A method and system for planning a fast charging station for an electric vehicle

Abstract

This invention relates to the field of charging station planning technology, and discloses a method and system for planning electric vehicle fast charging stations. An objective function is established to minimize the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost. Constraints are established for the distribution network model, static traffic flow allocation model, traffic network reconfiguration, travel time cost, and electric vehicle charging demand. Based on the objective function and a series of constraints, a planning model for electric vehicle fast charging stations is obtained, and the model is solved to obtain the planning results. A traffic network reconfiguration strategy is introduced to alleviate traffic congestion and increase route capacity, enabling the planned fast charging stations to more effectively meet the charging needs of electric vehicles, shortening the time cost of electric vehicles traveling in the traffic network, and increasing the flexibility of electric vehicles in the traffic network. This ensures effective satisfaction of electric vehicle charging needs while reducing the number of fast charging stations that need to be built.

Description

Technical Field

[0001] This invention relates to the field of charging station planning technology, and in particular to a method and system for planning fast charging stations for electric vehicles. Background Technology

[0002] Electric vehicles, due to their zero-carbon emissions, are gradually replacing traditional internal combustion engine vehicles. It is predicted that by 2030, the global electric vehicle fleet will reach 380 million vehicles. Clearly, the widespread use of electric vehicles has generated enormous charging demand, putting pressure on the power grid, and the current inadequacy of electric vehicle charging infrastructure is a serious problem. Therefore, the rational planning and construction of fast-charging stations for electric vehicles is urgently needed.

[0003] Properly planned fast-charging stations can better meet the charging needs of electric vehicles. Simultaneously, it can minimize various costs within the power distribution network. The rapid growth in the number of electric vehicles and the construction of EV charging stations have led to a close coupling between the power distribution network and the transportation network. However, most existing studies have only considered the power distribution network and focused solely on the fixed transmission network topology. Similar to the topology reconfiguration of power distribution networks and district heating networks, the topology of transportation networks can also be reconfigured; however, the planning of fast-charging stations in urban transportation networks has not adopted transportation network reconfiguration strategies. This results in the inability to ensure that the number of planned fast-charging stations is minimized while effectively meeting the charging needs of electric vehicles. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a planning method and system for electric vehicle fast charging stations, which can effectively meet the charging needs of electric vehicles while reducing the number of fast charging stations that need to be built.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for planning fast charging stations for electric vehicles, comprising the following steps:

[0007] Establish an objective function that minimizes the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost;

[0008] Establish constraints for the power distribution network model, static traffic flow allocation model, traffic network reconfiguration, travel time cost, and electric vehicle charging demand.

[0009] Based on the objective function, the power distribution network model constraints, the static traffic flow allocation model constraints, the traffic network reconfiguration constraints, the travel time cost constraints, and the electric vehicle charging demand constraints, a planning model for electric vehicle fast charging stations is obtained.

[0010] The electric vehicle fast charging station planning model is solved to obtain the fast charging station planning results.

[0011] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0012] An electric vehicle fast charging station planning system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the electric vehicle fast charging station planning method described above.

[0013] The beneficial effects of this invention are as follows: An objective function is established to minimize the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost. Constraints are established for the distribution network model, static traffic flow allocation model, traffic network reconfiguration, travel time cost, and electric vehicle charging demand. Based on the objective function and a series of constraints, a planning model for electric vehicle fast charging stations is obtained. Solving this model yields the planning results. The established objective function can save on the total planning cost of fast charging stations. The constraints include static traffic flow allocation model constraints and traffic network reconfiguration constraints, introducing a traffic network reconfiguration strategy to alleviate traffic congestion and increase route capacity. The constraints also include travel time cost constraints and electric vehicle charging demand constraints, enabling the planned fast charging stations to more effectively meet the charging needs of electric vehicles. This also shortens the time cost of electric vehicles traveling in the traffic network and increases their flexibility. Therefore, electric vehicles can reach charging stations more quickly, reducing their own electricity consumption and the number of fast charging stations needed. This ensures that the charging needs of electric vehicles are effectively met while reducing the number of fast charging stations that need to be built. Attached Figure Description

