Electric vehicle traffic-electric power collaborative optimization method, device, equipment and medium

Abstract

The invention relates to the technical field of traffic-power optimization scheduling. The invention discloses an electric vehicle traffic-power collaborative optimization method, device and equipment and a medium. The method comprises the following steps: constructing a battery capacity and energy consumption model of an electric vehicle along with temperature change; constructing a road traffic capacity and driving speed correction model changing along with the weather state; constructing a charging efficiency model of the electric vehicle battery along with temperature change; according to the battery capacity and energy consumption model, the road traffic capacity and driving speed correction model and the charging efficiency model, a traffic-electric power double-layer optimization model is constructed, and the traffic-electric power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct-current optimal power flow model; and based on a preset solving model, solving the traffic-power double-layer optimization model to obtain electric vehicle traffic flow space-time distribution. Mutual feedback adjustment of traffic and a power grid can be realized in a space-time dynamic framework, estimation deviation is reduced, and system behaviors in extreme weather in winter are effectively reflected.

Description

Technical Field

[0001] This application relates to the field of traffic-power optimization scheduling technology, and more specifically, to a method, apparatus, equipment, and medium for coordinated optimization of traffic and power for electric vehicles. Background Technology

[0002] With the increasing prevalence of electric vehicles (EVs) in cold regions, vehicle travel and power system operation face new challenges under low-temperature conditions. Existing traffic-power coupling optimization models are mostly based on ideal climate assumptions, adjusting only energy consumption parameters or battery capacity ratios within the model. This leads to significant estimation errors in EV travel patterns, charging load, and distribution network safety margins under cold climates. Furthermore, existing traffic-power coupling optimization models often employ static traffic assignment or simplified charging logic, making it difficult to reflect system behavior under extreme winter weather conditions.

[0003] Therefore, there is a need to find a method for coordinated optimization of electric vehicle transportation and power that can reduce estimation bias and effectively reflect the system behavior under extreme winter weather conditions. Summary of the Invention

[0004] In view of the above situation, this application provides a method, apparatus, device and medium for electric vehicle traffic-electricity coordinated optimization, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, this application provides a method for coordinated optimization of electric vehicle transportation and power, including: Construct a model of battery capacity and energy consumption of electric vehicles as temperature changes; Construct a road capacity and driving speed correction model that adapts to changes in weather conditions; Construct a charging efficiency model for electric vehicle batteries as a function of temperature; Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. Based on the pre-set solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow.

[0006] For example, constructing a model of battery capacity and energy consumption of an electric vehicle as a function of temperature includes: Construct the capacity retention factor of electric vehicle batteries as a function of temperature; The battery capacity model is determined based on the capacity retention factor and the nominal battery capacity. Construct the driving efficiency factor, cabin heating power, and battery thermal management power consumption of electric vehicles as they change with temperature; The energy consumption model of the electric vehicle is determined based on the driving efficiency factor, the cabin heating power, and the battery thermal management power consumption.

[0007] For example, constructing a road capacity and driving speed correction model that varies with weather conditions includes: Construct a road capacity reduction coefficient and speed maintenance factor that vary with weather conditions; The road capacity correction model is determined based on the road capacity reduction coefficient. The driving speed correction model is determined based on the speed maintenance factor.

[0008] For example, constructing a charging efficiency model for electric vehicle batteries as a function of temperature includes: Construct a charging efficiency factor and battery preheating power that vary with temperature; The actual effective charging efficiency is determined based on the charging efficiency factor and the nominal charging efficiency. The charging efficiency model is determined based on the actual effective charging efficiency and the battery preheating power.

[0009] For example, based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. This traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow optimization model for the distribution network, comprising: A dynamic road model is constructed based on the road capacity and driving speed correction model. A dynamic charging station model is constructed based on the charging efficiency model. Based on the battery capacity and energy consumption model, construct the energy constraints for electric vehicles; Construct constraints for driving demand allocation; The upper-level dynamic traffic assignment model is constructed based on the dynamic road model and the charging station model. The upper-level dynamic traffic assignment model aims to minimize the travel cost of all electric vehicles. The constraints of the upper-level dynamic traffic assignment model include the energy constraints of electric vehicles and the travel demand allocation constraints.

[0010] For example, the DC optimal power flow model of the lower-level distribution network takes minimizing the power generation cost of the distribution network as the optimization objective; The constraints of the DC optimal power flow model of the lower-level distribution network include power balance constraints, phase angle constraints, power flow constraints, and power generation output constraints.

[0011] For example, based on a pre-set solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow, including: The KKT conditions are used to transform the DC optimal power flow model of the lower-level distribution network into a set of constraints, and the traffic-power two-level optimization model is transformed into a single-level optimization model. The single-layer optimization model is solved based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow and power distribution network.

[0012] Secondly, this application provides an electric vehicle traffic-electricity cooperative optimization device, comprising: The first building block is used to build a model of the battery capacity and energy consumption of electric vehicles as temperature changes. The second building module is used to build a road capacity and driving speed correction model that changes with weather conditions; The third building module is used to build a charging efficiency model of electric vehicle batteries as temperature changes; The optimization module is used to construct a traffic-power dual-layer optimization model based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. The solution module is used to solve the traffic-electricity two-layer optimization model based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow.

