An electric vehicle charging and discharging benefit optimization method based on vehicle-to-grid (V2G) interaction

Abstract

The application discloses a kind of electric vehicle charging and discharging benefit optimization method based on vehicle-network interaction V2G, and vehicle can increase its benefit by reverse delivery of its excess power to power grid and improve renewable energy utilization rate.The application proposes a multi-vehicle charging and discharging benefit optimization method based on cooperative game and greedy strategy for the multi-agent and multi-constraint characteristics in the problem scenario, which realizes the goal of maximizing the income of vehicle group under the consideration of vehicle maximum battery capacity limit, starting point destination and other constraints, limited charging pile number and waiting position of charging station, traffic path accessibility, and multi-vehicle scheduling scheme competition conflict situation.The method of the application is simple to calculate, the result is effective, and has feasibility and superiority in the electric vehicle charging and discharging scenario based on V2G network.In addition, the application has good reusability in similar application scenarios, and has strong practical value.

Description

Technical fields:

[0001] This invention belongs to the field of Internet of Things, specifically relating to a method for optimizing the charging and discharging efficiency of electric vehicles based on vehicle-to-grid (V2G) interaction. Background technology:

[0002] Traditional gasoline-powered vehicles consume fossil fuels, and the hydrocarbons they release harm the environment and human health. With the development of new technologies, the improved efficiency of renewable energy generation such as wind and solar power, and the large-scale construction of electric vehicle charging stations in cities to stimulate electric vehicle use, electric vehicles are the future trend, considering factors such as environmental protection, energy conservation, cost-effectiveness, and convenience. However, the high penetration rate of electric vehicles may lead to the risk of grid overload. Furthermore, the instability and intermittency of renewable electricity make it difficult to connect to the grid. The large-scale charging of electric vehicles will have a potential impact on the power grid.

[0003] To address the aforementioned issues, Vehicle-to-Grid (V2G) technology has emerged as a promising approach, widely recognized as a mobile grid. V2G describes the relationship between electric vehicles (EVs) and the power grid. When an EV is not in use, the energy from its battery is sold to the grid. If the battery needs charging, current flows from the grid to the vehicle. This technology fully utilizes the bidirectional energy transfer between charging stations and EVs, allowing EVs to offload excess energy to the grid. Therefore, EVs carrying high-capacity batteries can be considered energy transport vehicles in cities, forming large-scale distributed energy storage systems that help alleviate energy shortages during peak hours. Simultaneously, V2G technology plays a crucial role in addressing peak-shaving and valley-filling energy storage solutions, improving grid efficiency, and promoting the low-carbon development of the energy structure. Through peak-shaving and valley-filling, users can choose to purchase electricity from the grid during low prices (valleys) and sell it back during high prices (peaks), achieving peak-shaving and valley-filling while also generating revenue. Currently, in some cities, the integration of renewable energy power plants with traditional power grids has shown good economic benefits.

[0004] Furthermore, the competitive and cooperative relationships among multiple electric vehicles are also considered in this method. Multi-vehicle joint benefit optimization differs from single-vehicle optimization. The competitive use of limited resources (charging stations in this method) by multiple vehicles means that not every vehicle can achieve overall optimality by following the optimal path found by a single vehicle. Instead, all vehicles choosing the same path increases competition among nodes on that path, resulting in only a few vehicles receiving the intended benefits while the rest incur costs along the route, even though they cannot charge or discharge. This also causes severe traffic congestion on that path, hindering traffic management. Simultaneously, the large number of unused charging stations on other routes represents a waste of resources.