[0014] Figure 1 This is a flowchart of a method for planning electric vehicle fast charging stations according to an embodiment of the present invention;

[0015] Figure 2 This is a schematic diagram of an electric vehicle fast charging station planning system according to an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram of a road reversal strategy in an electric vehicle fast charging station planning method according to an embodiment of the present invention. Detailed Implementation

[0017] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0018] In existing technologies, when planning fast charging stations, only the distribution network is considered, and only the fixed transmission network topology is focused on. This will result in the inability to ensure that the number of planned fast charging stations is minimized and can effectively meet the charging needs of electric vehicles.

[0019] To at least address the aforementioned problems, embodiments of the present invention provide a method and system for planning electric vehicle fast charging stations, applicable to fast charging station planning scenarios. The specific implementation details are described below:

[0020] Please refer to Figure 1 One embodiment of the present invention is as follows:

[0021] A method for planning fast charging stations for electric vehicles, comprising the following steps:

[0022] S1. Establish an objective function that minimizes the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost, specifically including S11-S16:

[0023] S11. Obtain the economic life, discount rate, investment cost, and operation and maintenance cost of the fast charging station.

[0024] S12. Obtain the traffic flow on each road segment and the number of fast charging stations installed at each node during each time period of the day.

[0025] S13. The total investment cost is obtained based on the economic life, the discount rate, the investment cost, and the number of fast charging stations installed at each node, specifically as follows:

[0026] ;

[0027] ;

[0028] In the formula, Indicates the total investment cost. Represents auxiliary variables. Indicates the number of nodes in the transportation network. This indicates the investment cost per unit of a fast charging station. Indicates that it is installed on the node The number of fast charging stations in the area. Indicates the discount rate. This indicates the economic lifespan of a fast charging station.

[0029] In one alternative implementation, the discount rate is set to 0.03.

[0030] S14. The operation and maintenance cost of the fuel cell power plant is obtained based on the aforementioned operation and maintenance cost and the number of fast charging stations installed at each node, specifically as follows:

[0031] ;

[0032] In the formula, This represents the operating and maintenance costs of a fuel cell power plant. This represents the operating and maintenance cost per unit of a fast charging station.

[0033] S15. The total travel time cost is obtained based on the traffic flow on each road segment during different time periods of the day, specifically as follows:

[0034] ;

[0035] In the formula, This represents the total travel time cost. Indicates a collection of road segments in a transportation network. Indicates road segment In time period The electric vehicle driving cost function, Indicates a time period within a day Next node To the node Directional section Traffic flow on the road.

[0036] S16. Construct an objective function with the goal of minimizing the sum of the total investment cost, the operation and maintenance cost of the fuel cell power plant, and the total travel time cost, specifically as follows:

[0037] .

[0038] In this way, when constructing the objective function, core economic parameters such as the economic lifespan and discount rate of fast charging stations are obtained. Combined with actual operational data such as traffic flow and the number of charging stations, the total investment, fuel cell power plant operation and maintenance, and total travel time are quantitatively calculated in different dimensions. With the goal of minimizing the sum of the three types of costs, the economic cost of energy facilities and the efficiency of transportation can be synergistically optimized, ensuring the economic efficiency and practicality of the planning scheme.

[0039] S2. Establish constraints for the distribution network model, static traffic flow assignment model, traffic network reconfiguration, travel time cost, and electric vehicle charging demand, specifically including S21-S28:

[0040] S21. Obtain the active power of each branch, the active power of each bus, the inherent load at each node, and the charging demand of electric vehicles at each node.