[0013] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electric vehicle traffic-electricity coordinated optimization method as described in the first aspect.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the electric vehicle traffic-electricity coordinated optimization method as described in the first aspect.

[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application uses a battery capacity and energy consumption model to characterize the impact of cold climate on battery capacity and energy consumption, a road capacity and speed correction model to characterize the impact of cold climate on road capacity, and a charging efficiency model to characterize the impact of cold climate on charging efficiency. By integrating the battery capacity and energy consumption model, the road capacity and speed correction model, and the charging efficiency model into a traffic-power two-level optimization model, the accuracy and safety of system operation under cold climate conditions are improved. This avoids the risks of load underestimation and power flow exceeding limits caused by ideal climate assumptions, and realizes dynamic two-level collaborative optimization of electric vehicles and the power system. It can achieve mutual feedback regulation between traffic and the power grid within a spatiotemporal dynamic framework, reduce estimation bias, and effectively reflect system behavior under extreme winter weather conditions. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of an application environment for the electric vehicle traffic-electricity coordinated optimization method in one embodiment of the present invention; Figure 2 This is a flowchart illustrating an electric vehicle traffic-electricity coordinated optimization method according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of a specific implementation method of the electric vehicle traffic-electricity coordinated optimization method in one embodiment of the present invention; Figure 4 This is a schematic diagram of the traffic network topology in one embodiment of the present invention; Figure 5 This is a schematic diagram of a power network topology in one embodiment of the present invention; Figure 6 This is a schematic diagram comparing the optimization results of different optimization models in one embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electric vehicle traffic-electricity coordinated optimization device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 9 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] As mentioned earlier, existing traffic-electricity coupled optimization models often employ static traffic assignment or simplified charging logic, resulting in significant estimation biases and difficulty in reflecting system behavior under extreme winter weather conditions. To address this technical problem, embodiments of this application provide a traffic-electricity coordinated optimization method for electric vehicles.

[0021] The electric vehicle traffic-electricity coordinated optimization method provided in this invention can be applied to, for example... Figure 1In this application environment, the device communicates with the server via a network. The server can use the device to construct models of electric vehicle battery capacity and energy consumption as temperature changes; models of road capacity and driving speed correction as weather conditions change; and models of electric vehicle battery charging efficiency as temperature changes. Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. This traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC optimal power flow model for the distribution network. Based on a pre-set solution model, the traffic-power dual-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow. This application uses a battery capacity and energy consumption model to characterize the impact of cold climate on battery capacity and energy consumption, a road capacity and speed correction model to characterize the impact of cold climate on road capacity, and a charging efficiency model to characterize the impact of cold climate on charging efficiency. By integrating the battery capacity and energy consumption model, the road capacity and speed correction model, and the charging efficiency model into a traffic-power two-level optimization model, the accuracy and safety of system operation under cold climate conditions are improved. This avoids the risks of load underestimation and power flow exceeding limits caused by ideal climate assumptions, and realizes dynamic two-level collaborative optimization of electric vehicles and the power system. It can achieve mutual feedback regulation between traffic and the power grid within a spatiotemporal dynamic framework, reduce estimation bias, and effectively reflect system behavior under extreme winter weather conditions.

[0022] The device side can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server side can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0023] Please see Figure 2 As shown, Figure 2 A flowchart illustrating the electric vehicle traffic-electricity cooperative optimization method provided in this embodiment of the invention includes the following steps: S10: Construct a model of battery capacity and energy consumption of electric vehicles as temperature changes.

[0024] In one embodiment, battery performance degrades and vehicle energy consumption increases under low-temperature conditions, which can be more accurately characterized by a battery capacity and energy consumption model.

[0025] In one embodiment, step S10, constructing a model of the battery capacity and energy consumption of an electric vehicle as a function of temperature, includes: S11: Construct the capacity retention factor of electric vehicle batteries as temperature changes.

[0026] In one embodiment, capacity retention factor Temperature-dependent. The capacity retention factor for different temperature ranges is described by a piecewise linear function, as shown in the following equation:

[0027] in, The ambient temperature.

[0028] S12: Determine the battery capacity model based on the capacity retention factor and the nominal battery capacity.

[0029] In one embodiment, the battery capacity model uses the nominal battery capacity. With capacity retention factor The product of these factors determines the battery capacity under different ambient temperatures. As shown in the following formula:

[0030] in, This refers to the nominal battery capacity at 25°C.

[0031] S13: Construct the driving efficiency factor, cabin heating power, and battery thermal management power consumption of electric vehicles as they change with temperature.

[0032] In one embodiment, a temperature-dependent electric vehicle energy consumption model needs to consider increased rolling resistance, increased air density, cabin heating, and battery thermal management energy consumption caused by low temperatures. Driving efficiency factor The vehicle's dynamic performance is affected by low temperatures, including rolling resistance, tire stiffness, air resistance, regenerative braking efficiency, and transmission system losses. (Car compartment heating power) Heat loss through windows, doors, and body panels increases with increasing temperature difference. Battery thermal management consumes power. The power consumed to maintain the electric vehicle battery pack at a suitable temperature to ensure performance and battery life.