[0005] Game theory is a classic method for solving multi-agent decision-making problems. It is often used to model conflicts and cooperation among intelligent, rational decision-makers, making it particularly suitable for studying the charging and discharging efficiency optimization problem in vehicle networks, considering the competition and cooperation among multiple electric vehicles. It mainly consists of three factors: participants, a set of strategies, and a utility function, and can solve various practical application problems. In game theory, each participant's chosen strategy affects the decisions of other participants. Ultimately, each participant's goal is to maximize their payoff and profit. Game theory is generally divided into two branches: non-cooperative games (competitive) and cooperative games. Non-cooperative games can be applied to demand-side management (DSM) modeling, real-time monitoring of microgrids, implementation and control, and market and dynamic pricing. Cooperative game theory includes two main branches: coalition games and Nash games. In coalition games, participants can form coalitions to significantly increase or decrease their own payoffs and costs. Game theory has been widely applied in many fields. Some scholars have proposed a collaborative edge caching framework based on game theory to simultaneously address three issues in short video application scenarios: data latency, heavy cloud server workload, and high backbone network data traffic pressure. Other experts have applied game theory to taxi-sharing recommendation mechanisms to increase taxi revenue. In the field of computer networks, game theory can help us understand the behavioral patterns of participants, from resource allocation to simulating competition between various entities. In recent years, game theory has also been widely applied in intelligent transportation systems and smart grids, such as traffic signal control, travel time reliability, and optimal V2G pricing mechanisms for aggregators against their competitors. Therefore, this paper proposes a V2G-based method for optimizing the charging and discharging efficiency of electric vehicles using game theory modeling. Summary of the Invention:

[0006] The purpose of this invention is to solve the problem of maximizing the benefits of vehicle groups under various real-world constraints in V2G scenarios, and to provide a V2G-based method for optimizing the charging and discharging efficiency of electric vehicles.

[0007] A method for optimizing the charging and discharging efficiency of electric vehicles based on V2G includes the following steps:

[0008] (1) The problem is modeled by mapping real-world entities involved in the application scenario, such as electric vehicles, new energy charging stations, traditional energy charging and discharging stations, and road conditions along the vehicle's route, into graph theory node weights. The model mainly includes nodes in the graph consisting of new energy charging stations, traditional energy charging and discharging stations, and ordinary intersections, as well as weight information consisting of distances between adjacent stations, electricity consumption during the journey, and travel time. Due to the different properties of new energy and traditional energy, electric vehicles can be charged at renewable energy charging stations at a lower price, while selling excess electricity beyond the energy required for normal vehicle passage at traditional energy stations at a higher price to generate revenue. The price of charging at a new energy station is much lower than the price of discharging at a traditional station.

[0009] (2) In view of the constraints in the scenario, including vehicle power constraints, site condition constraints, road information constraints, etc., the graph model in the previous step is expanded in the time dimension to describe the state information of different stations or different vehicles at different times. That is, the time extension graph is used as an abstract expression of the research problem. Specifically, it includes the state information of each node in the vehicle road condition graph at different times, such as the number of vehicles charging and discharging, the number of vehicles waiting in the queue, and the power consumption time status of the vehicle at different times.

[0010] (3) Considering the competitive and collaborative relationships among multiple workshops, each vehicle is taken as a participant, the station is taken as a strategy set, and the goal is to maximize the benefits of the vehicle group. Based on game theory, a multi-agent dynamic cooperative game model is established to realize the station selection and path planning problem of vehicles. On this basis, a greedy strategy is combined to complete the optimization problem of electric vehicle charging and discharging efficiency based on V2G.

[0011] Furthermore, the various practical constraints in step (2) can be described by formulas, wherein the constraints on the vehicle's own battery power can be expressed by the following formula:

[0012]

[0013]

[0014] Where N represents the set of nodes in step (1), E max , Let $\mathbf{j}$ represent the maximum capacity of the electric vehicle battery, the state of charge of the vehicle when it leaves station $j$, and the state of charge of the vehicle when it arrives at station $j$ and leaves station $j$, respectively. This formula means that the energy of the vehicle at any node and at any time cannot be negative and cannot exceed the maximum capacity of the battery.

[0015] The conditions and constraints for the site in step (2) can be expressed by the following formula:

[0016]

[0017]

[0018] X ij =1

[0019] X jend =1

[0020] in These represent the number of vehicles charging at station j at time t, the number of vehicles discharging at station j at time t, and the number of vehicles waiting at station j at time t, respectively. j wait X represents the total number of charging piles, the number of discharging piles, and the maximum waiting position at station j, respectively. This formula expresses the principle that the number of charging / discharging vehicles and the number of waiting vehicles at any station on the vehicle road condition map must not exceed the number of charging / discharging piles and the maximum waiting position at that point. ij =1 indicates that the current station i is reachable from the next hop station j in the road graph, X jend =1 indicates that the selected next-hop station j is reachable from the vehicle's destination on the road map, ensuring the selectability of stations.