[0041] S22. Construct power balance constraints based on the active power of each branch, the active power of each bus, the inherent load at each node, and the charging demand of electric vehicles at each node, specifically:

[0042] ;

[0043] ;

[0044] In the formula, This represents the set of child nodes of a distribution network node. Indicates the time period branch road active power, Indicates the time period busbar active power, This represents the set of parent nodes of a distribution network node. Indicates the time period branch road active power, Represents the set of nodes in the distribution network. Indicates the time period node The inherent load at the location, Indicates the time period node The charging needs of electric vehicles in the area.

[0045] S23. Establish upper and lower voltage limits and branch current constraints.

[0046] Specifically, the voltage upper and lower limit constraints are as follows:

[0047] ;

[0048] In the formula, This indicates the lower limit of the voltage amplitude limit. This indicates the upper limit of the voltage amplitude limit. Indicates the time period node The voltage amplitude at that location.

[0049] The branch current constraint is specifically as follows:

[0050] ;

[0051] In the formula, Indicates the time period branch road The current, Indicates a branch The maximum allowable current.

[0052] S24. Obtain the distribution network model constraints based on the power balance constraints, the voltage upper and lower limit constraints, and the branch current constraints.

[0053] In this way, power balance constraints are established to ensure the matching of power supply and demand in the power grid. Combined with voltage upper and lower limits and branch current constraints, the key indicators for safe operation of the distribution network are fully covered. The resulting distribution network model constraints can strictly limit the impact of charging load access on the power grid in the planning of charging stations, avoid power imbalance, voltage over-limit or branch overload caused by charging demand shocks, ensure the stable and safe operation of the distribution network, and at the same time make the planning of fast charging stations compatible with the carrying capacity of the power grid.

[0054] S25. Establish constraints for the static traffic flow assignment model, specifically:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] In the formula, Represents a set of paths. Representing a path Traffic flow within a day Indicates a start-end pair Total transportation demand Represents the set of intersection nodes in the transportation network. Indicates included road segments path Traffic flow on the road Indicates the time from node within a day To the node Directional section Traffic flow on the road Represents a set of time periods. Indicates the time from node within a day To the node Directional section Traffic flow on the road Indicates origin-end point (OD) pairs The starting node, Indicates a start-end pair The endpoint.

[0061] The first and second equations in the static traffic flow assignment model constraints describe that the traffic flow on all paths in the origin-destination pair must equal the total traffic demand, and the flow on the route cannot be negative; the third and fourth equations describe that the traffic flow on a road segment must equal the sum of the flows on all routes containing that road segment, and must also equal the total flow through that road segment in a day; the fifth equation describes that the traffic flow out of the origin node and the traffic flow into the destination node in the origin-destination pair must both equal the total transport demand of that origin-destination pair in a day.

[0062] S26. Establish constraints for transportation network reconstruction, specifically:

[0063] ;

[0064] ;

[0065] In the formula, Indicates time period Slave node after link reversal To the node Capacity in direction, Indicates the slave node before the link reversal To the node Capacity in direction, Indicates the total number of lanes. Indicates from node To the node The original capacity of the direction, Indicates the percentage of road carrying capacity. This represents an auxiliary parameter used to reflect the number of lanes inverted. If from node... To the node If one lane in a direction is reversed, then n=1; similarly, if x lanes are reversed, then n=x.

[0066] like Figure 3 As shown, road reversal is used to alleviate traffic congestion in the all-time network. When the traffic flow in a direction is about to reach its original capacity during peak hours, the capacity of that direction can be expanded by reversing several lanes in the opposite direction.

[0067] S27. Establish travel time cost constraints, specifically:

[0068] ;

[0069] In the formula, Indicating the economic value of time Indicates road segment Travel time cost when traffic is not congested.

[0070] Travel time cost is a crucial indicator in transportation networks, related to road capacity and traffic flow. When road capacity is constant, travel time cost increases rapidly with increasing traffic flow. Transportation network restructuring strategies can expand road capacity; the travel time cost after expanding road capacity is as shown in the formula above.