[0033] In one embodiment, a piecewise linear function is used to describe the driving efficiency factor across different temperature ranges. Carriage heating power and battery thermal management power consumption As shown in Formulas 3 to 5 respectively:

[0034] in, The ambient temperature.

[0035] S14: Determine the energy consumption model of the electric vehicle based on the driving efficiency factor, the cabin heating power, and the battery thermal management power consumption.

[0036] In one embodiment, the energy consumption of an electric vehicle is determined by the sum of three factors: the product of a pre-set baseline energy consumption and a driving efficiency factor, the product of the vehicle compartment heating power and the actual operating time, and the product of the battery thermal management power consumption and the actual operating time, as shown in the following formula:

[0037] in, The ambient temperature; This is the baseline energy consumption at 20℃; It is a driving efficiency factor in cold weather; It refers to the heating power of the carriage; It is the power consumed by battery thermal management; It is a road The actual travel time on the road.

[0038] S20: Construct a model to adjust road capacity and driving speed according to weather conditions.

[0039] In one embodiment, a weather-driven road capacity and speed correction model is used to characterize the decline in road capacity and speed under icy and snowy weather.

[0040] In one embodiment, step S20 constructs a road capacity and driving speed correction model that varies with weather conditions, including: S21: Construct a road capacity reduction coefficient and speed maintenance factor that vary with weather conditions.

[0041] In one embodiment, winter rain and snowfall affect traffic flow through multiple mechanisms: reduced tire-to-road friction, decreased visibility, snow accumulation blocking lanes, and drivers’ risk-avoidance behavior. This application models these effects using a road capacity reduction factor and a speed maintenance factor.

[0042] In one embodiment, the weather condition is represented by a discrete function. For discrete weather conditions, 0 represents clear weather (dry road surface), 1 represents light snow (1-3 cm of snowfall per hour, wet road surface), 2 represents moderate snow (3-6 cm of snowfall per hour, road surface covered by snow), and 3 represents blizzard (more than 6 cm of snowfall per hour or freezing rain).

[0043] In one embodiment, the road capacity reduction factor and velocity retention factor This can be expressed by the following formula:

[0044] S22: Determine the road capacity correction model based on the road capacity reduction coefficient.

[0045] In one embodiment, the road capacity correction model uses a pre-set benchmark road exit capacity. and road capacity reduction factor The product determines the actual road exit capacity. This is to correct road capacity.

[0046] In one embodiment, actual road exit capacity This can be expressed by the following formula:

[0047] in, For roads r The baseline road exit capacity; For roads r The actual road exit capacity.

[0048] S23: Determine the driving speed correction model based on the speed maintenance factor.

[0049] In one embodiment, the driving speed correction model is based on the driving time of the electric vehicle on the road at a pre-set normal temperature. and velocity retention factor The ratio determines the driving time of electric vehicles on the road under different weather conditions in cold climates, so as to achieve speed correction.

[0050] In one embodiment, electric vehicles on roads under different weather conditions in cold climates... r Travel time This can be expressed by the following formula:

[0051] in, Electric vehicles on roads under different weather conditions in cold climates r Travel time on the road; Electric vehicles on roads at normal temperatures r Travel time on the road.

[0052] The combined effect of reduced road capacity and lower speeds can double driving time in heavy snow, severely impacting electric vehicle route decisions and charging station access.

[0053] S30: Construct a charging efficiency model for electric vehicle batteries as temperature changes.

[0054] In one embodiment, a temperature-dependent charging efficiency model is used to describe the charging power decay and preheating energy consumption at low temperatures.

[0055] In one embodiment, step S30 constructs a charging efficiency model of the electric vehicle battery as a function of temperature, including: S31: Construct a temperature-dependent charging efficiency factor and battery preheating power.

[0056] In one embodiment, a cold battery needs to be preheated before accepting charging current to prevent lithium plating. This preheating consumes power from the charging station but does not increase the battery's charge level. Battery preheating ensures that the electric vehicle battery's state of charge is met before it can increase, accurately representing a long charging process in cold weather.

[0057] In one embodiment, the charging efficiency factor for different temperature ranges is described by a piecewise linear function. and battery preheating power As shown in the following formula:

[0058] S32: Determine the actual effective charging efficiency based on the charging efficiency factor and the nominal charging efficiency.

[0059] In one embodiment, by charging efficiency factor and nominal charging efficiency The product of these factors determines the actual effective charging efficiency. As shown in the following formula:

[0060] S33: Determine the charging efficiency model based on the actual effective charging efficiency and the battery preheating power.

[0061] In one embodiment, the charging efficiency model characterizes the charging power attenuation and preheating energy consumption at low temperatures using the actual power consumed by the charging pile. The actual power consumed by the charging pile is the larger of the preset power and the calculated power of the charging pile, where the calculated power of the charging pile is the charging power of the charging pile (related to the charging power level of the charging pile) and the charging efficiency factor. The product minus the battery preheating power .