[0021] The road information constraints in step (2) can be expressed by the following formula:

[0022]

[0023] Where in(j) and out(j) are the in-degree edge set and out-degree edge set of node j, Start represents the starting point of the vehicle, End represents the ending point of the vehicle, and N represents the set of nodes in step (1). This formula expresses the continuity of the route, where the in-degree of an intermediate node is equal to its out-degree. It is a 0 / 1 variable representing whether an electric car named Car passes through edge i. Similarly.

[0024] Furthermore, the specific implementation process of step (3) is as follows:

[0025] 3.1 A multi-agent cooperative game model is established with vehicles as participants, stations as the strategy set, and the goal of maximizing the benefits of the vehicle group.

[0026] 3.2 Remove stations that are unreachable to the vehicle's destination from the adjacency matrix of the current stations to obtain the filtered path set S. i ;

[0027] 3.3 Based on this, the next-hop site is divided into a set of nodes that can charge and discharge without queuing. A set of nodes where all charging positions are occupied but nodes can wait in their designated positions to charge or discharge. And node sets where charging / discharging and waiting positions are all full or at ordinary intersections.

[0028] 3.4 pairs The revenue of each element is calculated as a temporary destination for the vehicle's current step, and the station that maximizes the vehicle's revenue is selected as the vehicle's true destination for the current step.

[0029] 3.5 If For an empty set, The revenue of each element is calculated as a temporary destination for the vehicle's current step, and the station that minimizes the vehicle's waiting time is selected as the vehicle's true destination for this step.

[0030] 3.6 If When all sets are empty, for S i The elements in the middle are respectively used as ordinary nodes and temporary current stations of the vehicle, and as the first hop. The adjacency matrix of the vehicle continues to be processed from step 3.2 to this step until a station that meets the conditions is found or the vehicle's destination is reached in step 3.4 or 3.5. The station that minimizes the number of intermediate hops of the vehicle is selected as the vehicle's real next station.

[0031] 3.7 Based on the weight information between the vehicle's current station and the destination station selected in 3.4 to 3.6 for the current step, calculate and update the vehicle's revenue, time expenditure, remaining energy value, and current station status information according to the greedy strategy, and repeat steps 3.2 to 3.7 until the vehicle reaches its predetermined destination.

[0032] Furthermore, the model formulation of step 3.1 is as follows:

[0033] N = {1, 2, 3, ..., n}

[0034]

[0035]

[0036]

[0037] Where N represents the participants in game theory, and here refers to each electric vehicle in the system, S i Let represent the path planning of the vehicle, consisting of the decisions made by the i-th participant at each step. The first point along the route, which is the departure point, Let represent the m-th station passed sequentially along the path, and ki represent the total number of stations passed by the i-th participant from the starting point to the destination. S represents the total planned path set for n vehicles. This represents the profit of the vehicle when it reaches its destination (end). This indicates that the core objective of the present invention is to maximize the benefits to the vehicle group.

[0038] Furthermore, the mathematical expression of the greedy strategy in step 3.7 is as follows:

[0039]

[0040]

[0041]

[0042] in, E represents the battery level of the vehicle when it leaves station j. max Indicates the maximum capacity of the vehicle's battery, e jk This indicates the amount of electricity required for the vehicle to travel from the current station j to the destination station k selected in steps 3.3 to 3.5 above. This indicates the battery level of the vehicle when it leaves the starting point. This indicates the vehicle's initial battery level. This indicates the battery level of the vehicle when it leaves the destination. This indicates the vehicle's battery level when it reaches its destination. R represents a new energy charging station, G represents a traditional grid charging station, and C represents a non-charging station intersection.

