Electric vehicle charging and discharging dynamic path planning method, system, equipment and medium

Abstract

The invention relates to the technical field of electric vehicle path planning, and provides an electric vehicle charging and discharging dynamic path planning method, system and device and a medium. Power grid real-time information obtained based on the dynamic road network model is obtained, including road network real-time information of power grid basic discharge excitation scheduling electricity price, power grid basic charging service electricity price and basic active load of each power grid region, and charging station real-time information including aggregator charging pile utilization rate; and based on charging and discharging demand information obtained according to a charging and discharging request of the electric vehicle and power grid real-time information, road network real-time information and charging station real-time information matched with the charging and discharging request, performing dynamic path planning based on the path optimization model to obtain a corresponding target charging station. According to the method, real-time applicable path planning can be carried out on the electric vehicle, meanwhile, the charging and discharging requirements of a user can be effectively met, the peak load shifting effect of a power grid and balanced distribution of road network flow are ensured, and then overall benefit maximization is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle path planning, and in particular to a method, system, equipment and medium for dynamic path planning of electric vehicle charging and discharging. Background Art

[0002] As the number of electric vehicles increases, their charging needs have a significant impact on the safe and stable operation of the power grid. The randomness and volatility of electric vehicle charging behavior in time and space not only complicates grid operation and control but can also lead to increased peak-to-valley load variations, harmonic pollution, reduced power quality, and increased network losses. Furthermore, when parked, electric vehicles can serve as distributed energy storage devices, participating in the grid's charging and discharging processes to smooth out renewable energy fluctuations, reduce peak loads, and provide auxiliary services such as frequency regulation. The application of this vehicle-to-grid (V2G) technology not only improves grid efficiency but also promotes the deep integration of electric vehicles and smart grids, driving the sustainable development of energy and transportation systems. Reasonable route planning plays a crucial role in the overall V2G framework.

[0003] Existing research on electric vehicle path planning mainly focuses on the simple integration of road network and power grid information. However, this integration method does not reflect the spatial regional characteristics and the dynamic characteristics of the power grid. It pays little attention to the mutual influence between electric vehicles, charging stations, transportation road networks and distribution networks. It cannot guarantee the real-time and applicability of electric vehicle charging and discharging path planning, and it is difficult to effectively improve the utilization efficiency of electric vehicles and the stability of the power grid under the V2G framework. Summary of the Invention

[0004] The purpose of the present invention is to provide a dynamic path planning method for electric vehicle charging and discharging. By fully considering the mutual influence between electric vehicles, charging stations, transportation road networks and distribution networks, as well as the dynamic characteristics of the power grid and the road network, an electric vehicle path planning mechanism is designed. In combination with minimizing time cost, energy consumption cost and economic cost as the optimization goal, a path optimization model is constructed to realize real-time dynamic path planning for electric vehicles, while also effectively meeting the charging and discharging needs of users, ensuring the operational stability of the power grid, ensuring the peak shaving and valley filling effect of the power grid and the balanced distribution of road network traffic, thereby maximizing overall benefits.

[0005] In order to achieve the above objectives, it is necessary to provide a method, system, device and medium for dynamic path planning of electric vehicle charging and discharging in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for dynamic path planning for charging and discharging of an electric vehicle, the method comprising the following steps:

[0007] Acquire real-time grid information, road network information, and charging station information based on a preset collection cycle; the road network information is derived from a dynamic road network model constructed using graph theory; the grid information includes the grid's basic discharge incentive dispatch price, the grid's basic charging service price, and the regional basic active load for each grid region; the charging station information includes the utilization rate of aggregator charging piles in each grid region;

[0008] In response to the charge and discharge requests of the respective electric vehicles, acquiring charge and discharge demand information corresponding to the charge and discharge requests of the respective electric vehicles; the charge and discharge demand information including one of charge demand information and discharge demand information;

[0009] According to the charging and discharging demand information of each electric vehicle, and the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that match the corresponding charging and discharging request, dynamic path planning is performed based on a pre-built path optimization model to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle.

[0010] Furthermore, the real-time road network information includes real-time road resistance of different road sections in corresponding time periods; the dynamic road network model includes a road network node set, a road section set, a time period set, and a real-time road resistance set; the real-time road resistance of different road sections in the real-time road resistance set is obtained by analyzing the travel time as the road resistance factor, and is expressed as:

[0011] ω ij (t) = Ri j (t)+C i (t)

[0012] Where,

[0013]

[0014] Among them, ω ij (t) represents the real-time road resistance of the road section ij corresponding to the road network node i and the road network node j in the time period t; C i (t) represents the node impedance of the network node i in the period t; R ij (t) represents the impedance of the road section ij during the period t; represents the impedance of the road section ij at zero flow; a1, a2, a3 and a4 represent the road section parameters of the road section ij at the corresponding road level; represents the traffic saturation of road section ij in time period t; represents the free flow time of a vehicle passing through the road network node i; b1, b2, b3 and b4 represent the intersection parameters of the road network node i under the corresponding road level; represents the traffic saturation of the intersection at road network node i in time period t; exp(·) represents the exponential function.

[0015] Furthermore, the charging and discharging demand information includes one of charging demand information and discharging demand information; the path optimization model includes a charging path optimization model and a discharging path optimization model;

[0016] The step of performing dynamic path planning based on a pre-built path optimization model according to the charge and discharge demand information of each electric vehicle, and the real-time information of the power grid, the real-time information of the road network, and the real-time information of the charging station that matches the corresponding charge and discharge request, to obtain a target charging station corresponding to the charge and discharge request of each electric vehicle includes:

[0017] According to the real-time road network information, obtaining a corresponding real-time road network adjacency matrix;

[0018] According to the charging and discharging demand information of each electric vehicle, a corresponding path optimization model is obtained;

[0019] According to the real-time road network adjacency matrix, the real-time power grid information and the real-time charging station information, the optimal path of the path optimization model corresponding to each electric vehicle is solved based on the Dijkstra algorithm to obtain the target charging station of each electric vehicle.

[0020] Furthermore, the path optimization model is constructed with minimizing time cost, energy cost and economic cost as optimization objectives; the steps of constructing the path optimization model include:

[0021] Constructing a path time cost model based on the real-time road resistance in the dynamic road network model;

[0022] Based on the analysis of the impact of vehicle speed and ambient temperature on vehicle energy consumption under different road grades, a path energy cost model is constructed;

[0023] Constructing a discharge economic benefit model based on the basic discharge incentive dispatch electricity price of the power grid, the regional discharge incentive dispatch electricity price adjustment coefficient, and the battery discharge degradation loss cost;

[0024] Constructing a charging economic cost model based on the basic charging service electricity price of the power grid and the regional charging service fee adjustment coefficient;

[0025] Perform weighted synthesis based on the path time cost model, the path energy consumption cost model, and the discharge economic benefit model to construct a discharge path optimization model;

[0026] Constructing a charging path optimization model by weighted synthesis based on the path time cost model, the path energy consumption cost model, and the charging economic cost model;

[0027] According to the discharge path optimization model and the charging path optimization model, the path optimization model is obtained based on preset path optimization constraints; the preset path optimization constraints include dynamic electricity price constraints, discharge depth constraints and charging condition constraints.

