Electric vehicle charging behavior guiding method considering electricity price of charging station under coupling network

Abstract

The invention discloses an EV charging behavior guiding method under a traffic coupling network, and the method comprises the steps: building a grading road model for a power grid traffic coupling network, and simulating the charging demand distribution condition of an EV through considering the charging behaviors of electric vehicles with different demands according to EV unit mileage models under different road grades in combination with Monte Carlo simulation; according to the method, the minimum comprehensive cost is taken as a target function, different charging station electricity prices are set, an improved Floyd algorithm is adopted to solve the shortest charging path of the EV, and the optimal distribution condition of the EV is obtained according to the shortest path, so that the power grid voltage can work in an allowable fluctuation range, and the feasibility and effectiveness of the scheme are ensured. According to the method, the charging coincidence of the EV in the coupling network is tracked, so that the influence of the charging flexibility of the EV on the voltage of the power grid is relieved to a great extent, the stable operation of the power grid is ensured, and the impact on the power grid caused by sudden overload is reduced.

Description

Technical Field

[0001] The invention belongs to the field of electric vehicle charging and discharging, and in particular relates to a method for guiding electric vehicle charging behavior in a coupled network taking into account the electricity price of a charging station. Background Art

[0002] The power grid and transportation network are key to maintaining social operations. After the two grids are coupled, they have become one of the main sources of carbon emissions in the country. With the access of a high proportion of new energy, the balance of the power grid is affected in both time and space. From the perspective of time, its volatility and uncertainty affect the stability of the power system; from the perspective of space, the access of a large number of distributed new energy sources will cause power reflux, overvoltage and other unstable situations in the power system. In order to solve the above unstable situations, the power system urgently needs to develop flexible resources.

[0003] As a hub connecting the power grid and the transportation network, the charging characteristics of electric vehicles (EVs) also affect the operation of the power grid. The main energy of EVs comes from the power grid, and compared with fuel vehicles, they have the characteristics of cleanliness and mobile energy storage. EVs participate in the operation of the power grid as a flexible resource, which can improve the overall operation efficiency of the power grid. When EVs exist as loads in the power system, the uncertainty of EV users' charging behavior has an impact on the power grid and affects the stable operation of the power grid. In addition, with the widespread application of EVs, the congestion of the transportation system will affect the charging behavior of EVs, which in turn affects the operation of the transportation network. Since the installation location of public charging equipment is fixed and basically installed along the roads of the transportation network, the choice of EV charging time, route and location will affect the distribution of traffic flow in the transportation network, thereby affecting the operation of the transportation system.

[0004] At present, the methods for managing EV charging and discharging to constrain EV users' charging behavior mainly include analyzing the charging behavior through Monte Carlo prediction of EV charging load, introducing regret theory, simulating users' travel decisions, and considering users' subjective willingness to study charging behavior preferences through charging time characteristics, charging power characteristics, etc. For the coupled network of power grid and transportation network, in addition to the influence of the characteristics of the coupled network itself on EV charging behavior, EV charging behavior is constrained by time-of-use electricity prices, and different charging electricity prices will affect users' charging decisions. Including but not limited to setting up zone electricity prices, regulating charging behavior according to charging electricity prices of load characteristics; describing the distribution network model through branch flow, and deriving the electricity prices of each charging station according to the adaptive particle swarm algorithm. Therefore, it is necessary to study the impact of EV charging behavior on the coupled network under dual-grid coupling. In addition, in the coupled model, the travel path of electric vehicles should be guided by setting different charging station electricity prices. Summary of the invention

[0005] In order to solve or improve the above problems and further ensure the safe and reliable operation of the power-transportation coupling network, the peak load caused by the static electricity price of charging stations and the charging behavior guidance of electric vehicles under the dynamic electricity prices of different charging stations are comprehensively considered. The influence of factors such as the temporal and spatial distribution characteristics of electric vehicles as loads and the travel characteristics of users are comprehensively considered. Electric vehicles are used as the basis for the coupling of the "vehicle-electricity-road" three networks, and the charging path guidance problem of electric vehicles under the dynamic electricity prices of different charging stations is studied in detail. The present invention provides a charging behavior guidance method for EVs under the dynamic electricity prices of different charging stations. Under the coupling of the "vehicle-electricity-road" three networks, the method establishes an electric vehicle start-end matrix according to the differentiated charging characteristics of various electric vehicles, simulates the distribution of electric vehicle charging demand through Monte Carlo, and on this basis, establishes a method with the objective function of minimizing the comprehensive cost, sets the dynamic electricity prices of different charging stations, and uses the improved Floyd shortest path algorithm to determine the optimal charging path of electric vehicles, thereby obtaining the optimal charging distribution of electric vehicles.

