Optimized scheduling method for electricity price of charging station and charging and discharging power of pure electric heavy truck

Abstract

The invention discloses an optimal scheduling method for the electricity price of a charging station and the charging and discharging power of a pure electric heavy truck. The method comprises the following steps: (1) establishing and describing a road topology of a cluster driving network of the pure electric heavy truck; (2) establishing a battery loss model of the pure electric heavy truck; (3) establishing a travel power consumption function model of the pure electric heavy truck; (4) establishing a travel power consumption model of the pure electric heavy truck; (5) establishing a travel power consumption model of the pure electric heavy truck; (6) establishing a charging probability and V2G response probability model of the pure electric heavy truck; (7) predicting the charging time and the response capability of the pure electric heavy truck; (8) establishing a charging station income upper-layer optimization model based on V2G response; (9) establishing a pure electric heavy truck charging cost lower-layer optimization model based on time window penalty, power grid load variance and V2G response; and (10) solving the model by adopting a master-slave game method. According to the method, the defects in the prior art can be overcome, peak loads are reduced, valley loads are improved, overall load distribution is more balanced, and therefore the stability and economical efficiency of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to an optimization scheduling method for charging station electricity prices and the charging and discharging power of battery electric heavy trucks. Background Art

[0002] With the development of clean energy, the new energy vehicle industry is constantly evolving. Compared with ordinary private electric vehicles, battery electric heavy trucks (ETs) are usually fewer in number, but the charging load of a single vehicle is large, which will cause a greater impact on the power grid during charging. At the same time, due to their strong flexibility, they can be used as distributed power sources at the source end and flexible loads at the load end, thus realizing two-way vehicle-grid interaction. Therefore, under a reasonable regulation strategy, correctly guiding the disorderly charging behavior of large-scale ETs can not only avoid problems such as line overload and power quality degradation caused by disorderly charging, but also effectively smooth the load peak-valley difference and renewable energy fluctuations, reduce network losses, and improve power grid stability. As a bridge connecting ETs and the power grid, by formulating a reasonable price strategy, an electric vehicle charging station can guide the charging and discharging of large-scale ETs, improve the economic benefits of the charging station and the safety and stability of the power grid, while reducing the costs of ET users, achieving a win-win situation for multiple parties. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an optimization scheduling method for charging station electricity prices and the charging and discharging power of battery electric heavy trucks in view of various deficiencies of the prior art.

[0004] To solve the above technical problem, the technical solutions adopted by the present invention are as follows.

[0005] An optimization scheduling method for charging station electricity prices and the charging and discharging power of battery electric heavy trucks includes the following steps:

[0006] (1) Establish and describe the road topology of the driving network of the battery electric heavy truck cluster;

[0007] (2) Establish a battery loss model for battery electric heavy trucks based on temperature data;

[0008] (3) Establish a travel power consumption function model for battery electric heavy trucks based on load data;

[0009] (4) Establish a travel power consumption model for battery electric heavy trucks based on temperature data;

[0010] (5) Establish a travel power consumption model for battery electric heavy trucks based on the additional losses in the V2G mode;

[0011] (6) Analyze the travel characteristics of battery electric heavy trucks and establish a charging probability and V2G response probability model for battery electric heavy trucks;

[0012] (7) Predict the charging time and response ability of pure electric heavy trucks;

[0013] (8) Establish an upper-layer optimization model for the revenue of charging stations based on V2G response;

[0014] (9) Establish a lower-layer optimization model for the charging cost of pure electric heavy trucks based on time-window penalty, grid load variance, and V2G response;

[0015] (10) Solve the model using the master-slave game method.

[0016] As a preferred technical solution of the present invention, in step (1), the establishment of the road topology of the pure electric heavy truck cluster driving network includes the following steps.

[0017] Assume that the road for pure electric heavy trucks is two-way, and the topological structure is described by graph theory. Each node represents an intersection, and each edge represents a road section; then each edge is assigned a description to accurately describe the actual connection and traffic flow between roads.

[0018] The topological structure of the road is described in the form of G(V, E), where V represents the set of nodes in the topological structure, including endpoints and intersections, denoted as {1, 2,..., n}, and E represents the set of road sections in the road network; the distance relationship between points a and b in the road network set G is described by the adjacency matrix D, and the distance between a and b is represented by d ab to represent, d ab is an element of matrix D, d ab As shown in the following formula,

[0019]

[0020] In the formula, r ab is the road section between nodes a and b, and l ab is the length of the road section between a and b; inf indicates that the road section formed by nodes a and b is not directly connected.

[0021] Select the travel time as the road impedance model for model establishment, introduce the speed-flow model, and the expression for the driving speed V of pure electric heavy trucks is:

[0022]

[0023] In the formula: v 0 is the zero-flow speed of pure electric heavy trucks on the road; c is the traffic capacity of the road during this time period; q is the traffic flow of the road per unit time; p, d, and n are road grade adaptation coefficients.

[0024] The expression for the road impedance model T ab of pure electric heavy trucks considering traffic signal time and charging times is:

[0025]

[0026] Where: L ab is the length of road ab, t l is the waiting time of the intersection signal light, m is the number of charging times, R is the current available cruising range, t c is the ET charging time, and [x] is the rounding function.

[0027] As a preferred technical solution of the present invention, in step (2), establishing a battery loss model for a battery electric heavy truck based on temperature data includes the following steps:

[0028] The calculation expression for the actual maximum load capacity of the battery at different temperatures is:

[0029] E tmax = E 20 ·e a(t-20)

[0030] Where, E tmax is the maximum load capacity of the battery electric heavy truck battery at different temperatures t; E 20 is the battery capacity of the battery electric heavy truck when the ambient temperature is 20 degrees; a is the fitting parameter of the influence of temperature on the battery capacity of the battery electric heavy truck; considering that the battery capacity undergoes irreversible attenuation, that is, battery aging, due to multiple charge and discharge cycles, and affects the maximum load capacity of the battery next time, an Arrhenius model is constructed as the basis, and considering the depth of discharge, ambient temperature, charging power, and vehicle driving mileage, the maximum load capacity model of the battery electric heavy truck battery is updated, and the expression is:

[0031]

[0032] Where: E r,tmax is the maximum load capacity of the ET battery after the rth charge; L is the loss degree of the lithium iron phosphate battery; E a is the activation energy of the lithium iron phosphate battery reaction; R is the ideal gas constant; T is the actual ambient temperature where the ET is located, T 0 is the reference ambient temperature; P is the actual charging power of the battery electric heavy truck, P 0 is the reference charging power of the battery electric heavy truck; DoD is the actual depth of discharge of the battery electric heavy truck, DoD 0 is the reference depth of discharge; S represents the actual driving mileage of the battery electric heavy truck; S 0 is the reference driving mileage, taking the driving mileage of the battery electric heavy truck under standard test conditions; α, β, and γ are the influence coefficients of charging power, depth of discharge, and driving mileage on battery loss, respectively.