[0028] Furthermore, the step of constructing a path energy cost model based on the analysis of the influence of vehicle speed and ambient temperature on vehicle energy consumption under different road grades includes:

[0029] Based on the relationship analysis between vehicle speed and vehicle energy consumption under different road grades, an automobile road section energy consumption factor model is obtained, and according to the automobile road section energy consumption factor model and the road section distance, an automobile road section energy consumption model is constructed;

[0030] Based on the relationship analysis between ambient temperature and vehicle energy consumption, the vehicle temperature control energy consumption model is obtained;

[0031] According to the vehicle road section energy consumption model and the vehicle temperature control energy consumption model, a path energy consumption cost model is obtained; the path energy consumption cost model is expressed as:

[0032] Q ij (t) = Q L,ij (t)+Q T,ij (t)

[0033] Where,

[0034] Q L,ij (t) = ECF ij (t)·L ij

[0035]

[0036] Among them, Q ij (t) represents the total path energy consumption required for the vehicle to complete the road section ij within the time period t; Q L,ij (t) represents the energy consumption required for the vehicle to travel the road section ij within the time period t; L ij Indicates the distance between road segments ij; ECF ij (t) represents the energy consumption factor of the vehicle traveling through the road section ij within the time period t; Q T,ij (t) represents the vehicle speed V during the period t t and the energy consumption of automobile temperature control after the vehicle has traveled through the road section ij under the ambient temperature T; P L and P R Respectively represent the cooling power and heating power of the car air conditioner; T max and T min They represent the cooling temperature threshold and heating temperature threshold of the car air conditioner respectively.

[0037] Furthermore, the step of constructing a discharge economic benefit model based on the grid basic discharge incentive dispatching electricity price, the regional discharge incentive dispatching electricity price adjustment coefficient, and the battery discharge degradation loss cost includes:

[0038] Obtaining a power grid peak shaving coefficient according to a magnitude relationship between the regional basic active load and an average value of the regional basic active load;

[0039] Based on the utilization rate of the charging piles of the aggregator, a discharge incentive coefficient of the electric vehicle aggregator is obtained;

[0040] Obtaining the regional discharge incentive dispatch electricity price adjustment coefficient according to the product of the preset regional discharge incentive dispatch electricity price adjustment coefficient, the grid peak shaving coefficient, and the electric vehicle aggregator discharge incentive coefficient;

[0041] Obtaining a discharge incentive dispatching electricity price according to the product of the basic discharge incentive dispatching electricity price of the power grid and the adjustment coefficient of the regional discharge incentive dispatching electricity price;

[0042] A comprehensive analysis is conducted on the discharge incentive dispatch electricity price, the grid basic discharge incentive dispatch electricity price and the battery discharge degradation loss cost to construct the discharge economic benefit model.

[0043] Furthermore, the step of constructing a charging economic cost model based on the grid basic charging service electricity price and the regional charging service fee adjustment coefficient includes:

[0044] Obtaining a peak-shaving and valley-filling coefficient of the power grid according to the regional basic active load and the allowable fluctuation range of the regional basic active load;

[0045] Based on the utilization rate of the charging piles of the aggregator, a charging incentive coefficient for the electric vehicle aggregator is obtained;

[0046] Obtaining the regional charging service fee adjustment coefficient according to the product of the preset regional charging service fee adjustment coefficient, the grid peak shaving and valley filling coefficient, and the electric vehicle aggregator charging incentive coefficient;

[0047] Obtaining a charging incentive dispatching electricity price based on the product of the grid basic charging service electricity price and the regional charging service fee adjustment coefficient;

[0048] A comprehensive analysis is conducted on the charging incentive dispatch electricity price and the grid basic charging service electricity price to construct the charging economic cost model.

[0049] In a second aspect, an embodiment of the present invention provides a dynamic path planning system for charging and discharging an electric vehicle, the system comprising:

[0050] A real-time information acquisition module is used to obtain real-time information on the power grid, road network, and charging stations. The real-time road network information is obtained based on a dynamic road network model constructed using graph theory methods. The real-time power grid information includes the basic discharge incentive dispatch price, the basic charging service price, and the regional basic active load of each power grid area. The real-time charging station information includes the utilization rate of charging piles of aggregators in each power grid area.

[0051] A user demand acquisition module is configured to respond to the charge and discharge requests of each electric vehicle and acquire charge and discharge demand information corresponding to the charge and discharge requests of each electric vehicle; the charge and discharge demand information includes one of charge demand information and discharge demand information;

[0052] The dynamic path planning module is used to perform dynamic path planning based on a pre-built path optimization model according to the charging and discharging demand information of each electric vehicle and the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that matches the corresponding charging and discharging request, so as to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle.

[0053] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0055] The present invention provides a method, system, device and medium for dynamic path planning of electric vehicle charging and discharging. The method realizes a technical solution of obtaining, according to a preset collection period, real-time information of the power grid obtained based on a dynamic road network model constructed by a graph theory method, including real-time information of the road network of the basic discharge incentive scheduling electricity price of the power grid, the basic charging service electricity price of the power grid and the regional basic active load of each power grid area, and real-time information of charging stations including the utilization rate of charging piles of aggregators in each power grid area, and obtaining charging and discharging demand information corresponding to the charging and discharging request of each electric vehicle in response to the charging and discharging request of each electric vehicle. Based on the charging and discharging demand information of each electric vehicle and the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that match the corresponding charging and discharging request, dynamic path planning is performed based on a pre-constructed path optimization model to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle. Compared with the existing technology, this electric vehicle charging and discharging dynamic path planning method can fully consider the mutual influence between electric vehicles, charging stations, transportation road networks and distribution networks, as well as the dynamic characteristics of the power grid and the road network, and combine it with the electric vehicle path planning mechanism designed by the path optimization model with the optimization goal of minimizing time cost, energy consumption cost and economic cost. While realizing real-time dynamic path planning for electric vehicles, it can also effectively meet the charging and discharging needs of users, ensure the operation stability of the power grid, ensure the peak shaving and valley filling effect of the power grid and the balanced distribution of road network traffic, thereby maximizing overall benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 1 is a flow chart of a method for dynamic path planning for charging and discharging an electric vehicle according to an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the deployment and application framework of the method for dynamic path planning for charging and discharging electric vehicles according to an embodiment of the present invention;

[0058] Figure 3 2 is a schematic diagram of the structure of a dynamic path planning system for charging and discharging an electric vehicle according to an embodiment of the present invention;

[0059] Figure 4 is an internal structural diagram of a computer device according to an embodiment of the present invention;

[0060] Reference numerals: 1. Real-time information acquisition module; 2. User demand acquisition module; 3. Dynamic path planning module. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0062] The electric vehicle charging and discharging dynamic path planning method provided by the present invention can be understood as being based on the fact that the existing electric vehicle charging and discharging path planning method does not take into account the dynamic characteristics of the power grid and road network, and cannot ensure the real-time and applicability of electric vehicle charging and discharging path planning under the V2G framework. Instead, a multi-objective optimization dynamic path planning scheme based on road network and power grid coupling is proposed, which comprehensively considers user travel time, energy consumption, and economic costs, reflects the power grid load in real time based on a dynamic electricity price strategy, and utilizes real-time information of the road network, power grid, and charging stations to plan the charging and discharging path of electric vehicles. The scheme can be deployed on a cloud platform or server for charging and discharging path planning of electric vehicles, and can ensure that the power grid peak shaving and valley filling effect and the balanced distribution of road network traffic are achieved while ensuring that the charging and discharging needs of users are met, thereby maximizing overall benefits. The following embodiments will explain the electric vehicle charging and discharging dynamic path planning method of the present invention in detail.