[0062] In one embodiment, the actual power consumed by the charging pile is expressed by the following formula:

[0063] in, For charging power level The actual power consumed by the charging station; For charging power level The corresponding charging power; This refers to the actual effective charging efficiency.

[0064] The actual power consumed by the charging pile is the charging power level. The net power contribution of charging stations to the increase in electric vehicle battery capacity.

[0065] S40: Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, construct a traffic-power dual-layer optimization model, which includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model for the distribution network.

[0066] In one embodiment, S41: Construct an upper-level dynamic traffic assignment model.

[0067] S411: Construct a dynamic road model based on the road capacity and driving speed correction model.

[0068] Traffic flow on each road in the transportation network is modeled using a dynamic road model, and the queue dynamics are as follows:

[0069] in, r Number the road; j Number the path; t Number the time; for Time Path the way The number of electric vehicles queuing at road exits; for t -1 time path the way The number of electric vehicles queuing at road exits; This represents the number of electric vehicles entering the queue. Electric vehicles on roads under different weather conditions in cold climates r Travel time on the road This indicates that when a queue is created due to road exit capacity constraints, by default at the road exit, the electric vehicles entering the queue are... Enter the road r of; for Time Path Leave the road The number of electric vehicles.

[0070] The dynamic changes in the total number of vehicles on the road are as follows:

[0071] in, for Time Path the way The total number of electric vehicles on board; for t -1 time path the way The total number of electric vehicles on board; for Time Path Up onto the road The number of electric vehicles; for Time Path Leave the road The number of electric vehicles; Based on the road capacity and driving speed correction model, a road exit capacity constraint with weather-dependent limitations is obtained:

[0072] in, for Road at all times The actual road exit capacity is related to weather conditions and can be obtained through formula (9).

[0073] The dynamic road model can be used to obtain the dynamic changes of vehicle queues and total number of vehicles on each road over time, with constraints as shown in formula (17).

[0074] S412: Construct a dynamic charging station model based on the charging efficiency model.

[0075] Electric vehicles passing a charging station f At a node, one can freely pass through and enter for charging, as expressed by the following formula:

[0076] in, Indicates charging station f The forward path of a node, where the forward path is the path that gradually approaches the node; This represents the backward path of a node, where the backward path is the path that gradually moves away from the node; To leave the charging station f The number of electric vehicles on the road ahead of the node; for t Time Path j Enter the charging station f The number of electric vehicles at each node; for t Time Path j Do not enter the charging station f The number of electric vehicles at each node; for t Time Path j Leaving the charging stationf The number of electric vehicles at each node; for t Time Path j Enter the charging station f The number of electric vehicles on the road behind the node.

[0077] If charging station f When there is a queue, electric vehicles entering the charging station first join the waiting queue, as expressed by the following formula:

[0078] in, Number the charging stations; for Time Path Enter the charging station f The total number of electric vehicles in the waiting queue; for Time Path Up to charging station f The number of electric vehicles that have moved from the waiting queue to the charging service queue; This refers to the number of electric vehicles entering the charging station.

[0079] The dynamic process of the charging service queue is as follows:

[0080] in, Number the charging stations; for Time Path Up to charging station f Total number of electric vehicles in the charging service queue; for Time Path Up to charging station f The number of electric vehicles that have moved from the waiting queue to the charging service queue; for Time Path Leaving the charging station f The number of electric vehicles.

[0081] The capacity constraints for charging stations are as follows:

[0082] in, Number the charging stations; for Time Path Up to charging station Total number of electric vehicles in the charging service queue; For charging stations The total number of charging stations is preset.

[0083] The total charging power of the charging station considering temperature effects is:

[0084] in, The actual power consumed by the charging pile is obtained through S3; Charging power levels in the charging service queue of charging stations The corresponding total number of electric vehicles.

[0085] S413: Construct energy constraints for electric vehicles based on the battery capacity and energy consumption model.

[0086] To track battery energy state, this application introduces aggregated energy variables corresponding to traffic flow. e , representing the total energy state of the corresponding electric vehicle flow, with the following driving energy consumption on the road:

[0087] in, for t Time Path j On the road The aggregate energy level of the vehicle; for Time Path j Enter the road The aggregate energy level of electric vehicles at that time; Electric vehicles on roads under different weather conditions in cold climates r Travel time on the road; This indicates that electric vehicles on the road now are Enter the road r of; To complete the road The number of electric vehicles driving on the road; This refers to actual driving energy consumption.

[0088] The energy change of an electric vehicle caused by the charging operation is as follows:

[0089] in, Aggregated energy levels of electric vehicles in the charging service queue; The aggregate energy level of electric vehicles entering the charging service queue; The aggregate energy level of electric vehicles leaving the charging service queue; The actual power consumed by the charging pile is obtained through S3; Considering temperature-dependent battery capacity, the battery charge must be kept within a feasible range. The constraints on battery capacity are shown in the following formula:

[0090] in, It represents any location in the transportation network, including roads and nodes; This is the set lower limit for battery capacity; The number of electric vehicles at any location in the transportation network; The aggregated energy level of electric vehicles at any location within the transportation network; The ambient temperature is Battery capacity at that time.