[0033] As a preferred technical solution of the present invention, in step (3), establishing a travel power consumption function model for an all-electric heavy truck based on load data includes the following steps:

[0034] First, calculate the mechanical power P of the all-electric heavy truck during driving e . During the uniform driving process of the all-electric heavy truck, there are three types of resistances, namely air resistance, rolling resistance, and frictional resistance. The expression of the mechanical power of the all-electric heavy truck during driving is as follows:

[0035] P e = k 1 v 3 + k 2 (M + m)gv + μ(M + m)gv

[0036] In the formula: v is the driving speed of the all-electric heavy truck; k 1 is the air resistance coefficient; k 2 is the rolling resistance coefficient; μ is the frictional resistance coefficient; g is the acceleration due to gravity; m and M are the weight of the all-electric heavy truck itself and the load respectively;

[0037] Then the electric power P m of the all-electric heavy truck is:

[0038]

[0039] In the formula: η e is the efficiency of converting electrical energy into mechanical energy during the driving of the all-electric heavy truck;

[0040] According to the obtained mechanical energy efficiency, based on the influence of temperature on the battery discharge efficiency, calculate the total power consumption Q e of the all-electric heavy truck:

[0041]

[0042] In the formula: U is the voltage of the all-electric heavy truck battery; t is the driving time of the all-electric heavy truck; η b is the discharge efficiency of the battery at the ambient temperature T; a, b, c are constants determined according to different battery materials and structures;

[0043] Therefore, the power consumption per kilometer of the all-electric heavy truck is:

[0044]

[0045] In the formula, d is the driving distance.

[0046] As a preferred technical solution of the present invention, in step (4), establishing a travel power consumption model for an all-electric heavy truck based on temperature data includes the following steps:

[0047] Construct the air conditioner turning-on probability factor model k as:

[0048]

[0049] Wherein, T 1 and T 2 are the upper and lower temperature thresholds for setting the air conditioner to turn on. A piecewise function is used to represent the relationship between the power of the vehicle-mounted air conditioner and the daily temperature, and the expression is:

[0050]

[0051] Wherein: P 1 is the operating power of the vehicle-mounted air conditioner of the battery electric heavy truck at temperature T; T H and T L are respectively the temperature threshold for the air conditioner to start operating at low power and the temperature threshold for the air conditioner to maintain the basic minimum power; P 1max and P 1min are respectively the maximum power and the minimum power of the air conditioner operation;

[0052] Then the expression for the air conditioner power consumption per unit kilometer of the battery electric heavy truck is:

[0053]

[0054] Wherein: Q t is the air conditioner power consumption per unit kilometer of the battery electric heavy truck; P 1 is the operating power of the vehicle-mounted air conditioner of the battery electric heavy truck at temperature T; d is the driving mileage of the battery electric heavy truck; v is the driving speed of the battery electric heavy truck.

[0055] As a preferred technical solution of the present invention, in step (5), establishing the power consumption model of the battery electric heavy truck trip based on the additional loss in the V2G mode includes the following steps,

[0056] The power consumption participating in V2G is:

[0057]

[0058] Wherein: Q V2G is the power consumption of the battery electric heavy truck participating in V2G; P i,t is the discharge power of the battery of the i-th battery electric heavy truck at time t; k V2G is the factor for the battery electric heavy truck user to participate in the V2G response; Δt represents the time interval.

[0059] As a preferred technical solution of the present invention, in step (6), establishing the charging probability and V2G response probability model of the battery electric heavy truck includes the following steps,

[0060] The Gaussian mixture model is used to describe the travel time distribution of battery electric heavy trucks, which is used to characterize the differences in driving habits of different types of battery electric heavy trucks. Suppose the battery electric heavy trucks have travel peaks at 8-12 am and 18-21 pm respectively, then the expression is as follows:

[0061]

[0062] In the formula: x represents the travel time; Π is the pi;

[0063] The SOC of battery electric heavy trucks is a random variable with a Gaussian distribution, and its probability density function is:

[0064]

[0065] In the formula: Π is the pi;

[0066] The power consumption of battery electric heavy trucks increases non-linearly with the driving distance. The SOC of the battery electric heavy truck at time t is: Q t =Q 0 -Q e Δl

[0067] In the formula: Q 0 is the initial charge of the battery electric heavy truck when it just starts; Δl is the driving distance of the battery electric heavy truck from 0 to time t;

[0068] The lower the SOC, the greater the probability that the battery electric heavy truck chooses to charge. At the same time, the pricing of the charging station also has an impact on the charging of the battery electric heavy truck. The charging probability of the battery electric heavy truck based on the electricity price is:

[0069]

[0070] In the formula: Q L is the lower threshold of the initial charge of the battery electric heavy truck affecting charging, Q H is the upper threshold of the initial charge of the battery electric heavy truck affecting charging; Q t is the initial charge of the battery electric heavy truck at time t; k is the electricity price sensitivity coefficient; p a is the average charging electricity price, p is the current charging electricity price;

[0071] At the same time, participating in V2G requires that the SOC of the battery electric heavy truck is above the set threshold. According to the initial SOC value Q is of the i-th battery electric heavy truck connected to the charging pile and the expected value Q ia when charging ends, calculate the charging time T i and the shortest charging time T i,d under ideal conditions:

[0072]

[0073] In the formula: Q isis the initial state of charge when charging the battery of a pure - electric heavy - duty truck; Q ia is the desired state of charge when the charging of the pure - electric heavy - duty truck ends; ηi is the charging efficiency of the pure - electric heavy - duty truck; P i is the charging power of the pure - electric heavy - duty truck;

[0074] The V2G response probability formula based on the user's response willingness is:

[0075]

[0076] In the formula: B is the probability that the pure - electric heavy - duty truck user responds to V2G; T i is the charging time; T i,d is the shortest charging time under ideal conditions; R is the regional response coefficient; E i and Q i are the battery capacity and the remaining charge of the pure - electric heavy - duty truck respectively; Q th is the battery capacity threshold for the pure - electric heavy - duty truck user to participate in V2G.

[0077] As a preferred technical solution of the present invention, in step (7), predicting the charging time and response ability of the pure - electric heavy - duty truck includes the following steps,

[0078] According to the grid response period T dr and the grid - connection period T chr，i of the i - th pure - electric heavy - duty truck, calculate the demand response period T dr，i that the i - th pure - electric heavy - duty truck can participate in, which is

[0079] T dr,i = T dr ∩T chr,i

[0080] If the charging time of the pure - electric heavy - duty truck overlaps with the grid demand response time, that is, this pure - electric heavy - duty truck has the response ability; first, set the road network parameters and the DR period, generate the departure time and initial SOC of each pure - electric heavy - duty truck through Monte Carlo sampling, read the origin - destination information from the matrix D; use the dijkstra algorithm to plan the driving route, calculate the road travel time according to the speed - flow model; simulate the driving of the pure - electric heavy - duty truck, judge whether there is a charging demand, if so, select the nearest charging station to drive in and record the load information, and report the demand response ability of the pure - electric heavy - duty truck.