[0063] In one embodiment, Figure 1 As shown, a method for dynamic path planning for charging and discharging of an electric vehicle is provided, comprising the following steps:

[0064] S11. Acquire real-time grid information, road network information, and charging station information based on a preset collection cycle. The preset collection cycle can be set based on actual application requirements. For example, the road network information, grid information, and charging station information can be updated at 15-minute intervals to ensure the reliability of subsequent route planning and analysis. The real-time road network information is obtained based on a dynamic road network model constructed using graph theory methods, including real-time road resistance for different sections of the traffic network during the data collection period. It should be noted that real-time road resistance can be understood as a variable used to measure the smooth flow of traffic on a section, by considering the traffic network as a circuit topology, each section as a resistor, and the driving time of vehicles on the section as the resistance value.

[0065] The dynamic road network model in this embodiment can be understood as a way of constructing a road network model based on the existing road network modeling, which usually uses the road section length as the road section weight in the traffic network topology. Since the road section length is a fixed value, it cannot reflect the dynamic change characteristics of the road section. In order to more accurately reflect the real-time changes in road network information, it is preferred to construct the traffic network topology based on graph theory technology, and dynamically update the traffic network topology information by regularly updating the road network information. Specifically, the dynamic road network model includes a road network node set, a road section set, a time period set, and a real-time road resistance set, which can be expressed as:

[0066]

[0067] Where G represents the traffic network topology; V represents the set of road network nodes, that is, the set of road network nodes (intersection nodes) in the actual traffic network; v i represents the i-th road network node in the transportation network topology; E represents the road segment set, which is used to represent the connectivity between road network nodes in the transportation network; e ij represents the connectivity between the i-th road network node and the j-th road network node in the traffic network topology. If they are connected, it is 1, otherwise it is 0. M represents the time period set, which divides a day into m time periods. t represents the t-th time period in the time period set T. W represents the real-time road resistance set, which is used to represent the flow status of each road section in the actual traffic network in time period t. ij (t) represents the real-time road resistance of the corresponding road section from the i-th road network node to the j-th road network node within the time period t.

[0068] Considering that charging and discharging guidance within urban road networks primarily focuses on user travel costs, and that road resistance can be an indicator of the travel expenses of road users, in order to better reflect the travel expenses of road users, this embodiment preferably uses travel time as the key indicator for quantifying the road resistance of urban roads for modeling and analysis. Based on the fact that signal light control in urban traffic can cause vehicle delays at intersections, this embodiment models the real-time road resistance of a road section as consisting of section impedance and node impedance, taking into account the different vehicle operating capacities and zero-flow speeds borne by roads of different grades. It should be noted that roads in urban road networks can be divided into four road grades: expressways, main roads, secondary roads, and branch roads. The zero-flow speed and capacity corresponding to each road grade are shown in Table 1:

[0069] Road grade Zero flow speed (km / h) Traffic capacity (veh / h) Expressway 80 1200 Main Road 60 900 Secondary trunk road 50 660 branch road 40 660

[0070] In this embodiment, the real-time road resistance of different road sections in the real-time road resistance set is obtained by analyzing the travel time as the road resistance factor, taking into account the characteristics of different road grades, and is expressed as:

[0071] ω ij(t) = R ij (t)+C i (t)

[0072] Where,

[0073]

[0074] Among them, ω ij (t) represents the real-time road resistance of the road section ij corresponding to the road network node i and the road network node j in the time period t; C i (t) represents the node impedance of the network node i in the period t; R ij (t) represents the impedance of the road section ij during the period t; represents the impedance of the road section ij at zero flow. Zero flow can be understood as the time a vehicle travels on the road section ij is only related to the length and speed of the road section. a1, a2, a3, and a4 represent the road parameters of the road section ij at the corresponding road grade, which are affected by the road grade and actual traffic conditions. represents the traffic saturation of road section ij in time period t; represents the free flow time of a vehicle passing through the road network node i; b1, b2, b3 and b4 represent the intersection parameters of the road network node i under the corresponding road grade, which are affected by the road grade and actual traffic conditions; represents the traffic saturation of the intersection at road network node i in time period t; exp(·) represents the exponential function; FL ij (t) represents the traffic flow of road section ij in period t; FL i (t) represents the traffic flow of road network node i in period t; C ij represents the traffic capacity of vehicles passing through road section ij; C i represents the traffic capacity of road network node i; L ij represents the length of the road section ij; represents the drivable vehicle speed on road section ij.

[0075] This embodiment uses a dynamic road network model constructed based on graph theory methods to obtain real-time road network information, which can effectively reflect the dynamic changes in traffic flow on each road section in the actual traffic network and provide reliable data support for the applicability of subsequent path planning.

[0076] In actual applications, factors such as the dynamic update of the electric vehicle charging and discharging incentive scheduling electricity price, the active load of the power grid, and the utilization rate of charging piles will directly affect the economic cost of meeting the charging and discharging needs of electric vehicle users. In order to ensure the rationality and economy of the actual electric vehicle charging and discharging path planning, this embodiment preferably periodically obtains relevant information for path planning analysis when receiving the charging and discharging requests of each electric vehicle. The specific real-time information of the power grid includes the basic discharge incentive scheduling electricity price of the power grid, the basic charging service electricity price of the power grid, and the regional basic active load of each power grid area; the real-time information of the charging station includes the utilization rate of the aggregator charging piles in each power grid area.

[0077] S12. Responding to the charge and discharge requests of each electric vehicle, obtaining charge and discharge demand information corresponding to the charge and discharge requests of each electric vehicle; the charge and discharge demand information includes either charging demand information or discharging demand information; wherein the charge and discharge demand information may include information specifically indicating the charging demand or discharging demand, the current location of the electric vehicle, the destination location of the trip, and other relevant vehicle information to facilitate subsequent route planning, which is not specifically limited here. After receiving the charge and discharge requests sent by each electric vehicle, the corresponding charge and discharge requests are parsed to obtain the corresponding charge and discharge demand information, so as to facilitate optimal route planning analysis based on the actual charge and discharge demand information of each electric vehicle, combined with real-time information of the power grid, real-time information of the road network, and real-time information of charging stations, based on the following method steps.