[0006] The technical solution of the present invention is:

[0007] A method for guiding electric vehicle charging behavior in a coupled network taking into account the electricity price of charging stations. The method divides the electric vehicle charging behavior into two stages, including the following steps:

[0008] In the first stage, the parameters of the transportation network and power network are input to establish a dual-grid coupling model and a single EV model. Based on the Monte Carlo simulation of the charging demand distribution of electric vehicles in a single network, a preliminary analysis of the EV charging behavior is conducted. The impact of different parameter characteristics of different types of EVs on the charging load is jointly considered, and the OD start-end matrix is ​​used to simulate the travel characteristics of EVs, thereby obtaining the preliminary distribution of EV charging.

[0009] In the second stage, the objective function is to minimize the comprehensive cost, taking into account the mileage, time and charging cost, and fully considering the constraints of the remaining power. By setting different charging station electricity prices, the Floyd algorithm is used to solve the shortest charging path for EVs. The charging distribution of EVs is re-derived based on the shortest path, so that it does not violate people's travel patterns and the charging does not cause a major impact on the power grid. Finally, based on the real-time system voltage distribution, the solutions that do not meet the voltage indicators are promptly eliminated.

[0010] The above steps guide the charging behavior of the electric vehicle single power consumption model after entering the coupling network. As the Floyd algorithm calculates the shortest path for EV charging, unnecessary energy consumption of EV is reduced. The steps are repeated until the grid voltage equation is satisfied.

[0011] The minimum comprehensive cost under the coupling network model is:

[0012] G=(V,E,L,R),

[0013] V={v i |i=1,2,...,m},

[0014] E={e ij |e i ∈V,e j ∈V,i≠j},

[0015] L = {l ij |l ij ∈E},

[0016] R = {r ij |r ij ∈E},

[0017]

[0018] C EV =(O,D,t0,C p ,C o ,S L )

[0019]

[0020] cap t =cap t-1 -Δl·ε r

[0021] f1=min(α1F1+α2F2+α3F3)

[0022]

[0023] In the formula, V is the set of nodes at intersections of the traffic network; E is the set of sections of the traffic network, which represents the connection relationship between two intersections; L is the set of lengths of sections of the traffic network; R is the set of road grade weights of the traffic network; v i Indicates the road intersection node number; e ij Represents the connection relationship between road intersection node i and road intersection node j; l ij Indicates road e ij The length of r ij Indicates road e ij m is the road grade; m is the number of nodes in the transportation network; Indicates the unit power consumption of EV on each level of road. is the average speed of road section e during time period t; i and j are the starting and ending points of the road respectively; V i,j (t) is the driving speed of the EV on road (i, j) during period t; is the zero flow velocity of road (i, j); C i,j is the traffic capacity of road (i, j); q i,j (t) / C i,j is the saturation of the road in time period t; r, a, b are parameter values ​​of different road types; O is the starting point of the EV driving journey; D is the end point of the EV driving journey; t0 represents the starting time; C p Indicates the battery capacity of the EV; C o represents the initial capacity of EV; S L Indicates the state of charge of the electric vehicle when it meets the charging requirement; α and β are gamma distribution parameters; σ and μ are normal distribution parameters; is the EV traffic volume from node i to node j during period k; is the travel probability between node i and node j. When i=j, it means the probability requirement that EV has no travel demand in period k; Δl is the driving distance of EV in period t; ε r is the unit power consumption; F1 is the mileage cost of EV users; F2 is the time cost of EV driving; F3 is the EV charging cost; α1, α2, α3 are weight coefficients, and the sum of the three is 1; It represents the charging demand variable of EV, which is 1 when the nth EV chooses to charge at charging station k, otherwise it is 0; represents the mileage of the nth EV to the charging station k after it sets out for charging demand; represents the distance of the nth EV from charging station k to the end point D; represents the time taken by the nth EV to charge from the starting point to the charging station k; represents the time it takes for the nth EV to complete charging to the end point D at charging station k; represents the charging time of the nth EV at charging station k; represents the SOC value of the nth EV when it completes charging at charging station k; The power of the nth EV when it arrives at charging station k; M k is the charging electricity price of charging station k, in kW·h / yuan.