[0081] As a preferred technical solution of the present invention, in step (8), establishing the upper - layer optimization model of the charging station revenue based on V2G response includes the following steps,

[0082] The objective function of the charging station is the charging station revenue, which is expressed as follows:

[0083] Charging station revenue = electricity sales revenue - electricity purchase cost + compensation revenue obtained by participating in V2G dispatching

[0084]

[0085] Where: max F 2 is the profit of the pure electric heavy truck charging station; P i,c,t is the charging power of the pure electric heavy truck at time t; P i,d,t is the discharging power of the pure electric heavy truck at time t; N is the total number of pure electric heavy trucks participating in charging and discharging, and T is the total charging and discharging time of the i-th pure electric heavy truck; η c and η d are the charging and discharging efficiencies of the pure electric heavy truck battery respectively; M i,c,t and M i,d,t are the charging and discharging decision variables: and M i,c,t ·M i,d,t = 0; C ch is the charging price of the charging station at time t; C dt is the discharging price of the charging station at time t; C buy and C sell are the electricity purchase price from the power grid and the electricity selling price to the power grid of the charging station; C V2G is the reward price obtained by the charging station for mobilizing pure electric heavy trucks to participate in V2G;

[0086] The constraint conditions are:

[0087] 1. Maximum charging and discharging power constraint of the charging station: The charging and discharging power of the pure electric heavy truck charging station is restricted by the power grid load:

[0088]

[0089] Where: P i,c,nmax , P i,d,nmax represent the maximum charging and discharging powers of the charging station respectively;

[0090] 2. Customized charging and discharging prices of the charging station: The charging and discharging prices of the pure electric heavy truck charging station are restricted by the power grid:

[0091]

[0092] Where, and are the lower threshold and upper threshold of the charging price set by the charging station; and are the lower threshold and upper threshold of the discharging price set by the charging station.

[0093] As a preferred technical solution of the present invention, in step (9), establishing a lower-layer optimization model for the charging cost of battery electric heavy trucks based on time-window penalty, grid load variance, and V2G response includes the following steps.

[0094] The objective function on the user side of battery electric heavy trucks is the user cost.

[0095] The user cost = power purchase cost + loss cost - revenue from participating in V2G, and the expression is as follows:

[0096]

[0097] In the formula: min F 1 is the total cost of ET users; P i,c,t is the charging power of ET users at time t; C ch is the charging price of the charging station at time t; C b is the basic cost of the ET battery; E B is the rated capacity of the ET battery; L is the number of cycles of the ET battery; DoD is the depth of discharge of the ET battery; P i,d,t is the discharge power of ET users at time t; C dt is the discharge price of the charging station at time t; η d is the discharge efficiency of the ET battery; C d is the soft time-window penalty cost of ET, where T is the driving time of the i-th ET, T e and T l are the upper and lower thresholds of the early arrival and late arrival of ET delivery respectively; a and b are the penalty costs per unit time for early arrival and late arrival respectively.

[0098] The constraint conditions are:

[0099] 1. Charge and discharge power constraint: The charge and discharge power of ET is restricted by the charging station and its own battery attributes:

[0100]

[0101] In the formula: P i,c,tmax and P i,d,tmax are the maximum charging and discharging powers of the i-th ET respectively.

[0102] 2. Remaining power constraint: The participation of ET in response is restricted by its own power:

[0103] 0.35S i,t,p ≤S i,t ≤S i,t,p

[0104] In the formula: S i,t,p is the rated capacity of the ET battery;

[0105] 3. Grid load variance constraint:

[0106] To reduce the impact on the power grid caused by pure electric heavy trucks during response, the grid load variance is used as a constraint condition:

[0107]

[0108] In the formula: The whole day is divided into i time steps, and L i is the power grid load at time step i; L 0 is the initial load of the power grid at time step i; Δt represents the time interval, is the average load of the power grid at time step i; σ 2 is the maximum allowable variance of the power grid load setting.

[0109] As a preferred technical solution of the present invention, in step (10), using the master-slave game method to solve the model includes the following steps.

[0110] Establish a two-layer optimization master-slave game model for pure electric heavy trucks, and set the game convergence error;

[0111] Generate the initial charging and discharging electricity prices, and set the number of iterations to 0;

[0112] Predict the ET response willingness through the response ability prediction of the pure electric heavy truck cluster, and update the ET corresponding cluster;

[0113] The upper-layer charging station obtains the charging station revenue and the charging and discharging power of the pure electric heavy truck according to the charging corresponding cluster of the lower-layer pure electric heavy truck; if the charging station revenue does not meet the accuracy, the next iteration is carried out;

[0114] When the charging station revenue value meets the accuracy requirement, the iteration ends and the equilibrium solution is obtained.

[0115] The beneficial effects of adopting the above technical solutions are as follows: The invention establishes a two-layer optimization model of the upper-layer charging station revenue and the lower-layer pure electric heavy truck user cost by predicting the response ability of the pure electric heavy truck. The upper layer controls the charging time of the pure electric heavy truck through reasonable price customization, reduces the peak-valley difference, avoids excessive impact on the power grid, and maximizes its own revenue at the same time. The lower layer selects the optimal charging and discharging power after confirming the charging plan of the upper layer to achieve the purpose of minimizing the cost loss. Using the master-slave game method to solve the two-layer optimization model to obtain the optimal scheduling plan, and finally using the master-slave game method to solve the numerical example, which is more in line with the actual situation. Description of the Drawings

[0116] Figure 1 is the framework flow chart of the present invention;

[0117] Figure 2 is the topological map of the road where the pure electric heavy truck travels;

[0118] Figure 3 is the power grid load prediction curve;

[0119] Figure 4 is the electricity price formulation curve of the charging station;

[0120] Figure 5 is the master - slave game and disorder comparison diagram of the charging and discharging power of pure - electric heavy trucks. Detailed implementation manners

[0121] The following embodiments illustrate the present invention in detail. In the description of the following embodiments, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well - known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application. It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. It should also be understood that the term "and / or" as used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0122] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if detected [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detected [the described condition or event]", or "in response to detecting [the described condition or event]" according to the context. Additionally, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0123] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that in one or more embodiments of this application, specific features, structures or characteristics described in connection with that embodiment are included. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0124] Referring to Figure 1 , a specific embodiment of the present invention includes the following steps:

[0125] (1) Establish and describe the road topology of the pure electric heavy truck cluster driving network; specifically including the following steps,