[0091] The meaning of the above constraint is that the energy level of any segment of traffic flow in the transportation network (including on the road and at the charging station) (in this application, the driving of electric vehicles on the road is aggregated into traffic flow, and the energy level of the traffic flow = the aggregated energy level of the electric vehicle) is within the set upper and lower limits of the battery capacity.

[0092] S414: Construct driving demand allocation constraints.

[0093] Traffic flow allocation for electric vehicles must meet travel demand in every time period:

[0094] in, Number the origin and destination points for travel needs; For path The first section of the road; The number of electric vehicles assigned to the first segment of the route; For travel needs at specific start and end points.

[0095] According to the principle of flow conservation, all departing vehicles must reach their destination within the dispatch time:

[0096] in, For path The last section of the road; Traffic flow originating from the starting point; Traffic flow to the destination.

[0097] S415: Construct the upper-level dynamic traffic assignment model based on the dynamic road model and the charging station model. The upper-level dynamic traffic assignment model aims to minimize the travel cost of all electric vehicles. The constraints of the upper-level dynamic traffic assignment model include the energy constraints of the electric vehicles and the driving demand allocation constraints.

[0098] The optimization objective of the upper-level dynamic traffic assignment model is to minimize the travel cost of all electric vehicles.

[0099] in, This is the travel time cost coefficient; for Time Path the way The total number of electric vehicles on board; Time spent traveling on the road and in traffic congestion; for Time Path Enter the charging station f The total number of electric vehicles in the waiting queue; The total number of electric vehicles in the charging service queue at the charging station; For queuing and charging time at charging stations; The charging electricity price for charging stations is derived using the optimal DC power flow model of the underlying distribution network. Total charging cost for electric vehicles; Total charging power of the charging station.

[0100] It should be noted that: Includes time subscript t ,express t Road at all times r The number of electric vehicles right t The summation represents the summation of all time segments along the road within the scheduling time range. r Sum the number of electric vehicles on the road, for t The sum obtained represents the total time taken for all electric vehicles to complete their travel needs, including travel time on the road and time spent in traffic congestion. For example, consider a travel need where 3 electric vehicles need to cross a road. r ,the way r It takes 3 time steps to pass through, and the road exit capacity is 2. The conventional solution is that each of the 3 electric vehicles needs 3 time steps to pass through the road, which is 3 × 3 = 9 time steps. Due to the road exit capacity limit, the last electric vehicle needs to exit the road in the next time step, meaning it takes an additional 10 time steps (9 + 1 = 10 time steps) for all 3 electric vehicles to pass through. The solution using the formula in this application is: t At time 1, there are 3 electric cars on the road (start driving). t At time 2, there are still 3 electric cars on the road (all of them are in motion). tAt time 3, there are still 3 electric vehicles on the road (all at the end of the road, 2 of which are preparing to leave, and the last one has to wait until the next time step to leave due to the road exit capacity limit). t At time 4, there is one electric car preparing to drive off the road. For all times... t The sum of the number of electric vehicles on the road is 3 + 3 + 3 + 1 = 10, which is consistent with the result of the conventional solution. The same logic can be applied to understand this. This refers to the queuing time and charging time at the charging station.

[0101] In one embodiment, S42: Construct the optimal DC power flow model for the lower-level distribution network.

[0102] In one embodiment, the DC optimal power flow model of the lower-level distribution network aims to minimize the power generation cost of the distribution network; the constraints of the DC optimal power flow model of the lower-level distribution network include power balance constraints, phase angle constraints, power flow constraints, and power generation output constraints.

[0103] In one embodiment, assuming that the system will not experience exceedances in node voltage magnitude and power angle, the mathematical model is linear, and the DC model of the line is expressed as follows:

[0104] in, , M It is the set of all nodes in the power system; , N For power systems and nodes m The set of all connected nodes; for t Timetable mn The power value flowing through; For nodes m exist t The voltage phase angle at that moment; For the line mn The reactance value. For nodes n exist t The voltage phase angle at time t.

[0105] The system's optimal DC power flow problem needs to satisfy the following constraints:

[0106] in, , S This is the set of all nodes containing regular loads; For nodes s exist t The normal load power demand value at any given time; , HThis is the set of all nodes containing renewable energy sources; node h exist t The proportion of renewable energy consumption at any given time; For nodes h The output power value of renewable energy at any given time; For the nodal power balance constraint, the Lagrange multiplier is physically defined as the system's power balance multiplier. t Time Node m Electricity price; This represents the maximum power flow value of the system lines; For conventional generator sets g exist t Time of the first d The output increment on each segment For conventional generator sets m exist t The piecewise linear economic cost function at any given time represents the maximum power of piecewise production.