[0043] Furthermore, the updated mathematical expression for vehicle revenue in step 3.7 is as follows:

[0044]

[0045] in, P represents the revenue when the vehicle leaves station j. ij This represents the economic cost of traveling from the previous station i to the current station j. These indicate whether the current station is a traditional power grid charging station or a new energy charging station, respectively, and W. jd W jc These represent the unit price for the vehicle to discharge and recharge at point j.

[0046] Furthermore, the mathematical expression for updating the vehicle's remaining energy value in step 3.7 is as follows:

[0047]

[0048] Furthermore, the mathematical expression for updating the vehicle time spent in step 3.7 is as follows:

[0049]

[0050] in, t represents the time elapsed since the vehicle left station j. ij Pow represents the time taken to travel from the previous site i to the current site j. j This indicates the charging and discharging rate of the vehicle at point j. This indicates the waiting time for vehicles to queue at point j.

[0051] The beneficial effects of this invention are:

[0052] This invention proposes a multi-vehicle charging and discharging efficiency optimization method based on cooperative game theory and a greedy strategy, addressing the multi-agent, multi-constraint characteristics of V2G electric vehicle charging and discharging scenarios. It aims to maximize the benefits of the vehicle group while simultaneously considering constraints such as maximum vehicle energy limits, origin and destination points, the limited number of charging piles and waiting positions at charging stations, traffic path accessibility, and competition and conflict in multi-vehicle scheduling schemes. The method is computationally simple, yields effective results, and demonstrates feasibility and superiority in V2G-based electric vehicle charging and discharging scenarios. Furthermore, this invention exhibits good reusability in similar application scenarios, demonstrating strong practical value. Attached image description:

[0053] Figure 1 This is a schematic diagram of a system scenario for the electric vehicle charging and discharging efficiency optimization method of the present invention;

[0054] Figure 2 This is a V2G network schematic diagram of the electric vehicle charging and discharging efficiency optimization method of the present invention;

[0055] Figure 3 This is a schematic diagram of the execution flow of the electric vehicle charging and discharging efficiency optimization method of the present invention;

[0056] Figure 4 This is a schematic diagram illustrating the site selection method for optimizing the charging and discharging efficiency of electric vehicles according to the present invention.

[0057] Figure 5 This is a schematic diagram illustrating the benefit effect of the electric vehicle charging and discharging efficiency optimization method of the present invention; Detailed implementation method:

[0058] To enable those skilled in the art to better understand the technical content of this invention, the technical solution is described in detail below with reference to the accompanying drawings and specific embodiments. This invention discloses a method for optimizing the charging and discharging efficiency of electric vehicles based on vehicle-to-grid (V2G) interaction. Vehicles can increase their own efficiency and improve the utilization rate of renewable energy by transmitting their excess electricity back to the grid. This invention proposes a multi-vehicle charging and discharging efficiency optimization method based on cooperative game theory and a greedy strategy, addressing the multi-agent, multi-constraint characteristics of this problem scenario. It achieves the goal of maximizing the benefits of the vehicle group while simultaneously considering constraints such as the maximum battery capacity of vehicles, starting point and destination, the limited number of charging piles and waiting positions at charging stations, traffic path accessibility, and competition and conflict in multi-vehicle scheduling schemes. The method of this invention is computationally simple, yields effective results, and is feasible and superior in V2G network-based electric vehicle charging and discharging scenarios. Furthermore, this invention has good reusability in similar application scenarios and strong practical value.

[0059] A method for optimizing the charging and discharging efficiency of electric vehicles based on V2G, with an implementation example including the following steps:

[0060] Step 1: Map the real-world entities involved in the application scenario, such as electric vehicles, new energy charging stations, traditional energy charging and discharging stations, and road conditions along the vehicle's route, into graph theory information such as node weights to model the problem, as shown in the attached diagram. Figure 1 The system scenario diagram is shown below;

[0061] Step 2: Mathematically express the various real-world constraints in the scenario, including vehicle battery power constraints, site condition constraints, and road information constraints, as shown below:

[0062]

[0063]

[0064] Where N represents the set of nodes in step (1), E max , These represent the maximum capacity of the electric vehicle battery, the state of charge of the vehicle when it leaves station j, and the state of charge of the vehicle when it arrives at station j and leaves, respectively.