[0126] Assume that the road for the pure electric heavy truck to drive is two-way, and the topological structure is described by the graph theory method, where each node represents an intersection and each edge represents a road section; then each edge is assigned a value for description to accurately describe the actual connection and traffic flow between roads;

[0127] The topological structure of the road is described in the form of G(V, E), where V represents the set of nodes in the topological structure, including endpoints and intersections, expressed as {1, 2,..., n}, and E represents the set of road sections in the road network; the distance relationship between points a and b in the road network set G is described by the adjacency matrix D, and the distance between a and b is represented by d ab to represent, d ab is an element of matrix D, d ab is shown as follows,

[0128]

[0129] In the formula, l ab is the length of the road section between a and b, r ab is the road section between nodes a and b; inf means that the road section formed by nodes a and b is not directly connected;

[0130] Select the travel time as the road impedance model for model establishment, introduce the speed-flow model, and the expression for the driving speed V of the pure electric heavy truck is:

[0131]

[0132] In the formula: v 0is the zero-flow speed of the pure electric heavy truck on the road; c is the traffic capacity of the road during this period; q is the traffic flow of the road per unit time; p, d, and n are road grade adaptation coefficients; where n = 3, when the road grade is Ι, p = 1.726, d = 3.15; when the road grade is Π, p = 2.076, d = 2.87;

[0133] The traditional speed-flow model only considers the influence of road traffic flow and road grade on the driving process of pure electric heavy trucks, and does not consider the influence of signal waiting time and charging times during the actual driving process on the driving process of pure electric heavy trucks. On the basis of the original road resistance model, it is improved to introduce a pure electric heavy truck road resistance model considering traffic signal time and charging times. The pure electric heavy truck road resistance model T considering traffic signal time and charging times ab The expression is:

[0134]

[0135] In the formula: L ab is the length of road ab, t l is the waiting time of the intersection signal light, m is the number of charging times, R is the current available cruising range, t c is the ET charging time, and [x] is the rounding function.

[0136] (2) Establish a battery loss model for pure electric heavy trucks based on temperature data; specifically including the following steps,

[0137] The calculation expression of the actual maximum load capacity of the battery at different temperatures is:

[0138] E tmax = E 20 ·e a(t-20)

[0139] In the formula, E tmax is the maximum load capacity of the pure electric heavy truck battery at different temperatures t; E 20 is the battery capacity of the pure electric heavy truck when the ambient temperature is 20 degrees; a is the fitting parameter for the influence of temperature on the battery capacity of the pure electric heavy truck; the existing technology models the battery loss model relatively simply, only considering the influence of seasonal temperature, etc., introducing single-variable linearization processing, and not comprehensively considering the influence of factors such as discharge depth, ambient temperature, vehicle driving mileage, and charging power on battery loss. The present invention considers that the battery capacity undergoes irreversible attenuation, that is, battery aging, after multiple charge and discharge cycles, which affects the maximum load capacity of the battery next time. Based on the construction of the Arrhenius model, considering the discharge depth, ambient temperature, charging power, and vehicle driving mileage, the maximum load capacity model of the pure electric heavy truck battery is updated, and the expression is:

[0140]

[0141] Where: E r,tmax is the maximum load capacity of the ET battery after the r-th charge; L is the loss degree of the lithium iron phosphate battery; E a is the activation energy of the lithium iron phosphate battery reaction; R is the ideal gas constant; T is the actual ambient temperature where ET is located, T 0 is the reference ambient temperature, taken as 20 °C; P is the actual charging power of the pure electric heavy truck, P 0 is the reference charging power of the pure electric heavy truck; DoD is the actual discharge depth of the pure electric heavy truck, and the value range is 0 - 1, DoD 0 is the reference discharge depth, usually taken as 0.5; S represents the actual driving mileage of the pure electric heavy truck. The longer the driving mileage, the more charge-discharge cycles the battery may experience, and the greater the battery loss; S 0 is the reference driving mileage, taken as the driving mileage of the pure electric heavy truck under standard test conditions; α, β, and γ are the influence coefficients of charging power, discharge depth, and driving mileage on battery loss respectively.

[0142] (3) Establish a travel power consumption function model for the pure electric heavy truck based on load data; specifically including the following steps,

[0143] First, calculate the mechanical power P e of the pure electric heavy truck when driving. During the uniform driving process of the pure electric heavy truck, there are three types of resistances, namely air resistance, rolling resistance, and frictional resistance. The expression of the mechanical power of the pure electric heavy truck when driving is as follows:

[0144] P e = k 1 v 3 + k 2 (M + m)gv + μ(M + m)gv

[0145] Where: v is the driving speed of the pure electric heavy truck; k 1 is the air resistance coefficient; k 2 is the rolling resistance coefficient; μ is the frictional resistance coefficient; g is the acceleration due to gravity; m and M are the weight of the pure electric heavy truck itself and the load respectively;

[0146] Then the electric power P m of the pure electric heavy truck is:

[0147]

[0148] Where: η e is the efficiency of converting electrical energy into mechanical energy when the pure electric heavy truck is driving;

[0149] According to the obtained mechanical energy efficiency, based on the influence of temperature on the battery discharge efficiency, find the total power consumption Q e of the pure electric heavy truck:

[0150]

[0151] Where: U is the voltage of the battery of the pure electric heavy truck, generally taking 800V - 1000V; t is the driving time of the pure electric heavy truck; η b is the discharge efficiency of the battery at the ambient temperature T; a, b, c are constants determined according to different battery materials and structures; here, the data of a certain lithium iron phosphate lithium-ion battery in the range of -20°C to 60°C are taken: a = -0.0005, b = 0.04, c = 0.9

[0152] After determining the battery discharge efficiency by least squares fitting, considering the load and battery loss comprehensively, the mileage power consumption of the pure electric heavy truck is:

[0153]

[0154] Where d is the driving distance.

[0155] (4) Establish a travel power consumption model for the pure electric heavy truck based on temperature data; specifically including the following steps,

[0156] The change of ambient temperature will affect the use of the air conditioner. The temperature set by the on-vehicle air conditioner is different in different seasons. The adjustment of the pure electric heavy truck corresponding to the temperature and humidity inside and outside the vehicle will affect the power consumption of the on-vehicle air conditioner, thus directly changing the charging demand of the electric vehicle. Most of the existing technologies for dealing with the power consumption of the on-vehicle air conditioner do not consider the on-off state of the air conditioner, and only convert the power consumption of the air conditioner into the mileage power consumption of the pure electric heavy truck. Therefore, on the original basis, an air conditioner opening probability factor k is introduced to describe the influence of the actual driving on-vehicle air conditioner power consumption on the mileage power consumption of the pure electric heavy truck.