[0078] S13. According to the charging and discharging demand information of each electric vehicle, and the real-time information of the power grid, the real-time information of the road network, and the real-time information of the charging station that match the corresponding charging and discharging request, dynamic path planning is performed based on a pre-built path optimization model to obtain a target charging station corresponding to the charging and discharging request of each electric vehicle.

[0079] In practical applications, in order to effectively control the impact of electric vehicle charging and discharging behavior on the safe and stable operation of the actual power grid under the V2G architecture, a discharge incentive scheduling price update mechanism and a charging incentive scheduling price update mechanism are designed specifically: the grid operator formulates the discharge incentive scheduling price based on the operating characteristics of the regional power grid and the utilization rate of the charging piles at the charging station, and updates it based on the peak shaving and valley filling needs of the regional power grid. The updated discharge price is then conveyed to the electric vehicle aggregators in each region, who respond to the regulation based on the discharge incentive scheduling price; at the same time, the electric vehicle charging service fee is formulated by the charging service provider CS (Charge Service Provider) based on the operation of the regional power grid and the grid connection status of electric vehicles, and is directly transmitted to the distribution system operator DSO (Distribution System Operator) and the regional electric vehicle aggregator EVA (Electric Vehicle Aggregator) after updating. The regional EVA and electric vehicle users can respond according to the regulation plan. Based on the difference between the discharge incentive scheduling electricity price update mechanism and the charging incentive scheduling electricity price update mechanism in actual applications, in order to better meet the charging and discharging needs of electric vehicle users when planning the path of electric vehicles, this embodiment preferably identifies the charging and discharging needs of each electric vehicle, and based on different needs, adopts different path optimization models to plan the optimal path for the corresponding needs, that is, the path optimization model includes a charging path optimization model and a discharging path optimization model.

[0080] Specifically, the step of performing dynamic path planning based on a pre-built path optimization model according to the charge and discharge demand information of each electric vehicle, and the real-time information of the power grid, the real-time information of the road network, and the real-time information of the charging station that matches the corresponding charge and discharge request, to obtain the target charging station corresponding to the charge and discharge request of each electric vehicle includes:

[0081] According to the real-time road network information, the corresponding real-time road network adjacency matrix is ​​obtained; wherein the real-time road network adjacency matrix can be understood as the adjacency matrix D(t) of the dynamic road network model G, and its matrix elements can be expressed as:

[0082]

[0083] Where, d ij (t) represents the edge weight of the road segment ij in the time period t, that is, the directed edge weight from the i-th road network node to the j-th road network node; that is, the real-time road network adjacency matrix can be expressed as:

[0084]

[0085] Where D(t) represents the real-time road network adjacency matrix of time period t; ∞ represents that there is no connecting road section between two road network nodes; ω 12 (t) represents the real-time road resistance of the road section from the first road network node to the second road network node within the time period t; ω 21 (t) represents the real-time road resistance of the road section from the second road network node to the first road network node within the time period t; ω 23 (t) represents the real-time road resistance of the road section from the second road network node to the third road network node within the time period t; ω 32 (t) represents the real-time road resistance of the road section from the third road network node to the second road network node within the time period t.

[0086] According to the charging and discharging demand information of each electric vehicle, a corresponding path optimization model is obtained; that is, if the actual charging and discharging demand information of the electric vehicle is charging demand information, the corresponding path optimization model is a charging path optimization model; conversely, if the charging and discharging demand information is discharging demand information, the corresponding path optimization model is a discharging path optimization model.

[0087] According to the real-time road network adjacency matrix, the real-time power grid information, and the real-time charging station information, the optimal path is solved for the path optimization model corresponding to each electric vehicle based on the Dijkstra algorithm to obtain the target charging station of each electric vehicle; wherein, the difference between the process of solving the optimal path based on the Dijkstra algorithm to obtain the target charging station of the electric vehicle and the existing implementation process of performing path planning based on the Dijkstra algorithm is that the real-time road network adjacency matrix used in path planning is obtained by weighting multiple influencing factors and is dynamically changing. The other parts are implemented with reference to the existing algorithm logic and are not described in detail here.

[0088] In order to ensure that the ultimately optimized charging or discharging path can meet the user's charging and discharging needs while also ensuring the peak-shaving and valley-filling effect of the power grid and the balanced distribution of road network traffic, this embodiment preferably constructs a path optimization model with minimization of time cost, energy cost, and economic cost as the optimization objectives; specifically, the steps of constructing the path optimization model include:

[0089] A path time cost model is constructed based on the real-time road resistance in the dynamic road network model. The path time cost model can be understood as a design concept that directly calculates the path travel time based on the real-time road resistance of different roads, taking into account the different vehicle operating capabilities of different road grades, and the fact that the vehicle operating capabilities of different roads are reflected in the modeling of the road section real-time road resistance model. It can be expressed as:

[0090] H ij (t) = ω ij (t), L(i,j)∈L

[0091] Where L represents the road grade set, which is used to characterize the functional grade of each road. Roads of different grades have different vehicle operation capabilities. L(i,j) represents the road grade of section ij. For roads in the urban road network, they are divided into four grades. The road grades can be marked according to the principle that expressway is set as 1, main road is set as 2, secondary road is set as 3, and branch road is set as 4. ij (t) represents the real-time road resistance of the road section ij corresponding to the road network node i and the road network node j in the time period t; H ij (t) represents the path travel time of the vehicle on road section ij during time period t, that is, the path time cost.

[0092] Based on the analysis of the impact of vehicle speed and ambient temperature on vehicle energy consumption under different road grades, a path energy cost model is constructed. The path energy cost model can be understood as an expression of electric vehicle path energy consumption that simultaneously considers vehicle speed and ambient temperature. Specifically, the steps of constructing the path energy cost model based on the analysis of the impact of vehicle speed and ambient temperature on vehicle energy consumption under different road grades include:

[0093] Based on the analysis of the relationship between vehicle speed and vehicle energy consumption under different road grades, a vehicle section energy consumption factor model is obtained, and based on the vehicle section energy consumption factor model and the road section distance, a vehicle section energy consumption model is constructed. The vehicle section energy consumption factor model can be understood as a mapping relationship model between speed and energy consumption per unit distance established by simulating and analyzing electric vehicles under different road grades based on the ECF (Economic Commission for Europe) cycle test method, which can be expressed as:

[0094]

[0095] Where V t is the average speed of electric vehicles in time period t, in km / h; ECF(t) is the speed V of electric vehicles in time period t t The corresponding energy consumption factor (energy consumption per unit distance) is expressed in J / km; z1, z2, z3, and z4 are regression coefficients. The energy consumption factor model for automobile sections of different road grades is shown in Table 2:

[0096] Table 2 Energy consumption factor model of automobile sections of different road grades

[0097]

[0098] After obtaining the energy consumption factor model of the automobile road section through the above method steps, the corresponding automobile road section energy consumption model can be expressed as:

[0099] QL,ij (t) = ECF ij (t)·L ij

[0100] Where Q L,ij (t) represents the energy consumption required for the vehicle to travel the road section ij within the time period t; L ij Indicates the distance between road segments ij; ECF ij (t) represents the energy consumption factor of the vehicle traveling through the road section ij within the time period t.