[0065]

[0066]

[0067] X ij =1

[0068] X jend =1

[0069] in These represent the number of vehicles charging at station j at time t, the number of vehicles discharging at station j at time t, and the number of vehicles waiting at station j at time t, respectively. j wait X represents the total number of charging piles, the number of discharging piles, and the maximum waiting position at station j, respectively. ij =1 indicates that the current station i is reachable from the next hop station j in the road graph, X jend =1 indicates that the selected next-hop station j is reachable from the vehicle's destination on the road map.

[0070]

[0071] Wherein(j) and out(j) are the in-degree edge set and out-degree edge set of node j, Start represents the starting point of the vehicle, End represents the ending point of the vehicle, and N represents the set of nodes in step 1.

[0072] Step 3: Expand the graph model from Step 1 in conjunction with the formula from Step 2 in the time dimension to describe the status information of different stations or different vehicles at different times, that is, use the time-extended graph as an abstract expression of the research problem.

[0073] Step 4: Using vehicles as participants, stations as the strategy set, and maximizing the benefits of the vehicle group as the objective, establish a multi-agent cooperative game model based on the following formula.

[0074] N = {1, 2, 3, ..., n}

[0075]

[0076]

[0077]

[0078] Where N represents each electric vehicle in the system, S i Let represent the path planning of the vehicle, consisting of the decisions made by the i-th participant at each step. The first point passed along the path, Let represent the m-th station passed sequentially along the path, and ki represent the total number of stations passed by the i-th participant from the starting point to the destination. S represents the total planned path set for n vehicles. This represents the profit of the vehicle when it reaches its destination (end). This indicates that the core objective of the present invention is to maximize the benefits to the vehicle group.

[0079] Step 5: Remove stations that are unreachable from the vehicle's destination from the adjacency matrix of the current station to obtain the filtered path set S. i ;

[0080] Step 6: Based on this, divide the next-hop sites into a set of nodes that can charge and discharge without queuing. A set of nodes where all charging positions are occupied but nodes can wait in their designated positions to charge or discharge. And node sets where charging / discharging and waiting positions are all full or at ordinary intersections.

[0081] Step 7, for The revenue of each element is calculated as a temporary destination for the vehicle's current step, and the station that maximizes the vehicle's revenue is selected as the vehicle's true destination for the current step.

[0082] Step 8, if For an empty set, The revenue of each element is calculated as a temporary destination for the vehicle's current step, and the station that minimizes the vehicle's waiting time is selected as the vehicle's true destination for this step.

[0083] Step 9, if When all sets are empty, for S i The elements in the middle are respectively used as ordinary nodes and temporary current stations of the vehicle, and as the first hop. The adjacency matrix of the vehicle continues to be processed from step 3.2 to this step until a station that meets the conditions is found or the vehicle's destination is reached in step 3.4 or 3.5. The station that minimizes the number of intermediate hops of the vehicle is selected as the vehicle's real next station.

[0084] Step 10: Based on the weight information between the vehicle's current station and the selected destination station for this step, calculate and update the vehicle's revenue, time expenditure, remaining energy value, and other status information according to a greedy strategy. Repeat steps 5 to 9 until the vehicle reaches its predetermined destination. The overall execution steps of the method are shown in the appendix. Figure 3 The method execution flow diagram is shown below, and the node selection steps are as follows: Figure 4 The site selection diagram is shown below. The mathematical expressions for updating vehicle revenue, remaining energy value, and time spent are as follows:

[0085]

[0086]

[0087]

[0088] in, P represents the revenue when the vehicle leaves station j. ij This represents the economic cost of traveling from the previous station i to the current station j. These indicate whether the current station is a traditional power grid charging station or a new energy charging station, respectively, and W. jd Wjc These represent the unit price for the vehicle to discharge and recharge at point j. t represents the time elapsed since the vehicle left station j. ij Pow represents the time taken to travel from the previous site i to the current site j. j This indicates the charging and discharging rate of the vehicle at point j. This indicates the waiting time for vehicles to queue at point j.