[0157] Construct the air conditioner opening probability factor model k as:

[0158]

[0159] Where T 1 and T 2 are the upper and lower temperature thresholds for setting the air conditioner to turn on. The power consumption of the on-vehicle air conditioner will change due to the daily temperature change. In order to accurately describe the relationship between the on-vehicle air conditioner power and the daily temperature during the ET driving process, a piecewise function is used to represent the relationship between the on-vehicle air conditioner power and the daily temperature, and the expression is:

[0160]

[0161] Where: P 1 is the running power of the on-vehicle air conditioner of the pure electric heavy truck at the temperature T; T H and T LThey are the temperature threshold for the air conditioner to start running at low power and the temperature threshold for the air conditioner to maintain the basic minimum power; P 1max and P 1min are the maximum power and the minimum power for the operation of the air conditioner respectively;

[0162] Then the expression for the air conditioner power consumption per unit kilometer of the battery electric heavy truck is:

[0163]

[0164] In the formula: Q t is the air conditioner power consumption per unit kilometer of the battery electric heavy truck; P 1 is the operating power of the on-vehicle air conditioner of the battery electric heavy truck at temperature T; d is the driving mileage of the battery electric heavy truck; v is the driving speed of the battery electric heavy truck.

[0165] (5) Establish a power consumption model for the trips of battery electric heavy trucks based on the additional losses in the V2G mode; specifically, it includes the following steps,

[0166] The power consumption for participating in V2G is:

[0167]

[0168] In the formula: Q V2G is the power consumption of the battery electric heavy truck for participating in V2G; P i,t is the discharge power of the battery of the i-th battery electric heavy truck at time t; k V2G is the factor for the battery electric heavy truck user to participate in the V2G response, and Δt represents the time interval.

[0169] (6) Analyze the travel characteristics of battery electric heavy trucks and establish a charging probability and V2G response probability model for battery electric heavy trucks; specifically, it includes the following steps,

[0170] The linear combination of Gaussian distributions can depict the characteristics of complex data distributions. The Gaussian mixture model is used to describe the travel time period distribution of battery electric heavy trucks to characterize the differences in driving habits of different types of battery electric heavy trucks; assuming that the battery electric heavy trucks have travel peaks at 8-12 am and 18-21 pm respectively, the expression is as follows:

[0171]

[0172] In the formula: x represents the travel time, and Π is the pi;

[0173] The SOC of the battery electric heavy truck is a random variable with a Gaussian distribution, and its probability density function is:

[0174]

[0175] The power consumption of the battery electric heavy truck increases non-linearly with the driving distance, and the SOC of the battery electric heavy truck at time t is: Qt = Q 0 -Q e Δl;

[0176] Where: Q 0 is the initial charge quantity when the pure electric heavy truck just starts; Δl is the driving distance of the pure electric heavy truck from 0 to time t;

[0177] The charging behavior of the pure electric heavy truck is mainly affected by the SOC value. The lower the SOC, the greater the probability that the pure electric heavy truck chooses to charge. At the same time, the pricing of the charging station also affects the charging of the pure electric heavy truck. The charging probability of the pure electric heavy truck based on the electricity price is:

[0178]

[0179] Where: Q L is the lower threshold of the initial charge quantity of the pure electric heavy truck affecting charging, Q H is the upper threshold of the initial charge quantity of the pure electric heavy truck affecting charging; Q t is the initial charge quantity of the pure electric heavy truck at time t; k is the electricity price sensitivity coefficient; p a is the average charging electricity price, p is the current charging electricity price;

[0180] At the same time, participating in V2G requires that the SOC of the pure electric heavy truck is above the set threshold. According to the initial SOC value Q is of the i-th pure electric heavy truck connected to the charging pile and the expected value Q ia at the end of charging, calculate the charging time T i and the shortest charging time T i,d under ideal conditions:

[0181]

[0182] Where: Q is is the starting state of charge of the pure electric heavy truck battery during charging; Q ia is the expected state of charge at the end of charging of the pure electric heavy truck; η i is the charging efficiency of the pure electric heavy truck; P i is the charging power of the pure electric heavy truck;

[0183] The V2G response probability formula based on the user response willingness is:

[0184]

[0185] Where: B is the probability that the pure electric heavy truck user responds to V2G; T i is the charging time; T i,d is the shortest charging time under ideal conditions; R is the regional response coefficient; E i and Q iare the battery capacity and the remaining charge of the battery electric heavy truck, respectively; Q th is the battery capacity threshold for battery electric heavy truck users to participate in V2G.

[0186] (7) Predict the charging time and response ability of battery electric heavy trucks; specifically including the following steps,

[0187] According to the grid response period T dr and the grid connection period T chr，i of the i-th battery electric heavy truck, calculate the demand response period T dr，i that the i-th battery electric heavy truck can participate in as:

[0188] T dr,i = T dr ∩T chr,i

[0189] If the charging time of the battery electric heavy truck overlaps with the grid demand response time, that is, this battery electric heavy truck has the response ability (the present invention predicts the dispatchable ability of the charging station, providing basic data for the master-slave game dispatch strategy); first, set the road network parameters and the DR period, generate the departure time and initial SOC of each battery electric heavy truck through Monte Carlo sampling, read the origin-destination information from the matrix D; use the dijkstra algorithm to plan the driving route, calculate the road travel time according to the speed-flow model; simulate the driving of the battery electric heavy truck, judge whether there is a charging demand, if so, select the nearest charging station to drive in and record the load information, and report the demand response ability of the battery electric heavy truck.

[0190] (8) Establish an upper-layer optimization model for the charging station revenue based on V2G response; specifically including the following steps,

[0191] Considering the power sales revenue, power purchase cost and compensation revenue obtained from participating in V2G dispatch of the charging station, with the maximum charging station revenue as the objective function. The charging station objective function is expressed as follows:

[0192] Charging station revenue = power sales revenue - power purchase cost + compensation revenue obtained by considering participating in V2G dispatch

[0193]

[0194] In the formula: max F 2 is the profit of the battery electric heavy truck charging station; P i,c,t is the charging power of the battery electric heavy truck at time t; P i,d,t is the discharging power of the battery electric heavy truck at time t, N is the total number of battery electric heavy trucks participating in charging and discharging, T is the total charging and discharging time of the i-th battery electric heavy truck; η c and η d are the charging and discharging efficiencies of the battery of the battery electric heavy truck, respectively; M i,c,t and Mi,d,t is the charge and discharge decision variable: and M i,c,t ·M i,d,t = 0; C ch is the charging price of the charging station at time t; C dt is the discharging price of the charging station at time t; C buy and C sell are the electricity purchase price from the power grid and the electricity selling price to the power grid of the charging station; C V2G is the reward price obtained by the charging station for mobilizing pure electric heavy trucks to participate in V2G;

[0195] The constraint conditions are:

[0196] 1. Maximum charge and discharge power constraint of the charging station: The charge and discharge power of the pure electric heavy truck charging station is restricted by the power grid load:

[0197]

[0198] In the formula: P i,c,nmax , P i,d,nmax respectively represent the maximum charge and discharge powers of the charging station;

[0199] 2. Customized charge and discharge prices of the charging station: The charge and discharge prices of the pure electric heavy truck charging station are restricted by the power grid:

[0200]

[0201] In the formula, and are the lower threshold and upper threshold of the charging price set by the charging station; and are the lower threshold and upper threshold of the discharging price set by the charging station.