[0101] Based on the analysis of the relationship between ambient temperature and vehicle energy consumption, the vehicle temperature control energy consumption model is obtained. Among them, the vehicle temperature control energy consumption model can be understood as taking into account the fact that the impact of ambient temperature on electric vehicle energy consumption is mainly reflected in the characteristics of air conditioning use. The established relationship model between ambient temperature and electric vehicle energy consumption can be expressed as:

[0102]

[0103] Where Q T,ij (t) represents the vehicle speed V during the period t t and the energy consumption of automobile temperature control after the vehicle has traveled through the road section ij under the ambient temperature T; P L and P R Respectively represent the cooling power and heating power of the car air conditioner; T max and T min They represent the cooling temperature threshold and heating temperature threshold of the car air conditioner respectively, which can be set according to actual application requirements and are not specifically limited here.

[0104] According to the vehicle road section energy consumption model and the vehicle temperature control energy consumption model, a path energy consumption cost model is obtained; the path energy consumption cost model is expressed as:

[0105] Q ij (t) = Q L,ij (t)+Q T,ij (t)

[0106] Among them, Q ij (t) represents the total path energy consumption required for the vehicle to complete the road section ij within the time period t.

[0107] The path energy cost model constructed in this embodiment based on the analysis of the relationship between vehicle speed and ambient temperature on vehicle energy consumption under different road grades can comprehensively and reliably evaluate the driving energy consumption of electric vehicles on road sections of different road grades, thereby providing reliable guarantees for the accuracy of subsequent path optimization considering driving energy consumption.

[0108] A discharge economic benefit model is constructed based on the basic discharge incentive dispatching electricity price of the power grid, the regional discharge incentive dispatching electricity price adjustment coefficient, and the battery discharge degradation loss cost. The discharge economic benefit model can be understood as a mathematical model that evaluates the discharge economic benefit of electric vehicles by simultaneously considering the discharge incentive dispatching electricity price update mechanism and the factor that the discharge behavior of electric vehicles will cause irreversible damage to the battery. Specifically, the steps of constructing the discharge economic benefit model based on the basic discharge incentive dispatching electricity price of the power grid, the regional discharge incentive dispatching electricity price adjustment coefficient, and the battery discharge degradation loss cost include:

[0109] According to the relationship between the regional basic active load and the average value of the regional basic active load, the power grid peak shaving coefficient is obtained; wherein the power grid peak shaving coefficient is expressed as:

[0110]

[0111] Where,

[0112]

[0113] in, represents the regional base active load of region q in time period t; represents the average base active load of region q in time period t; exp(·) represents the exponential function; It represents the peak-shaving coefficient of the power grid in region q during time period t. When there is a peak-shaving demand in the regional power grid, the coefficient is positive. When the regional basic active load of the power grid is greater than or equal to the average regional basic active load, it is a positive discharge incentive, otherwise it is 0. m represents the total number of time periods.

[0114] Based on the utilization rate of the charging piles of the aggregator, the discharge incentive coefficient of the electric vehicle aggregator is obtained; wherein the discharge incentive coefficient of the electric vehicle aggregator is expressed as:

[0115]

[0116] in, N represents the discharge incentive coefficient of electric vehicle aggregators in region q during period t; q,t N represents the number of electric vehicles connected to the charging station in area q during time period t; q represents the total number of charging piles in the charging station in area q during time period t; β1 represents the constant coefficient, which can be set according to actual application requirements. In actual applications, based on N q,t With N q Division is used to represent the utilization rate of charging piles. In order to fully stimulate the vehicle-electric interconnection potential of electric vehicles, more incentive subsidies should be given if the utilization rate is higher, that is, the larger the corresponding parameter β1 is.

[0117] The regional discharge incentive dispatch electricity price adjustment coefficient is obtained according to the product of the preset regional discharge incentive dispatch electricity price adjustment coefficient, the grid peak shaving coefficient, and the electric vehicle aggregator discharge incentive coefficient; wherein the regional discharge incentive dispatch electricity price adjustment coefficient is expressed as:

[0118]

[0119] Where, represents the regional discharge incentive dispatch price adjustment coefficient of region q during period t; Indicates the preset regional discharge incentive dispatch electricity price adjustment coefficient of region q, which can be set according to actual application requirements; and They represent the grid peak shaving coefficient and electric vehicle aggregator discharge incentive coefficient of region q during period t, respectively.

[0120] The discharge incentive dispatching electricity price is obtained by multiplying the basic discharge incentive dispatching electricity price of the power grid and the adjustment coefficient of the regional discharge incentive dispatching electricity price. That is, the discharge incentive dispatching electricity price can be expressed as:

[0121]

[0122] Where, represents the discharge incentive dispatch electricity price in area q during period t; represents the regional discharge incentive dispatch price adjustment coefficient of region q during period t; It represents the basic discharge incentive dispatch price of the power grid.

[0123] A comprehensive analysis is performed on the discharge incentive dispatch electricity price, the grid basic discharge incentive dispatch electricity price, and the battery discharge degradation loss cost to construct the discharge economic benefit model; wherein the battery discharge degradation loss cost can be calculated based on the battery purchase cost and the actual battery cycle life, and is expressed as:

[0124]

[0125] Where y loss Represents the battery discharge degradation loss cost. The discharge loss here will be compensated by the corresponding charging station, and can also be converted into economic benefits and included in the comprehensive discharge benefits; N0 represents the actual cycle life of the battery; y bat Indicates the battery purchase cost.

[0126] The discharge economic benefit model in this embodiment can be understood as a mathematical expression for calculating the comprehensive discharge benefit based on the discharge incentive dispatch electricity price, the basic discharge incentive dispatch electricity price, and the battery discharge degradation loss cost, which can be expressed as:

[0127]

[0128] Where, It represents the economic benefit of discharge in region q during period t.

[0129] A charging economic cost model is constructed based on the grid-based charging service electricity price and the regional charging service fee adjustment coefficient. The charging economic cost model can be understood as a mathematical model for evaluating the economic cost of electric vehicle charging based on a charging incentive scheduling electricity price update mechanism. Specifically, the steps of constructing the charging economic cost model based on the grid-based charging service electricity price and the regional charging service fee adjustment coefficient include:

[0130] According to the regional basic active load and the allowable fluctuation range of the regional basic active load, the peak-shaving and valley-filling coefficient of the power grid is obtained; wherein the peak-shaving and valley-filling coefficient of the power grid is expressed as:

[0131]

[0132] Where, represents the regional base active load of region q during period t; and They represent the maximum and minimum values ​​of the base active load in the allowable fluctuation range of the base active load of area q respectively; It represents the peak shaving and valley filling coefficient of the power grid in area q during time period t.