[0107] The DC optimal power flow model of the lower-level distribution network aims to minimize the generation cost of the distribution network, i.e., to minimize the economic cost of conventional generator operation. The quadratic economic cost of conventional generator operation is piecewise linearized, and the lower-level optimization objective is as follows:

[0108] in, , G It is the set of all nodes where conventional generator sets are located; These are the coefficients of the constant term in the operating cost function; , D It is the number of pieces of a piecewise linear function. It is the last segment of a piecewise linear function; For conventional generator sets m The operating economic cost function of the first d The slope of the segment; For conventional generator sets g exist t Time of the first d The increase in output on each segment; For the piecewise linear economic cost function, the first... d The length of the power range, It is the rated maximum output of the generator set.

[0109] The DC model of the line and its constraints are connected to the lower-level optimization objective through variables. Related, It is both a variable in the equality constraint and a decision variable for the lower-level optimization objective.

[0110] S50: Based on the pre-set solution model, solve the traffic-power two-layer optimization model to obtain the spatiotemporal distribution of electric vehicle traffic flow and power distribution network.

[0111] In one embodiment, the KKT conditions are used to transform the DC optimal power flow model of the lower-level distribution network into a set of constraints, and the traffic-power two-layer optimization model is transformed into a single-layer optimization model; the single-layer optimization model is solved based on the pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow-distribution network.

[0112] In one embodiment, the Karush-Kuhn-Tucker Conditions (KKT Conditions) are the core theory for handling nonlinear programming problems with inequality constraints. They extend the gradient conditions (derivatives of 0) of unconstrained optimization to constrained scenarios, integrating the relationship between the objective function gradient and the constraints.

[0113] In one embodiment, the KKT conditions are used to transform the DC optimal power flow model of the lower-level distribution network into a set of constraints as follows:

[0114] In the formula, The Lagrangian function representing the lower-level optimization problem (the DC optimal power flow model of the lower-level distribution network); Variables representing the upper-level optimization problem (upper-level dynamic traffic assignment model); Variables representing the lower-level optimization problem; Equality constraints representing lower-level optimization problems; Inequality constraints represent the lower-level optimization problem.

[0115] Using the Big M method to relax the nonlinear complementary constraints in the KKT conditions Transform into linear constraints: ; In the formula, Auxiliary variables that represent values ​​of 0 or 1. M 0 is a sufficiently large constant. The Big M method is an artificial variable method for solving linear programming problems with artificial variables. It assigns a very large penalty coefficient to the artificial variables in the objective function. M (Usually positive numbers, for minimization problems) force artificial variables to take the value of 0 in the optimal solution, thereby eliminating their influence on the objective function.

[0116] Substituting the transformed KKT conditions (Formula (37)) into the upper-level dynamic traffic assignment model, a single-layer optimization model is obtained. Solving the model using the solver yields the spatiotemporal distribution results of electric vehicle traffic flow and power distribution network, including electric vehicle traffic flow on each road at each time, load of each charging station at each time, power generation scheduling of the power distribution system, and nodal electricity price at each time.

[0117] like Figure 3 As shown, in the collaborative optimization process of the traffic-power dual-layer optimization model, the upper-layer dynamic traffic assignment model updates the charging station load in real time to the lower-layer DC optimal power flow model of the distribution network, and the lower-layer DC optimal power flow model of the distribution network updates the node electricity price in real time to the upper-layer dynamic traffic assignment model.

[0118] The solver solves the model pre-set. This application uses linearization and mixed integer programming to solve the model, making it computable in scheduling and planning scenarios.

[0119] Specifically, the present application will be further illustrated below through embodiments: This embodiment uses a 22-node traffic network and a 21-node power distribution network for testing. The traffic network topology of the embodiment is as follows: Figure 4 As shown, in the transportation network, T represents a traffic node and F represents a charging station. The power network topology is as follows: Figure 5 As shown, in the power distribution network, E represents a power distribution station node and F represents a charging station. Figure 4 and Figure 5 It demonstrates the coupling relationship between transportation and electricity. Figure 5 Nodes F4 and F6 are connected to wind turbines, and node E4 is connected to distributed generators. The travel demand of electric vehicles is to depart from nodes T4 and T9 and travel to T1, T2, T3, T6, T7, T10, T11, and T12.

[0120] Figure 6 This paper presents simulation results of existing traffic-electricity coupled optimization models and simulation results of the proposed electric vehicle traffic-electricity co-optimization method under cold climate conditions. The simulation results include the time-varying variations of electric vehicle charging load and power distribution system load. Figure 6As can be seen, during most periods, the charging load in cold weather is higher than the charging load simulated by existing traffic-power coupling optimization models. The method in this application can accurately reflect the increased energy demand of electric vehicles due to cold weather. Furthermore, factors such as reduced battery capacity and increased energy consumption in cold weather force electric vehicles to enter charging stations before reaching their battery capacity limits. Even during periods of higher electricity prices, this causes a local peak, putting local pressure on the distribution network, which existing traffic-power coupling optimization models cannot predict. The method in this application can accurately reflect the increased duration of electric vehicle charging activities in cold weather, due to both the delayed arrival time at charging stations caused by reduced driving speed and the extended charging time caused by reduced charging power.