[0071] S28. Establish electric vehicle charging demand constraints, specifically:

[0072] ;

[0073] ;

[0074] In the formula, This indicates the electricity consumption of an electric vehicle per kilometer. This indicates the total distance traveled by the electric vehicle. This represents the distance traveled by each electric vehicle, calculated based on the road segments selected after the route is chosen.

[0075] The charging needs of electric vehicles depend on their driving distance and energy consumption per kilometer, hence the constraints mentioned above.

[0076] S3. Based on the objective function, the distribution network model constraints, the static traffic flow allocation model constraints, the traffic network reconfiguration constraints, the travel time cost constraints, and the electric vehicle charging demand constraints, a planning model for electric vehicle fast charging stations is obtained.

[0077] S4. Solve the electric vehicle fast charging station planning model to obtain the fast charging station planning results.

[0078] According to another aspect of the invention, Figure 2 This is a schematic diagram illustrating an electric vehicle fast charging station planning system according to an embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of an electric vehicle fast charging station planning method as described above.

[0079] In summary, the electric vehicle fast charging station planning method and system described above by this invention integrates multi-dimensional costs to construct an objective function, taking into account the investment, operation and maintenance costs of fuel cell power plants and charging stations, as well as travel time costs, achieving dual optimization of economy and efficiency. Simultaneously, it integrates constraints from multiple systems such as power distribution networks, traffic flow, traffic network reconfiguration, and charging demand, ensuring that charging station planning aligns with power grid operation safety, traffic network efficiency, and the actual charging needs of electric vehicles, avoiding a disconnect between planning and actual operation. The planning results obtained through the solution enable coordinated optimization of the power distribution network and the traffic network, ensuring stable operation of the power distribution network, reducing the overall cost of energy facilities, optimizing traffic flow allocation, reducing travel time losses, and accurately matching the fast charging needs of electric vehicles, thereby improving charging service efficiency. This ensures effective satisfaction of electric vehicle charging needs while reducing the number of fast charging stations that need to be built.

[0080] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for planning fast charging stations for electric vehicles, characterized in that, Including the following steps: Establish an objective function that minimizes the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost; Establish constraints for the power distribution network model, static traffic flow allocation model, traffic network reconfiguration, travel time cost, and electric vehicle charging demand. Based on the objective function, the power distribution network model constraints, the static traffic flow allocation model constraints, the traffic network reconfiguration constraints, the travel time cost constraints, and the electric vehicle charging demand constraints, a planning model for electric vehicle fast charging stations is obtained. The electric vehicle fast charging station planning model is solved to obtain the fast charging station planning results. The objective function for minimizing the sum of total investment cost, fuel cell power plant operation and maintenance cost, and total travel time cost includes: Obtain the economic life, discount rate, investment cost, and operation and maintenance cost of the fast charging station; Get the traffic flow of each road segment at different times of the day and the number of fast charging stations installed at each node; The total investment cost is obtained based on the economic life, the discount rate, the investment cost, and the number of fast charging stations installed at each node. The operation and maintenance cost of the fuel cell power plant is obtained based on the operation and maintenance cost and the number of fast charging stations installed at each node. The total travel time cost is obtained based on the traffic flow on each road segment during different time periods of the day. An objective function is constructed with the goal of minimizing the sum of the total investment cost, the operation and maintenance cost of the fuel cell power plant, and the total travel time cost. The constraints for establishing the static traffic flow assignment model are as follows: ； ； ； ； ； In the formula, Represents a set of paths. Representing a path Traffic flow within a day Indicates a start-end pair Total transportation demand Represents the set of intersection nodes in the transportation network. Indicates included road segments path Traffic flow on the road Indicates the time from node within a day To the node Directional section Traffic flow on the road Indicates the time from node within a day To the node Directional section Traffic flow on the road Indicates a start-end pair The starting node, Indicates a start-end pair The endpoint Indicates a collection of road segments in a transportation network. Represents a set of time periods. Indicates a time period within a day Next node To the node Directional section Traffic flow on the road; Establish constraints for transportation network reconstruction, specifically: ； ； In the formula, Indicates time period Slave node after link reversal To the node Capacity in direction, Indicates the slave node before the link reversal To the node Capacity in direction, Indicates the total number of lanes. Indicates from node To the node The original capacity of the direction, Indicates the percentage of road carrying capacity. This indicates an auxiliary parameter used to reflect the number of lanes in reverse. One approach to alleviate traffic congestion in the all-time network is to reverse the traffic flow in one direction. When the traffic flow in one direction is about to reach its original capacity during peak hours, the capacity of that direction is expanded by reversing several lanes in the opposite direction.