[0133] Based on the utilization rate of the charging piles of the aggregator, the charging incentive coefficient of the electric vehicle aggregator is obtained; wherein the charging incentive coefficient of the electric vehicle aggregator is expressed as:

[0134]

[0135] in, N represents the charging incentive coefficient of electric vehicle aggregators in region q during period t; q,t N represents the number of electric vehicles connected to the charging station in area q during time period t; q represents the total number of charging piles in the charging station in area q during time period t; β2 represents the constant coefficient, which can be set according to actual application requirements.

[0136] The regional charging service fee adjustment coefficient is obtained according to the product of the preset regional charging service fee adjustment coefficient, the grid peak shaving and valley filling coefficient, and the electric vehicle aggregator charging incentive coefficient; wherein the regional charging service fee adjustment coefficient is expressed as:

[0137]

[0138] Where, represents the regional charging service fee adjustment coefficient for region q during period t; Indicates the preset regional charging service fee adjustment coefficient for region q, which can be set according to actual application needs; and They represent the peak-shaving and valley-filling coefficient of the power grid and the charging incentive coefficient of the electric vehicle aggregator in region q during period t.

[0139] The charging incentive dispatching electricity price is obtained by multiplying the basic charging service electricity price of the power grid and the regional charging service fee adjustment coefficient; wherein the charging incentive dispatching electricity price is expressed as:

[0140]

[0141] Where, represents the charging incentive dispatch electricity price in area q during period t; represents the regional charging service fee adjustment coefficient for region q during period t; Indicates the electricity price of grid-based charging services.

[0142] A comprehensive analysis is performed on the charging incentive dispatch electricity price and the grid basic charging service electricity price to construct the charging economic cost model; wherein the charging economic cost model is expressed as:

[0143]

[0144] Where, represents the economic cost of charging in area q during period t.

[0145] A discharge path optimization model is constructed by weighted synthesis based on the path time cost model, the path energy consumption cost model, and the discharge economic benefit model. The discharge path optimization model is expressed as:

[0146]

[0147] Where, H represents the comprehensive discharge cost of the xth road segment in the t period, and the xth road segment is the road segment from the x1th road network node to the x2th road network node; x (t) represents the time cost of the xth road segment in time period t, which is obtained based on the aforementioned path time cost model; Q x (t) represents the total path energy consumption of the x-th segment in time period t, which is obtained based on the aforementioned path energy cost model; represents the discharge economic benefit of the qth region in period t, which is obtained based on the aforementioned discharge economic benefit model; H x,e (t) represents the minimum time cost of the xth road segment when only the time cost optimization is considered, which is the minimum time cost of this road segment during the period obtained according to the database statistics; Q x,e(t) represents the minimum total path energy consumption of the x-th road segment when only the total path energy consumption is optimized, which is the minimum energy consumption cost of this road segment during this period obtained according to the database statistics; It represents the minimum economic benefit of discharge when only economic cost optimization is considered, which is the minimum economic benefit of discharge in this period obtained according to database statistics; u1, u2, u3 and u4 represent weight coefficients.

[0148] A charging path optimization model is constructed by performing weighted synthesis based on the path time cost model, the path energy consumption cost model, and the charging economic cost model. The charging path optimization model is expressed as:

[0149]

[0150] Where, H represents the comprehensive charging cost of the yth road section in the t period, and the yth road section is the road section from the y1th road network node to the y2th road network node; y (t) represents the time cost of the yth road segment in time period t, which is obtained based on the aforementioned path time cost model; Q y (t) represents the total path energy consumption of the yth road segment in time period t, which is obtained based on the aforementioned path energy consumption cost model; represents the economic cost of charging to the qth region within time period t, which is obtained based on the aforementioned charging economic cost model; H y,e (t) represents the minimum time cost of the yth road segment when only the time cost optimization is considered, which is the minimum time cost of this road segment during the period obtained according to the database statistics; Q y,e (t) represents the minimum total path energy consumption of the yth segment when only the total path energy consumption is optimized, which is the minimum energy consumption cost of this segment during the period obtained according to the database statistics; It represents the minimum charging cost when only economic cost optimization is considered, which is the minimum charging economic cost for this period obtained based on database statistics; w1, w2, w3 and w4 represent weight coefficients.

[0151] It should be noted that for multi-objective optimization problems, the determination of appropriate weight coefficients is the key to solving the problem. In order to ensure the rationality of the charging path and discharge path planning, this embodiment preferably sets the weight coefficients in the above-mentioned discharge path optimization model and the charging path optimization model based on specific coefficient rules: Taking into account that time cost and energy consumption cost are mainly related to road network information such as real-time traffic flow and congestion conditions, the distribution of road vehicles can be balanced by adjusting the weights of these two aspects; at the same time, taking into account that the economic cost is affected by the load conditions of the power grid and the utilization rate of the charging piles at the charging station, adjusting the correlation coefficient corresponding to the economic cost can better achieve peak shaving and valley filling. Based on this, the weight coefficients in the above-mentioned discharge path optimization model and the charging path optimization model can be appropriately and flexibly adjusted according to the road network and power grid pressure conditions in different time periods, as shown in Table 3:

[0152] Table 3 Adjustment table of weight coefficients in discharge path optimization model and charging path optimization model

[0153]

[0154] According to the discharge path optimization model and the charging path optimization model, the path optimization model is obtained based on preset path optimization constraints; the preset path optimization constraints include dynamic electricity price constraints, discharge depth constraints, and charging condition constraints, which are specifically expressed as follows:

[0155] 1) Dynamic electricity price constraints include discharge electricity price constraints and charging electricity price constraints. The discharge electricity price constraint can be expressed as:

[0156]

[0157] Where, represents the discharge incentive dispatch electricity price in area q during period t; and They represent the minimum and maximum values ​​of the discharge incentive dispatch electricity price in area q respectively;

[0158] The charging price constraint can be expressed as:

[0159]

[0160] Where, represents the charging incentive dispatch electricity price in area q during period t; and They represent the minimum and maximum values ​​of the charging service electricity price in area q respectively.

[0161] 2) The depth of discharge constraint can be understood as protecting the battery and reducing losses. The depth of discharge must ensure that the state of charge at the end of the discharge is not less than 20% of the vehicle's full charge, expressed as:

[0162] SOCend >20%E0

[0163] Where, SOC end Indicates the state of charge of the battery after the electric vehicle is discharged; E0 indicates the total charge of the battery of the electric vehicle;

[0164] 3) The charging condition constraint can be understood as ensuring that the electric vehicle can reach the optimal charging station before the battery is exhausted, which can be expressed as:

[0165] Q total ≤SOC r

[0166] Where Q total Indicates the total energy consumption of the vehicle arriving at the charging station; SOC r Indicates the state of charge of the electric vehicle when it issues a charging request.