[0121] Table 1 illustrates the increase in electric vehicle travel time cost, charging quantity, and charging cost when considering cold weather using the method described in this application. The increase in travel time cost stems from the reduced vehicle speed and decreased road capacity in cold and snowy conditions. The increase in charging quantity is due to the reduced battery capacity and increased energy consumption in cold weather. The increase in charging cost is higher than that of charging quantity. This is because electric vehicles must be charged even during periods of higher electricity prices in order to ensure sufficient battery power for continuous driving in cold weather. Under normal weather conditions, due to the larger actual battery capacity, electric vehicles have greater flexibility and can shift charging time to off-peak hours when electricity prices are lower.

[0122] Table 1 Comparison of Electric Vehicle Travel Costs

[0123] As can be seen, in the above scheme, this application uses a battery capacity and energy consumption model to characterize the impact of cold climate on battery capacity and energy consumption, a road capacity and driving speed correction model to characterize the impact of cold climate on road capacity, and a charging efficiency model to characterize the impact of cold climate on charging efficiency. By integrating the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model into the traffic-power two-layer optimization model, the accuracy and safety of system operation under cold climate conditions are improved. This avoids the risk of load underestimation and power flow exceeding limits caused by ideal climate assumptions, and realizes dynamic two-layer collaborative optimization of electric vehicles and power systems. It can achieve mutual feedback regulation between traffic and power grid within a spatiotemporal dynamic framework, reduce estimation bias, and effectively reflect system behavior under extreme winter weather conditions.

[0124] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0125] In one embodiment, an electric vehicle traffic-electricity cooperative optimization device is provided, which corresponds one-to-one with the electric vehicle traffic-electricity cooperative optimization method in the above embodiments. For example... Figure 7 As shown, the electric vehicle traffic-electricity collaborative optimization device includes a first construction module 101, a second construction module 102, a third construction module 103, an optimization module 104, and a solution module 105. Detailed descriptions of each functional module are as follows: The first building module 101 is used to build a model of the battery capacity and energy consumption of electric vehicles as temperature changes; The second construction module 102 is used to construct a road capacity and driving speed correction model that changes with weather conditions; The third building module 103 is used to build a charging efficiency model of electric vehicle batteries as temperature changes; Optimization module 104 is used to construct a traffic-electricity dual-layer optimization model based on the battery capacity and energy consumption model, the road capacity and driving speed correction model and the charging efficiency model. The traffic-electricity dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. The solution module 105 is used to solve the traffic-electricity dual-layer optimization model based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow.

[0126] The first building module 101 is also used to build the capacity retention factor of the electric vehicle battery as temperature changes; The battery capacity model is determined based on the capacity retention factor and the nominal battery capacity. Construct the driving efficiency factor, cabin heating power, and battery thermal management power consumption of electric vehicles as they change with temperature; The energy consumption model of the electric vehicle is determined based on the driving efficiency factor, the cabin heating power, and the battery thermal management power consumption.

[0127] The second building module 102 is also used to build a road capacity reduction factor and a speed maintenance factor that change with weather conditions; The road capacity correction model is determined based on the road capacity reduction coefficient. The driving speed correction model is determined based on the speed maintenance factor.

[0128] The third building module 103 is also used to build the charging efficiency factor and battery preheating power that vary with temperature. The actual effective charging efficiency is determined based on the charging efficiency factor and the nominal charging efficiency. The charging efficiency model is determined based on the actual effective charging efficiency and the battery preheating power.

[0129] The optimization module 104 is also used to construct a dynamic road model based on the road capacity and driving speed correction model; Construct a charging station model based on the charging efficiency model; Based on the battery capacity and energy consumption model, construct the energy constraints for electric vehicles; Construct constraints for driving demand allocation; The upper-level dynamic traffic assignment model is constructed based on the dynamic road model and the charging station model. The upper-level dynamic traffic assignment model aims to minimize the travel cost of all electric vehicles. The constraints of the upper-level dynamic traffic assignment model include the energy constraints of electric vehicles and the travel demand allocation constraints.

[0130] The optimization module 104 is also used to determine the optimal DC power flow model of the lower-level distribution network with the optimization objective of minimizing the power generation cost of the distribution network; The constraints of the DC optimal power flow model of the lower-level distribution network include power balance constraints, phase angle constraints, power flow constraints, and power generation output constraints.

[0131] Solving model 105 is also used to transform the DC optimal power flow model of the lower-level distribution network into a set of constraints using KKT conditions, and to transform the traffic-power two-layer optimization model into a single-layer optimization model. The single-layer optimization model is solved based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow and power distribution network.

[0132] This invention provides a traffic-power coordinated optimization device for electric vehicles. This application uses a battery capacity and energy consumption model to characterize the impact of cold weather on battery capacity and energy consumption, a road capacity and speed correction model to characterize the impact of cold weather on road capacity, and a charging efficiency model to characterize the impact of cold weather on charging efficiency. By integrating these models into a two-layer traffic-power optimization model, the accuracy and safety of system operation under cold weather conditions are improved. This avoids the risks of load underestimation and power flow exceeding limits caused by ideal climate assumptions, achieving dynamic two-layer coordinated optimization of electric vehicles and the power system. It enables mutual feedback regulation between traffic and the power grid within a spatiotemporal dynamic framework, reducing estimation bias and effectively reflecting system behavior under extreme winter weather conditions.