[0089] After completing the above steps, this method was tested for profitability when the number of vehicles in the system ranged from 110 to 490. It was also compared with existing methods that do not consider V2G based on shortest paths, methods that do not consider multi-vehicle cooperation where each vehicle follows the optimal path in single-vehicle mode, and centralized scheduling schemes based on V2G. The optimization results are attached. Figure 5 The diagram illustrates the benefits of this method. The results show that, in all scenarios, the benefits of this method are greater than those of the other three.

[0090] It should be understood that any parts not described in detail in this specification belong to the prior art. Those skilled in the art should understand that the above embodiments are merely to help readers understand the principles and implementation methods of the present invention, and the scope of protection of the present invention is not limited to such embodiments. All equivalent substitutions made based on the present invention are within the scope of protection of the present invention.

Claims

1. A method for optimizing the charging and discharging efficiency of electric vehicles based on vehicle-to-grid (V2G) interaction, characterized in that... The implementation process of this method is as follows: (1) The entities involved in the application scenario, such as electric vehicles, new energy charging stations, traditional energy charging and discharging stations, and road conditions along the vehicle route, are mapped into the node weight information of graph theory to realize the problem model and obtain the graph model; (2) In view of the real constraints in the application scenario, including vehicle power constraints, station condition constraints and road information constraints, the graph model in step (1) is expanded in the time dimension to describe the state information of different times, different stations or different vehicles. That is, the time extension graph is used as an abstract expression of the research problem. (3) Each vehicle is taken as a participant, and the pre-processed relevant stations are taken as the strategy set corresponding to the participants. The goal is to maximize the benefits of the vehicle group. Based on game theory, a multi-agent dynamic cooperative game model is established to realize the station selection and path planning problem of the vehicle. On this basis, a greedy strategy is combined to complete the V2G-based electric vehicle charging and discharging efficiency optimization problem. The specific implementation process of the vehicle cooperative game idea in step (3) is as follows: 3.1 Implement the preprocessing of the decision set in game theory described in step (3). Specifically, remove stations that are unreachable from the vehicle's destination from the adjacency matrix of the current station i to obtain the path-filtered set S. i ; 3.2 Similarly, the decision set preparation step in game theory modeling involves obtaining the set S in step 3.

1. i Based on this, the next-hop site is divided into a set of nodes that can charge and discharge without queuing. A set of nodes where all charging positions are occupied but nodes can wait in their designated positions to charge or discharge. And node sets where charging / discharging and waiting positions are all full or at ordinary intersections. Complete the decision set preprocessing steps; 3.3 After determining the participants and the decision set, the vehicle makes decisions based on the benefit function for the elements in the decision set. Specifically, the vehicle will... Each element in the algorithm is used as a temporary next-hop station for the current step to calculate the revenue. The next-hop station that maximizes the vehicle's revenue is selected as the vehicle's true destination for the current step. 3.4 If the above is described in step 3.3 If it is an empty set, then the vehicles will be set. The middle element is used as the temporary next hop station for the current step of the vehicle to calculate the revenue, and the next hop station that minimizes the vehicle's waiting time is selected as the vehicle's true destination for this step. 3.5 If set and If all sets are empty, then set S will be... i The elements in the middle are respectively used as ordinary nodes and temporary current stations of the vehicle, and as the first hop. The adjacency matrix of the vehicle continues to be processed from step 3.1 to step 3.5 until a station that meets the conditions is found or the vehicle reaches its destination in step 3.3 or step 3.

4. The station that minimizes the number of intermediate hops of the vehicle is selected as the vehicle's real next hop station. Thus, the vehicle achieves the goal of selecting the next hop station using cooperative game theory under multi-agent system based on the processed policy set state. 3.6 Based on the weight information between the vehicle's current station and the next hop station selected in steps 3.3 to 3.5, calculate and update the vehicle's revenue, time cost, remaining energy value, and current station status information according to a greedy strategy, and repeat steps 3.1 to 3.6 until the vehicle reaches its predetermined destination.