[0167] It should be noted that, in practical applications, the electric vehicle charging and discharging dynamic path planning method provided by the present invention can be deployed on a cloud platform, such as Figure 2 As shown in FIG, the cloud platform can regularly update road network information (real-time road resistance information and traffic flow of different sections in the traffic network), power grid information (power grid basic discharge incentive dispatch electricity price, power grid basic charging service electricity price and regional basic active load of each power grid area), and charging station information (utilization rate of charging piles of aggregators in each power grid area) to ensure that charging and discharging path planning decisions are based on the latest data. After the cloud platform receives the charging and discharging request of each electric vehicle, it can use the Dijkstra algorithm to optimize the corresponding charging path based on the above-constructed path optimization model according to the acquired charging and discharging demand information, combined with the real-time power grid information, road network information and charging station information corresponding to the charging and discharging request. After obtaining the target charging station based on the path planning, it can determine whether the target charging station is within the driving range based on the current electric vehicle position and destination location in the charging and discharging demand information. If not, it needs to be excluded and replanned until the target charging station within the driving range of the electric vehicle is obtained as the final target charging station (optimal charging station). The above embodiment only illustrates the charging and discharging path planning of one electric vehicle. However, in actual applications, the cloud platform may execute charging and discharging path planning for multiple electric vehicles at the same time. At this time, the corresponding optimal path planning can be performed in sequence according to the order of receiving the charging and discharging requests of the electric vehicles based on the above path planning method until the charging and discharging path planning scheduling of all electric vehicles is completed.

[0168] The embodiments of the present invention provide a method for acquiring, according to a preset collection cycle, real-time grid information obtained based on a dynamic road network model constructed using graph theory methods, including grid basic discharge incentive dispatch electricity prices, grid basic charging service electricity prices, and real-time road network information of regional basic active loads in each grid area, and real-time charging station information including the utilization rate of charging piles of aggregators in each grid area. In response to charging and discharging requests from each electric vehicle, charging and discharging demand information corresponding to the charging and discharging request of each electric vehicle is acquired. Based on the charging and discharging demand information of each electric vehicle and the real-time grid information, road network information, and charging station information that match the corresponding charging and discharging request, dynamic route planning is performed based on a pre-constructed route optimization model to obtain a target charging station solution corresponding to the charging and discharging request of each electric vehicle. This method fully considers the mutual influence between electric vehicles, charging stations, transportation road networks, and distribution networks. Based on the design of an electric vehicle route planning mechanism that considers the dynamic characteristics of the power grid and road networks, this method implements real-time dynamic route planning that is applicable to electric vehicles while effectively meeting user charging and discharging needs, ensuring the operational stability of the power grid, the peak-shaving effect of the power grid, and the balanced distribution of road network traffic, thereby maximizing overall benefits.

[0169] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0170] In one embodiment, Figure 3 As shown, a dynamic path planning system for charging and discharging of an electric vehicle is provided, the system comprising:

[0171] Real-time information acquisition module 1 is used to obtain real-time information on the power grid, road network, and charging stations. The real-time road network information is obtained based on a dynamic road network model constructed using graph theory. The real-time power grid information includes the basic discharge incentive dispatch price, the basic charging service price, and the regional basic active load of each power grid area. The real-time charging station information includes the utilization rate of charging piles of aggregators in each power grid area.

[0172] A user demand acquisition module 2 is configured to respond to the charge and discharge requests of each electric vehicle and acquire charge and discharge demand information corresponding to the charge and discharge requests of each electric vehicle; the charge and discharge demand information includes one of charge demand information and discharge demand information;

[0173] The dynamic path planning module 3 is used to perform dynamic path planning based on a pre-built path optimization model according to the charging and discharging demand information of each electric vehicle, as well as the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that match the corresponding charging and discharging request, to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle.

[0174] For the specific definition of the electric vehicle charging and discharging dynamic path planning system, please refer to the definition of the electric vehicle charging and discharging dynamic path planning method above. The corresponding technical effects can also be obtained equivalently, so they will not be repeated here. The various modules in the above-mentioned electric vehicle charging and discharging dynamic path planning system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0175] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in one embodiment, which may be a terminal or a server. Figure 4 As shown, the computer device includes a processor, memory, network interface, display, camera and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a dynamic path planning method for charging and discharging an electric vehicle can be implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0176] It can be understood by those skilled in the art that Figure 4 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0177] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0178] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0179] In summary, the embodiments of the present invention provide a method, system, device and medium for dynamic path planning for electric vehicle charging and discharging. The method for dynamic path planning for electric vehicle charging and discharging fully considers the mutual influence between electric vehicles, charging stations, transportation road networks and distribution networks, as well as the dynamic characteristics of the power grid and the road network, and combines an electric vehicle path planning mechanism with a path optimization model designed with minimizing time cost, energy consumption cost and economic cost as the optimization goal. While realizing real-time and applicable dynamic path planning for electric vehicles, it can also effectively meet the charging and discharging needs of users, ensure the operational stability of the power grid, ensure the peak shaving and valley filling effect of the power grid and the balanced distribution of road network traffic, thereby maximizing overall benefits.

[0180] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-described embodiments merely represent several preferred implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and such improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the scope of protection of the claims.

Claims

1. A method for dynamic path planning for charging and discharging of electric vehicles, characterized in that: The method comprises the following steps: Acquire real-time grid information, road network information, and charging station information based on a preset collection cycle; the road network information is derived from a dynamic road network model constructed using graph theory; the grid information includes the grid's basic discharge incentive dispatch price, the grid's basic charging service price, and the regional basic active load for each grid region; the charging station information includes the utilization rate of aggregator charging piles in each grid region; In response to the charge and discharge requests of the respective electric vehicles, acquiring charge and discharge demand information corresponding to the charge and discharge requests of the respective electric vehicles; the charge and discharge demand information including one of charge demand information and discharge demand information; According to the charging and discharging demand information of each electric vehicle, and the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that match the corresponding charging and discharging request, dynamic path planning is performed based on a pre-built path optimization model to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle.

2. The electric vehicle charging and discharging dynamic path planning method according to claim 1, characterized in that: The real-time road network information includes the real-time road resistance of different road sections in the corresponding time period; the dynamic road network model includes a road network node set, a road section set, a time period set, and a real-time road resistance set; the real-time road resistance of different road sections in the real-time road resistance set is obtained by analyzing the travel time as the road resistance factor, and is expressed as: ω ij (t)=R ij (t)+C i (t) Where, Among them, ω ij (t) represents the real-time road resistance of the road section ij corresponding to the road network node i and the road network node j in the time period t; C i (t) represents the node impedance of the network node i in the period t; R ij (t) represents the impedance of the road section ij during the period t; represents the impedance of the road section ij at zero flow; a1, a2, a3 and a4 represent the road section parameters of the road section ij at the corresponding road level; represents the traffic saturation of road section ij in time period t; represents the free flow time of a vehicle passing through the road network node i; b1, b2, b3 and b4 represent the intersection parameters of the road network node i under the corresponding road level; represents the traffic saturation of the intersection at road network node i in time period t; exp(·) represents the exponential function.