[0133] Specific limitations regarding the electric vehicle traffic-electricity coordinated optimization device can be found in the limitations of the electric vehicle traffic-electricity coordinated optimization method described above, and will not be repeated here. Each module in the aforementioned electric vehicle traffic-electricity coordinated optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0134] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a server-side method for electric vehicle traffic-electricity coordinated optimization.

[0135] In one embodiment, a computer device is provided, which may be a device terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of an electric vehicle traffic-electricity coordinated optimization method on the device side.

[0136] 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 executes the computer program to perform the following steps: Construct a model of battery capacity and energy consumption of electric vehicles as temperature changes; Construct a road capacity and driving speed correction model that adapts to changes in weather conditions; Construct a charging efficiency model for electric vehicle batteries as a function of temperature; Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. Based on the pre-set solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Construct a model of battery capacity and energy consumption of electric vehicles as temperature changes; Construct a road capacity and driving speed correction model that adapts to changes in weather conditions; Construct a charging efficiency model for electric vehicle batteries as a function of temperature; Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. Based on the pre-set solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow.

[0138] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and device side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0141] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for coordinated optimization of traffic and power systems for electric vehicles, characterized in that, include: Construct a model of battery capacity and energy consumption of electric vehicles as temperature changes; Construct a road capacity and driving speed correction model that adapts to changes in weather conditions; Construct a charging efficiency model for electric vehicle batteries as a function of temperature; Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. Based on the pre-set solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow.

2. The method according to claim 1, characterized in that, Construct a model of battery capacity and energy consumption of electric vehicles as a function of temperature, including: Construct the capacity retention factor of electric vehicle batteries as a function of temperature; The battery capacity model is determined based on the capacity retention factor and the nominal battery capacity. Construct the driving efficiency factor, cabin heating power, and battery thermal management power consumption of electric vehicles as they change with temperature; The energy consumption model of the electric vehicle is determined based on the driving efficiency factor, the cabin heating power, and the battery thermal management power consumption.

3. The method according to claim 1, characterized in that, Construct a road capacity and driving speed correction model that adapts to weather conditions, including: Construct a road capacity reduction coefficient and speed maintenance factor that vary with weather conditions; The road capacity correction model is determined based on the road capacity reduction coefficient. The driving speed correction model is determined based on the speed maintenance factor.

4. The method according to claim 1, characterized in that, Construct a charging efficiency model for electric vehicle batteries as a function of temperature, including: Construct a charging efficiency factor and battery preheating power that vary with temperature; The actual effective charging efficiency is determined based on the charging efficiency factor and the nominal charging efficiency. The charging efficiency model is determined based on the actual effective charging efficiency and the battery preheating power.

5. The method according to claim 1, characterized in that, Based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model, a traffic-power dual-layer optimization model is constructed. This model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow optimization model for the distribution network, comprising: A dynamic road model is constructed based on the road capacity and driving speed correction model. A dynamic charging station model is constructed based on the charging efficiency model. Based on the battery capacity and energy consumption model, construct the energy constraints for electric vehicles; Construct constraints for driving demand allocation; The upper-level dynamic traffic assignment model is constructed based on the dynamic road model and the charging station model. The upper-level dynamic traffic assignment model aims to minimize the travel cost of all electric vehicles. The constraints of the upper-level dynamic traffic assignment model include the energy constraints of electric vehicles and the travel demand allocation constraints.

6. The method according to claim 1, characterized in that, The optimal DC power flow model of the lower-level distribution network aims to minimize the generation cost of the distribution network. The constraints of the DC optimal power flow model of the lower-level distribution network include power balance constraints, phase angle constraints, power flow constraints, and power generation output constraints.

7. The method according to claim 1, characterized in that, Based on a pre-defined solution model, the traffic-electricity two-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic flow, including: The KKT conditions are used to transform the DC optimal power flow model of the lower-level distribution network into a set of constraints, and the traffic-power two-level optimization model is transformed into a single-level optimization model. The single-layer optimization model is solved based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow and power distribution network.

8. An electric vehicle traffic-electricity coordinated optimization device, characterized in that, include: The first building block is used to build a model of the battery capacity and energy consumption of electric vehicles as temperature changes. The second building module is used to build a road capacity and driving speed correction model that changes with weather conditions; The third building module is used to build a charging efficiency model of electric vehicle batteries as temperature changes; The optimization module is used to construct a traffic-power dual-layer optimization model based on the battery capacity and energy consumption model, the road capacity and driving speed correction model, and the charging efficiency model. The traffic-power dual-layer optimization model includes an upper-layer dynamic traffic assignment model and a lower-layer DC power flow model of the distribution network. The solution module is used to solve the traffic-electricity two-layer optimization model based on a pre-set solution model to obtain the spatiotemporal distribution of electric vehicle traffic flow.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle transportation-electricity collaborative optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle traffic-electricity collaborative optimization method as described in any one of claims 1 to 7.

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