[0202] (9) Establish a lower-layer optimization model for the charging cost of pure electric heavy trucks based on time-window penalty, power grid load variance, and V2G response; specifically including the following steps,

[0203] Different from traditional EVs, the main purpose of pure electric heavy trucks is to transport and deliver goods to obtain benefits. During the delivery process, in addition to considering the benefits obtained from participating in V2G response, they are also restricted by time windows. On the one hand, if participating in V2G causes the delivery time to exceed the time-window constraint, the delivery income of pure electric heavy trucks will be reduced; on the other hand, delivering goods in advance will also reduce the delivery income of pure electric heavy trucks. Therefore, pure electric heavy trucks can obtain benefits by participating in V2G and avoid the situation of delivering goods in advance, so as to maximize benefits. The objective function on the user side of pure electric heavy trucks is the user cost;

[0204] User cost = electricity purchase cost + loss cost - income from participating in V2G, and the expression is as follows:

[0205]

[0206] In the formula: min F 1 is the total cost of ET users; P i,c,t is the charging power of ET users at time t; C ch is the charging price of the charging station at time t; C b is the basic cost of the ET battery; E B is the rated capacity of the ET battery; L is the number of cycles of the ET battery; DoD is the depth of discharge of the ET battery; P i,d,t is the discharge power of ET users at time t; C dt is the discharge price of the charging station at time t; η d is the discharge efficiency of the ET battery; C d is the soft time window penalty cost of ET, where T is the driving time of the i-th ET, T e and T l are the upper and lower thresholds of the early arrival and late arrival of ET delivery respectively; a and b are the penalty costs per unit time for early arrival and late arrival respectively;

[0207] The constraint conditions are:

[0208] 1. Charging and discharging power constraint: The charging and discharging power of ET is restricted by the charging station and its own battery attributes:

[0209]

[0210] In the formula: P i,c,tmax and P i,d,tmax are the maximum charging and discharging powers of the i-th ET respectively;

[0211] 2. Remaining power constraint: The participation of ET in response is restricted by its own power:

[0212] 0.35S i,t,p ≤S i,t ≤S i,t,p

[0213] In the formula: S i,t,p is the rated capacity of the ET battery;

[0214] 3. Grid load variance constraint:

[0215] In order to reduce the impact on the power grid caused by pure electric heavy trucks during response, the grid load variance is used as a constraint condition:

[0216]

[0217] In the formula: The whole day is divided into i time steps, L iThe grid load when the step size is \(i\); \(L\) 0 The initial load of the power grid when the step size is \(i\); \(\sigma\) 2 The maximum variance allowed for the grid load setting, \(\Delta t\) represents the time interval The average load of the power grid when the step size is \(i\).

[0218] (10) Solve the model using the master - slave game method; specifically including the following steps

[0219] Establish a two - layer optimization master - slave game model for pure - electric heavy trucks, and set the game convergence error;

[0220] Generate the initial charging and discharging electricity prices, and set the number of iterations to 0;

[0221] Predict the ET response willingness through the response ability prediction of the pure - electric heavy - truck cluster, and update the ET corresponding cluster;

[0222] The upper - layer charging station obtains the charging station revenue and the charging and discharging power of the pure - electric heavy truck according to the charging corresponding cluster of the lower - layer pure - electric heavy truck; if the charging station revenue does not meet the accuracy, perform the next iteration;

[0223] When the charging station revenue value meets the accuracy requirement, the iteration ends and the equilibrium solution is obtained.

[0224] Conduct a simulation experiment on the technical solution provided by the present invention.

[0225] The simulation environment is based on the IEEE69 - node distribution network, and the network topology is shown in Figure 2 . The parameter settings of each entity are as follows: the base voltage is 12.66 kV, the base power is 10 MW. The number of electric vehicles is 5000, the charging and discharging electric power is 160 W, the battery charging and discharging efficiency is 95%, and the battery capacity is 320 kWh; the battery charging and discharging loss coefficients are 0.01 yuan / kWh and 0.1 yuan / kWh respectively, the upper and lower limits of SOC are 0.95 and 0.1 respectively, the expected SOC is 0.85, and the upper and lower thresholds for triggering the charging selection are 0.5 and 0.15 respectively; generate information such as the travel time period, D matrix, and initial SOC of the pure - electric heavy truck through the Monte Carlo method to simulate the driving of the pure - electric heavy truck. The demand response time period is charging: 1:00 - 5:00; discharging: 10:00 - 13:00, 19:00 - 21:00, and the compensation for the discharging demand response time period is 0.8 yuan / (kW·h). The grid load prediction is as shown in Figure 3 During the period from 09:00 to 12:00 and from 16:00 to 19:00, the load of the pure - electric heavy - truck cluster increases the grid load.

[0226] The optimized result of the charging and discharging electricity price of the charging station is as shown in Figure 4As shown in the figure. During the periods of 08:00 - 12:00 and 18:00 - 21:00, pure electric heavy truck users are encouraged to participate in V2G for discharging, and during the period of 22:00 - 6:00, pure electric heavy truck users are encouraged to charge, participating in the power grid's "peak shaving and valley filling" to reduce load fluctuations.

[0227] The charging and discharging powers of ET before and after the master-slave game optimization are as Figure 5 shown in the figure. By optimizing the charging station price and the charging and discharging powers of pure electric heavy trucks, the load of pure electric heavy trucks can be transferred in time and space, reducing peak loads and increasing valley loads, making the overall load distribution more balanced, thereby improving the stability and economy of the power system.

[0228] In each embodiment, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial control computers, PCs, smart phones, handheld single machines, floor-standing single machines, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memories, calculators, and controllers, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, the World Wide Web, etc. Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be assigned to different functional units and modules according to needs, 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. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0229] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented.

[0230] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for optimizing the electricity price of charging stations and the charging and discharging power of pure electric heavy trucks, characterized in that The following steps are involved: (1) Establish and describe the road topology of the pure electric heavy truck cluster driving network; (2) Establish a battery loss model for pure electric heavy trucks based on temperature data; (3) Establish a travel power consumption function model for pure electric heavy trucks based on load data; (4) Establish a travel power consumption model for pure electric heavy trucks based on temperature data; (5) Establish a power consumption model for pure electric heavy trucks based on the additional losses in V2G mode; (6) Analyze the travel characteristics of pure electric heavy trucks and establish a charging probability and V2G response probability model for pure electric heavy trucks; (7) Predicting charging time and responsiveness of pure electric heavy trucks; (8) Establish an upper-level optimization model for charging station revenue based on V2G response; (9) Establish a lower-level optimization model for charging costs of pure electric heavy trucks based on time window penalty, grid load variance, and V2G response; (10) The master-slave game method is used to solve the model.

2. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (1), the establishment of the road topology of the pure electric heavy truck cluster driving network includes the following steps: Assuming that the road for pure electric heavy trucks is bidirectional, the topological structure is described by graph theory, where each node represents an intersection and each edge represents a road section. Then, each edge is assigned a value to accurately describe the actual connection and traffic flow between roads. The topological structure of the road is described in the form of G(V, E), where V represents the set of nodes in the topological structure, including endpoints and intersections, expressed as {1, 2, ..., n}, and E represents the set of road segments in the road network; the distance relationship between points a and b in the road network set G is described by the adjacency matrix D, and the distance between a and b is described by d ab To indicate that ab is the element of matrix D, d ab As shown in the following formula, In the formula, r ab is the section between nodes a and b, l ab is the length of the road segment between a and b; inf means that the road segment formed by nodes a and b is not directly connected; The travel time is selected as the road resistance model for model establishment, and the speed-flow model is introduced. The expression of the pure electric heavy truck driving speed V is: Where: v0 is the zero-flow speed of a pure electric heavy truck on the road; c is the road capacity during this time period; q is the road traffic volume per unit time; p, d, n are the road grade adaptive coefficients; Road resistance model T of pure electric heavy truck considering traffic light time and charging times ab The expression is: Where: L ab is the length of road ab, t l is the waiting time for the traffic light at the intersection, m is the number of charging times, R is the current range, t c is the ET charging time, and [x] is the rounding function.

3. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (2), establishing a battery loss model for a pure electric heavy truck based on temperature data includes the following steps: The calculation expression of the actual maximum capacity of the battery at different temperatures is: AND tmax =And 20 ·And a(t-20) In the formula, E tmax is the maximum load capacity of the battery of a pure electric heavy truck at different temperatures t; E 20 is the battery capacity of a pure electric heavy truck when the ambient temperature is 20 degrees; a is the fitting parameter of the effect of temperature on the battery capacity of a pure electric heavy truck; considering that the battery capacity will be irreversibly attenuated after multiple charge and discharge, that is, battery aging, and will affect the next maximum load capacity of the battery, the Arrhenius model is constructed as the basis, considering the depth of discharge as well as the ambient temperature, charging power and vehicle mileage, and the maximum load capacity model of the pure electric heavy truck battery is updated, and the expression is: Where: E r,tmax is the maximum load of the ET battery after the rth charge; L is the loss degree of the lithium iron phosphate battery; E a is the activation energy of the lithium iron phosphate battery reaction; R is the ideal gas constant; T is the actual ambient temperature of ET, and T0 is the reference ambient temperature; P is the actual charging power of the pure electric heavy-duty truck, and P0 is the reference charging power of the pure electric heavy-duty truck; DoD is the actual discharge depth of the pure electric heavy-duty truck, and DoD0 is the reference discharge depth; S represents the actual mileage of the pure electric heavy-duty truck; S0 is the reference mileage, which is the mileage of the pure electric heavy-duty truck under standard test conditions; α, β, γ are the influence coefficients of charging power, discharge depth and mileage on battery loss, respectively.

4. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (3), establishing a travel power consumption function model of a pure electric heavy truck based on load data includes the following steps: First, calculate the mechanical power P of the pure electric heavy truck when driving e There are three kinds of resistance when a pure electric heavy truck is driving at a constant speed, namely air resistance, rolling resistance and friction resistance. The mechanical power expression of a pure electric heavy truck when driving is as follows: P e =k1v 3 +k2(M+m)gv+μ(M+m)gv Where: v is the speed of the pure electric heavy truck; k1 is the air resistance coefficient; k2 is the rolling resistance coefficient; μ is the friction resistance coefficient; g is the acceleration of gravity; m and M are the weight and load of the pure electric heavy truck respectively; Then the electric power P of a pure electric heavy truck is m for: Where: η e It is the efficiency of converting electrical energy into mechanical energy when a pure electric heavy truck is running; According to the efficiency of mechanical energy, based on the influence of temperature on battery discharge efficiency, the total power consumption Q of pure electric heavy truck is calculated. e : Where: U is the voltage of the battery of the pure electric heavy truck; t is the driving time of the pure electric heavy truck; η b is the discharge efficiency of the battery at ambient temperature T; a, b, c are constants determined according to different battery materials and structures; Therefore, the mileage power consumption of pure electric heavy trucks is: Where d is the driving distance.

5. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (4), establishing a travel power consumption model of a pure electric heavy truck based on temperature data includes the following steps: The air conditioner on probability factor model k is constructed as: In the formula, T1 and T2 are the upper and lower temperature thresholds for setting the air conditioner to be turned on. A piecewise function is used to represent the relationship between the vehicle air conditioner power and the daytime temperature. The expression is: Where: P1 is the operating power of the pure electric heavy-duty truck air conditioner at temperature T; T H and T L are the temperature threshold for the air conditioner to start low-power operation and the temperature threshold for the air conditioner to maintain basic minimum power; P 1max and P 1min They are the maximum power and minimum power of the air conditioner respectively; The power consumption of air conditioning per kilometer of pure electric heavy truck is expressed as: Where: Q t is the air conditioning power consumption per kilometer of pure electric heavy-duty truck driving; P1 is the operating power of the pure electric heavy-duty truck's onboard air conditioning at temperature T; d is the pure electric heavy-duty truck's mileage; v is the pure electric heavy-duty truck's driving speed.

6. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (5), establishing a pure electric heavy truck travel power consumption model based on the additional loss in the V2G mode includes the following steps: The power consumption of participating in V2G is: Where: Q V2G P is the power consumption of pure electric heavy trucks participating in V2G; i,t is the discharge power of the battery of the i-th pure electric heavy truck at time t; k V2G is the factor of pure electric heavy truck users participating in V2G response; Δt represents the time interval.

7. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 1 is characterized by: In step (6), establishing the charging probability and V2G response probability model of the pure electric heavy truck includes the following steps: The Gaussian mixture model is used to describe the travel time distribution of pure electric heavy trucks, which is used to characterize the differences in driving habits of different types of pure electric heavy trucks. Assuming that pure electric heavy trucks have travel peaks from 8 to 12 am and from 18 to 21 pm, the expression is as follows: Where: x represents the travel time; Π is the pi; The SOC of a pure electric heavy truck is a random variable with a Gaussian distribution, and its probability density function is: Where: π is the circumference of a circle; The power consumption of a pure electric heavy truck increases nonlinearly with the driving distance. The SOC of a pure electric heavy truck at time t is: Q t =Q0-Q e Δl Where: Q0 is the initial charge of the pure electric heavy truck when it is just started; Δl is the driving distance of the pure electric heavy truck from time 0 to t; The lower the SOC, the greater the probability that a pure electric heavy truck will choose to charge. At the same time, the pricing of charging stations also has an impact on the charging of pure electric heavy trucks. The probability of pure electric heavy trucks charging based on electricity prices is: Where: Q L Q is the lower threshold of the initial charge of a pure electric heavy truck that affects charging. H The upper threshold of the initial charge of a pure electric heavy truck that affects charging; Q t is the initial charge of the pure electric heavy truck at time t; k is the electricity price sensitivity coefficient; p a is the average charging electricity price, p is the current charging electricity price; At the same time, participating in V2G requires that the SOC of pure electric heavy trucks is above the set threshold. According to the initial SOC value Q of the i-th pure electric heavy truck connected to the charging pile is and the expected value Q at the end of charging ia Calculate the charging time T i And the ideal minimum charging time T i,d : Where: Q is The initial state of charge when charging the battery of a pure electric heavy truck; Q ia The expected state of charge of the pure electric heavy truck when charging is completed; η i is the charging efficiency of pure electric heavy truck; P i The charging power of a pure electric heavy truck; The V2G response probability formula based on user response willingness is: Where: B is the probability of pure electric heavy truck users responding to V2G; T i is the charging time; T i,d The shortest charging time under ideal conditions; R is the regional response coefficient; E i and Q i They are the battery capacity and remaining charge of the pure electric heavy truck; Q th The battery capacity threshold for pure electric heavy truck users to participate in V2G.

8. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 7 is characterized by: In step (7), predicting the charging time and response capability of a pure electric heavy truck includes the following steps: According to the power grid response period T dr and the grid-connected period T of the i-th pure electric heavy truck chr，i , calculate the time period T during which the i-th pure electric heavy truck can participate in the demand response dr，i for: T dr,i =T dr ∩T chr,i If the charging time of a pure electric heavy truck overlaps with the grid demand response time, The pure electric heavy truck has responsiveness. First, the road network parameters and DR period are set, the travel time and initial SOC of each pure electric heavy truck are generated through Monte Carlo sampling, and the matrix D is read to obtain the starting and ending point information. The driving route is planned using the Dijkstra algorithm, and the road travel time is calculated according to the speed-flow model. Simulate the driving of a pure electric heavy truck to determine whether there is a need for charging. If so, choose to drive into the nearest charging station and record the load information, and report the demand response capability of the pure electric heavy truck.

9. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 8, characterized in that: In step (8), establishing the upper-level optimization model of charging station revenue based on V2G response includes the following steps: The objective function of the charging station is the charging station revenue, which is expressed as follows: Charging station revenue = electricity sales revenue - electricity purchase cost + compensation revenue from participating in V2G dispatch Where: max F2 is the profit of pure electric heavy truck charging station; P i,c,t P is the charging power of the pure electric heavy truck at time t; i,d,t is the discharge power of the pure electric heavy truck at time t; N is the total number of pure electric heavy trucks involved in charging and discharging, and T is the total charging and discharging time of the i-th pure electric heavy truck; η c and η d are the efficiency of charging and discharging of pure electric heavy-duty truck batteries; M i,c,t With M i,d,t is the charge and discharge decision variable: And M i,c,t ·M i,d,t =0; C ch is the charging price at the charging station at time t; C dt is the discharge price of the charging station at time t; C buy and C sell The price of electricity purchased from the power grid and sold to the power grid by the charging station; C V2G The reward price for mobilizing pure electric heavy trucks to participate in V2G for charging stations; The constraints are:

1. Maximum charging and discharging power constraints of charging stations: The charging and discharging power of pure electric heavy truck charging stations is subject to grid load constraints: Where: P i,c,nmax , P i,d,nmax Respectively represent the maximum charging and discharging power of the charging station; 2. Customized charging and discharging prices at charging stations: The charging and discharging prices of pure electric heavy truck charging stations are subject to grid constraints: In the formula, and Lower and upper thresholds for charging prices set for charging stations; and Establish lower and upper thresholds for discharge prices for charging stations.

10. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 9, characterized in that: In step (9), establishing a lower-level optimization model for charging cost of pure electric heavy trucks based on time window penalty, grid load variance, and V2G response includes the following steps: The objective function of the pure electric heavy truck user side is the user cost; User cost = electricity purchase cost + loss cost - benefits of participating in V2G, expressed as follows: Where: min F1 is the total cost of ET users; P i,c,t The charging power of ET user at time t; C ch is the charging price at the charging station at time t; C b is the basic cost of ET battery; E B is the rated capacity of the ET battery; L is the number of cycles of the ET battery; DoD is the depth of discharge of the ET battery; P i,d,t is the discharge power of ET user at time t; C dt The discharge price of the charging station at time t; η d is the ET battery discharge efficiency; C d is the ET soft time window penalty cost, Where T is the travel time of the i-th ET, T e With T l are the upper and lower thresholds for early and late ET delivery, respectively; a and b are the penalty costs per unit time for early and late delivery, respectively; The constraints are:

1. Charge and discharge power constraints: ET charge and discharge power is constrained by the charging station and its own battery properties: Where: P i,c,tmax and P i,d,tmax are the maximum charging and discharging powers of the i-th ET respectively; 2. Remaining power constraint: ET participation in response is constrained by its own power: 0.35S i,t,p ≤S i,t ≤S i,t,p Where: S i,t,p is the rated capacity of the ET battery; 3. Grid load variance constraint: In order to reduce the impact of pure electric heavy trucks on the power grid when responding, the grid load variance is used as a constraint condition: Where: The whole day is divided into i steps, L i is the grid load when the step length is i; L0 is the initial load of the grid when the step length is i; σ 2 Set the maximum allowable variance for the grid load; Δt represents the time interval, is the average load of the power grid when the step size is i.

11. The method for optimizing the charging station electricity price and the charging and discharging power of pure electric heavy trucks according to claim 10, characterized in that: In step (10), solving the model using the master-slave game method includes the following steps: Establish a dual-layer optimized master-slave game model for pure electric heavy trucks and set the game convergence error; Generate the initial charging and discharging electricity price and set the number of iterations to 0; Calculate ET response intention through prediction of pure electric heavy truck cluster response capability and update ET corresponding cluster; The upper-level charging station calculates the charging station revenue and the charging and discharging power of the pure electric heavy truck according to the corresponding cluster of the lower-level pure electric heavy truck charging; if the charging station revenue does not meet the accuracy requirements, the next iteration is performed; When the charging station revenue value meets the accuracy requirement, the iteration ends and the equilibrium solution is obtained.

Images