2. The method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 1, characterized in that: In step (1), the nodes in the graph model consist of new energy charging stations, traditional energy charging and discharging stations, and ordinary intersections. Vehicles are charged at new energy charging stations at low prices and sold at high prices at traditional energy charging and discharging stations for excess electricity beyond what is necessary to ensure normal vehicle passage, thus generating revenue. There is a relationship where the charging price at new energy stations is less than the discharge price at the grid, which is less than the charging price at the grid. The weights in the graph model include information on the distance between adjacent stations, the electricity consumption between adjacent stations, and the time required between adjacent stations.

3. A method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 1 or 2, characterized in that... The constraint on the vehicle's own battery power in step (2) can be expressed by the following formula: Where N represents the set of nodes being described, E max , Let $\mathbf{j}$ represent the maximum capacity of the electric vehicle battery, the battery state of the vehicle when it leaves station j, and the battery state of the vehicle when it arrives at station j, respectively. This formula means that the battery state of the vehicle at any node and at any time cannot be negative and cannot exceed the maximum battery capacity.

4. A method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 1 or 2, characterized in that... The site condition constraints in step (2) are expressed by the following formula: X ij ＝1 (5) X jend ＝1 (6) in These represent the number of vehicles charging at station j at time t, the number of vehicles discharging at station j at time t, and the number of vehicles waiting at station j at time t, respectively. j wait X represents the total number of charging piles, the number of discharging piles, and the maximum waiting position at station j, respectively; this formula expresses the principle that the charging / discharging capacity and the number of waiting vehicles at any station in the graphical model must not exceed the number of charging / discharging piles and the maximum waiting position at that point; ij =1 indicates that the current station i is reachable from the next hop station j in the road graph, X jend =1 indicates that the selected next-hop station j is reachable from the vehicle's destination on the road map, ensuring the selectability of stations.

5. A method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 1 or 2, characterized in that... The road information constraint in step (2) can be expressed by the following formula: Where in(j) and out(j) are the in-degree edge set and out-degree edge set of node j, Start represents the starting point of the vehicle, End represents the ending point of the vehicle, and N represents the set of nodes; this formula expresses the continuity of the route, where the in-degree of an intermediate node is equal to its out-degree. It is a 0 / 1 variable representing whether an electric car named 'car' passes through edge 'i'. Similarly.

6. The method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 1 or 2, characterized in that: The expression for the greedy strategy in step 3.6 is as follows: in, E represents the battery level of the vehicle when it leaves station j. max Indicates the maximum capacity of the vehicle's battery, e jk This indicates the amount of electricity required for the vehicle to travel from the current station j to the next hop station k selected in steps 3.3 to 3.5 above. This indicates the battery level of the vehicle when it leaves the starting point. This indicates the vehicle's initial battery level. This indicates the battery level of the vehicle when it leaves the destination. This indicates the vehicle's battery level when it reaches its destination; R represents a new energy charging station, G represents a traditional power grid charging and discharging station, and C represents a non-charging station intersection.

7. The method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 6, characterized in that: The update expression for vehicle revenue in step 3.6 is as follows: in, P represents the revenue when the vehicle leaves station j. ij e represents the economic cost of traveling from the previous station i to the current station j. ij This represents the amount of electricity consumed between the previous station i and the current station j. These indicate whether the current station is a traditional power grid charging / discharging station or a new energy charging station, respectively, and W. jd W jc These represent the unit price for the vehicle to discharge and recharge at station j.

8. The method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 7, characterized in that: The expression for updating the vehicle's remaining battery power in step 3.6 is as follows:

9. The method for optimizing the charging and discharging efficiency of electric vehicles based on V2G according to claim 8, characterized in that: The vehicle time cost update expression in step 3.6 is as follows: in, t represents the time elapsed since the vehicle left station j. ij Pow represents the time taken to travel from the previous site i to the current site j. j This indicates the charging and discharging rate of the vehicle at station j. This indicates the waiting time for the vehicle at station j.

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