3. The electric vehicle charging and discharging dynamic path planning method according to claim 1, characterized in that: The path optimization model includes a charging path optimization model and a discharging path optimization model; the steps of performing dynamic path planning based on the pre-built path optimization model according to the charging and discharging demand information of each electric vehicle and the real-time information of the power grid, the real-time information of the road network, and the real-time information of the charging station that matches the corresponding charging and discharging request, to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle include: According to the real-time road network information, obtaining a corresponding real-time road network adjacency matrix; According to the charging and discharging demand information of each electric vehicle, a corresponding path optimization model is obtained; According to the real-time road network adjacency matrix, the real-time power grid information and the real-time charging station information, the optimal path of the path optimization model corresponding to each electric vehicle is solved based on the Dijkstra algorithm to obtain the target charging station of each electric vehicle.

4. The electric vehicle charging and discharging dynamic path planning method according to claim 1, characterized in that: The path optimization model is constructed with the optimization goal of minimizing time cost, energy consumption cost and economic cost; the steps of constructing the path optimization model include: Constructing a path time cost model based on the real-time road resistance in the dynamic road network model; Based on the analysis of the impact of vehicle speed and ambient temperature on vehicle energy consumption under different road grades, a path energy cost model is constructed; Constructing a discharge economic benefit model based on the basic discharge incentive dispatch electricity price of the power grid, the regional discharge incentive dispatch electricity price adjustment coefficient, and the battery discharge degradation loss cost; Constructing a charging economic cost model based on the basic charging service electricity price of the power grid and the regional charging service fee adjustment coefficient; Perform weighted synthesis based on the path time cost model, the path energy consumption cost model, and the discharge economic benefit model to construct a discharge path optimization model; Constructing a charging path optimization model by weighted synthesis based on the path time cost model, the path energy consumption cost model, and the charging economic cost model; According to the discharge path optimization model and the charging path optimization model, the path optimization model is obtained based on preset path optimization constraints; the preset path optimization constraints include dynamic electricity price constraints, discharge depth constraints and charging condition constraints.

5. The electric vehicle charging and discharging dynamic path planning method according to claim 4, characterized in that: The steps of constructing a path energy cost model based on the analysis of the influence of vehicle speed and ambient temperature on vehicle energy consumption under different road grades include: Based on the relationship analysis between vehicle speed and vehicle energy consumption under different road grades, an automobile road section energy consumption factor model is obtained, and according to the automobile road section energy consumption factor model and the road section distance, an automobile road section energy consumption model is constructed; Based on the relationship analysis between ambient temperature and vehicle energy consumption, the vehicle temperature control energy consumption model is obtained; According to the vehicle road section energy consumption model and the vehicle temperature control energy consumption model, a path energy consumption cost model is obtained; the path energy consumption cost model is expressed as: Q ij (t)=Q L,ij (t)+Q T,ij (t) Where, Q L,ij (t)=ECF ij (t)·L ij Among them, Q ij (t) represents the total path energy consumption required for the vehicle to complete the road section ij within the time period t; Q L,ij (t) represents the energy consumption required for the vehicle to travel the road section ij within the time period t; L ij Indicates the distance between road segments ij; ECF ij (t) represents the energy consumption factor of the vehicle traveling through the road section ij within the time period t; Q T,ij (t) represents the vehicle speed V during the period t t and the energy consumption of automobile temperature control after the vehicle has traveled through the road section ij under the ambient temperature T; P L and P R Respectively represent the cooling power and heating power of the car air conditioner; T max and T min They represent the cooling temperature threshold and heating temperature threshold of the car air conditioner respectively.

6. The electric vehicle charging and discharging dynamic path planning method according to claim 4, characterized in that: The step of constructing a discharge economic benefit model based on the grid basic discharge incentive dispatching electricity price, the regional discharge incentive dispatching electricity price adjustment coefficient, and the battery discharge degradation loss cost includes: Obtaining a power grid peak shaving coefficient according to a magnitude relationship between the regional basic active load and an average value of the regional basic active load; Based on the utilization rate of the charging piles of the aggregator, a discharge incentive coefficient of the electric vehicle aggregator is obtained; Obtaining the regional discharge incentive dispatch electricity price adjustment coefficient according to the product of the preset regional discharge incentive dispatch electricity price adjustment coefficient, the grid peak shaving coefficient, and the electric vehicle aggregator discharge incentive coefficient; Obtaining a discharge incentive dispatching electricity price according to the product of the basic discharge incentive dispatching electricity price of the power grid and the adjustment coefficient of the regional discharge incentive dispatching electricity price; A comprehensive analysis is conducted on the discharge incentive dispatch electricity price, the grid basic discharge incentive dispatch electricity price and the battery discharge degradation loss cost to construct the discharge economic benefit model.

7. The electric vehicle charging and discharging dynamic path planning method according to claim 4, characterized in that: The step of constructing a charging economic cost model based on the grid basic charging service electricity price and the regional charging service fee adjustment coefficient includes: Obtaining a peak-shaving and valley-filling coefficient of the power grid according to the regional basic active load and the allowable fluctuation range of the regional basic active load; Based on the utilization rate of the charging piles of the aggregator, a charging incentive coefficient for the electric vehicle aggregator is obtained; Obtaining the regional charging service fee adjustment coefficient according to the product of the preset regional charging service fee adjustment coefficient, the grid peak shaving and valley filling coefficient, and the electric vehicle aggregator charging incentive coefficient; Obtaining a charging incentive dispatching electricity price based on the product of the grid basic charging service electricity price and the regional charging service fee adjustment coefficient; A comprehensive analysis is conducted on the charging incentive dispatch electricity price and the grid basic charging service electricity price to construct the charging economic cost model.

8. A dynamic path planning system for charging and discharging electric vehicles, characterized in that: The system comprises: A real-time information acquisition module is configured to acquire real-time grid information, road network information, and charging station information based on a preset acquisition cycle. The road network information is derived from a dynamic road network model constructed using graph theory. The grid information includes the grid's basic discharge incentive dispatch price, the grid's basic charging service price, and the regional basic active load for each grid region. The charging station information includes the utilization rate of aggregator charging piles in each grid region. A user demand acquisition module is configured to respond to the charge and discharge requests of each electric vehicle and acquire charge and discharge demand information corresponding to the charge and discharge requests of each electric vehicle; the charge and discharge demand information includes one of charge demand information and discharge demand information; The dynamic path planning module is used to perform dynamic path planning based on a pre-built path optimization model according to the charging and discharging demand information of each electric vehicle and the real-time information of the power grid, the real-time information of the road network and the real-time information of the charging station that matches the corresponding charging and discharging request, so as to obtain the target charging station corresponding to the charging and discharging request of each electric vehicle.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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