2. The electric vehicle fast charging station planning method according to claim 1, characterized in that, The total investment cost is obtained based on the economic life, the discount rate, the investment cost, and the number of fast charging stations installed at each node, specifically as follows: ； ； In the formula, Indicates the total investment cost. Represents auxiliary variables. Indicates the number of nodes in the transportation network. This indicates the investment cost per unit of a fast charging station. Indicates that it is installed on the node The number of fast charging stations in the area. Indicates the discount rate. Indicates the economic lifespan of a fast charging station; The operation and maintenance cost of the fuel cell power plant is obtained based on the aforementioned operation and maintenance cost and the number of fast charging stations installed at each node, specifically as follows: ； In the formula, This represents the operating and maintenance costs of a fuel cell power plant. This represents the operating and maintenance cost per unit of a fast charging station; The total travel time cost is calculated based on the traffic flow on each road segment during different time periods throughout the day, specifically as follows: ； In the formula, This represents the total travel time cost. Indicates road segment In time period The electric vehicle driving cost function.

3. The electric vehicle fast charging station planning method according to claim 2, characterized in that, The constraints for establishing the distribution network model include: Obtain the active power of each branch, the active power of each bus, the inherent load at each node, and the charging demand of electric vehicles at each node; Power balance constraints are constructed based on the active power of each branch, the active power of each bus, the inherent load at each node, and the charging demand of electric vehicles at each node. Establish upper and lower voltage limits and branch current constraints; The power balance constraint, the voltage upper and lower limit constraint, and the branch current constraint are used to obtain the distribution network model constraints.

4. The electric vehicle fast charging station planning method according to claim 3, characterized in that, A power balance constraint is constructed based on the active power of each branch, the active power of each bus, the inherent load at each node, and the charging demand of electric vehicles at each node, specifically as follows: ； ； In the formula, This represents the set of child nodes of a distribution network node. Indicates the time period branch road active power, Indicates the time period busbar active power, This represents the set of parent nodes of a distribution network node. Indicates the time period branch road active power, Represents the set of nodes in the distribution network. Indicates the time period node The inherent load at the location, Indicates the time period node The charging demand for electric vehicles in the area; Establish upper and lower voltage limits, specifically as follows: ； In the formula, This indicates the lower limit of the voltage amplitude limit. This indicates the upper limit of the voltage amplitude limit. Indicates the time period node Voltage amplitude at the location; Establish branch current constraints, specifically as follows: ； In the formula, Indicates the time period branch road The current, Indicates a branch The maximum allowable current.

5. The electric vehicle fast charging station planning method according to claim 1, characterized in that, Establish travel time cost constraints, specifically as follows: ； In the formula, Indicating the economic value of time Indicates road segment Travel time cost when traffic is not congested.

6. The electric vehicle fast charging station planning method according to claim 1, characterized in that, Establish electric vehicle charging demand constraints, specifically: ； ； In the formula, This indicates the electricity consumption of an electric vehicle per kilometer. This indicates the total distance traveled by the electric vehicle. This represents the distance traveled by each electric vehicle, calculated based on the road segments selected after the route is chosen.

7. A fast charging station planning system for electric vehicles, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the electric vehicle fast charging station planning method according to any one of claims 1 to 6.

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