation indicators. This impact value reflects the degree of impact on the distribution network if mobile charging piles are delivered to the N charging stations according to this delivery strategy. Then, based on the impact value of each delivery strategy, a target delivery strategy is selected from multiple delivery strategies. In this way, the reasonableness of the delivery strategy can be ensured while minimizing the impact on the distribution network.

Claims

1. A method for determining a mobile charging station delivery strategy, characterized in that, include: Based on the dynamic road network, determine the K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route; Based on the principle of minimizing delivery costs, multiple delivery strategies are predicted for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station. For each delivery strategy, an impact value corresponding to the delivery strategy is determined. The impact value is used to reflect the degree of impact on the power distribution network of delivering mobile charging piles to V charging stations according to the delivery strategy. The target delivery strategy is determined from multiple delivery strategies based on the impact value corresponding to each delivery strategy. Determining the impact value corresponding to the delivery strategy includes: Obtain a network topology diagram of the power-transportation coupling; the network topology diagram includes V charging station nodes corresponding to V charging stations, distribution network nodes, and the connection relationships between each node. Based on the network topology, the resistance of each branch, the current of each branch, and the voltage of each charging node under the delivery strategy are determined. The impact value corresponding to the delivery strategy is determined based on the resistance of each branch, the current of each branch, and the voltage of each charging node. The step of determining the impact value corresponding to the delivery strategy based on the resistance of each branch, the current of each branch, and the voltage of each charging node includes: The active power loss corresponding to the delivery strategy is calculated based on the resistance of each branch, the current of each branch, and the time variation. Based on the voltage of each charging node and the preset node voltage offset extreme value calculation rule, calculate the node voltage offset extreme value under the delivery strategy; Based on the voltage of each charging node and the preset daily voltage offset calculation formula, the daily voltage offset of each charging node is calculated, and the cumulative value of the daily voltage offset corresponding to the delivery strategy is calculated based on the daily voltage offset of each charging node. The impact value corresponding to the delivery strategy is determined based on any one or more of the following: active power loss, node voltage deviation extreme value, and daily voltage deviation cumulative value. The determination of K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network includes: Construct a traffic network model. If it is determined that there is a path between any two nodes based on the traffic network model, then calculate the real-time network weight between any two nodes based on the node impedance of any two nodes and the road segment impedance between any two nodes. Based on the real-time road network weights and optimal path calculation rules, K driving routes are determined for K delivery vehicles to deliver mobile charging piles to V charging stations.

2. The method as described in claim 1, characterized in that, The optimal path calculation rule instructs the determination of a driving route for each delivery vehicle based on the shortest path; the determination of K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on the real-time road network weights and the optimal path calculation rule includes: The service center and V charging stations are mapped as nodes, and for each delivery vehicle, the starting and ending nodes that the delivery vehicle needs to pass through are determined; the service center refers to the provider of the delivery vehicle. Based on the real-time road network weights, temporary markers are added to L nodes. The temporary marker for each node includes the sum of the road network weights of the shortest path from the starting node to the node, and the node preceding the node in the shortest path. The L nodes refer to the nodes corresponding to the charging stations among the V charging stations that have not been assigned delivery vehicles. The delivery vehicle's travel route is determined based on temporary markers for L nodes.

3. The method as described in claim 2, characterized in that, The temporary marking based on L charging stations for determining the driving route of the delivery vehicle includes: Convert the temporary tag of the starting node to a permanent tag, and add the starting node to the permanent tag set; Based on the temporary labels of L nodes, the temporary label of the minimum sum of road network weights is determined as the target temporary label, and the starting node is updated using the previous node in the target temporary label. If the updated starting node is the same as the ending node, then the path composed of the nodes in the permanent tag set is used to determine the driving path corresponding to the delivery vehicle. If the updated starting node is different from the ending node, then repeat the process of adding temporary markers to L charging stations based on the real-time road network weights and subsequent steps.

4. The method as described in claim 1, characterized in that, The real-time road network weights include the real-time road network weights between any two nodes in traffic, and the nodes in traffic include the nodes corresponding to service centers and the nodes corresponding to charging stations. The arbitrary two nodes include a first node and a second node, and the method further includes: Based on the traffic conditions, impedance influence factor, and zero-flow travel time between the first node and the second node, calculate the segment impedance between the first node and the second node; Based on the traffic conditions between the first node and the second node, the signal cycle of the first node, the green light ratio, and the vehicle arrival rate of the road segment, the node impedance of the first node is calculated.

5. The method as described in claim 1, characterized in that, Based on the principle of minimizing delivery costs, and based on K driving routes and the delivery requirements of each charging station, the following are multiple delivery strategies for delivering mobile charging piles to V charging stations: Given K delivery vehicles delivering mobile charging piles to V charging stations according to their respective driving routes, construct an objective function based on delivery costs and delivery requirements for each charging station. Set solution constraints for the objective function, including any one or more of the following: flow constraints, load capacity constraints of delivery vehicles, and time window constraints for delivery vehicles to arrive at the delivery charging station. Based on the principle of minimizing delivery costs and the aforementioned constraints, the objective function is solved to obtain various delivery strategies.

6. An apparatus for implementing the method for determining a mobile charging pile delivery strategy according to any one of claims 1-5, characterized in that, include: The determination unit is used to determine K driving routes for K delivery vehicles to deliver mobile charging piles to V charging stations based on a dynamic road network; K and V are both integers greater than 1; one delivery vehicle corresponds to one driving route; The prediction unit is used to predict multiple delivery strategies for delivering mobile charging piles to V charging stations based on K driving routes and the delivery requirements of each charging station, according to the principle of minimizing delivery costs. The determining unit is further configured to determine the impact value corresponding to each delivery strategy, wherein the impact value is used to reflect the degree of impact of delivering mobile charging piles to V charging stations according to the delivery strategy on the power distribution network. The determining unit is further configured to determine the target delivery strategy from multiple delivery strategies based on the impact value corresponding to each delivery strategy.

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