[0024] The multiple constraints include: EV driving constraints, distance constraints, charging quantity constraints, node voltage constraints and power flow constraints;

[0025] The EV driving constraints are:

[0026]

[0027] The distance constraints are:

[0028]

[0029] The charging quantity constraint is:

[0030]

[0031] The node voltage constraint is:

[0032] V i min ≤V i ≤V i max

[0033] The power flow constraint is:

[0034]

[0035] In the formula, C p Indicates the battery capacity of the EV; S L Indicates the state of charge of an electric vehicle when it reaches the charging requirement; Indicates the power consumption per unit distance of EV on each level of road; V represents the distance from the location where the EV triggers the charging demand to the destination point D; i min 、V i max is the lower and upper limits of the node voltage at AC node i, P i , Q i is the active power and reactive power given by node i, V i 、V j is the voltage amplitude of nodes i and j, δ ij is the phase angle difference of the voltage between nodes i and j, G ij , B ij is the conductance and susceptance between nodes i and j.

[0036] Beneficial effects of the present invention:

[0037] The present invention proposes a method for guiding EV charging behavior under different charging station electricity prices in a coupled network. This method establishes a hierarchical road model for the power grid traffic coupling network, takes into account the charging behavior of electric vehicles with different demands, and simulates the distribution of EV charging demand based on the EV unit mileage model under different road levels combined with Monte Carlo simulation; in addition, by setting different charging station electricity prices, the Floyd algorithm is used to solve the shortest charging path of EV, and the optimal distribution of EV is obtained based on the shortest path, so that the grid voltage can work within the allowable fluctuation range, effectively solving the fluctuation and impact of EV charging randomness on the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a 29-node transportation network model diagram.

[0039] Figure 2 It is the flow chart of Floyd algorithm.

[0040] Figure 3 It is a flow chart of EV charging demand.

[0041] Figure 4 It is a road section speed diagram of the coupled road network in an embodiment of the present invention.

[0042] Figure 5 This is the EV charging demand distribution diagram before guidance in the embodiment of the present invention.

[0043] Figure 6 It is a distribution diagram of EV charging demand after guidance in an embodiment of the present invention.

[0044] Figure 7 2 is a diagram of a charging station in an embodiment of the present invention.

[0045] Figure 8 It is a 24-hour distribution network node voltage diagram in an embodiment of the present invention. Specific implementation methods

[0047] The following describes in detail the specific implementation of the EV charging behavior guidance method based on the power-traffic coupling network considering different charging station electricity prices of the present invention in conjunction with the accompanying drawings.

[0048] The example used in the present invention includes a 33-node IEEE distribution network, with a system rated voltage of 12.66 kV, a reference power of 100 MVA and a reference voltage of 12.66 kV; and a 29-node traffic network model, which has 49 traffic roads; the EV fast charging power is set to 48 kW, the EV slow charging power is set to 12 kW, and the total number of EVs is set to 100,000, of which private cars, taxis and shared cars account for 25%, 45% and 30% of the total number of EVs respectively, and the speed flow model parameters are set to a1 = 1.726, b1 = 3.15, n = 2; a2 = 2.076, b2 = 2.87, n = 3. The boot method process is as follows:

[0049] 1) Divide the transportation network into functional areas, specifically Figure 1 As shown in the figure, according to the EV single-unit good point model, the speed of the EV in the corresponding road section can be calculated, as shown in the attached figure. Figure 4 As shown in the figure. Through Monte Carlo simulation, we can get the 24-hour charging demand of EV at each node of the distribution network without any constraints. Figure 5 The EV charging demand distribution diagram shows that the charging time is mainly concentrated in the peak load points of the power grid between 8:00 and 10:00 and 18:00 and 20:00. When EVs choose to charge during the above time periods, the load peak of the power grid will be increased, which may seriously cause system voltage collapse.

[0050] 2) By setting different charging station electricity prices, EVs will take economic indicators into consideration when charging, thus avoiding the EV charging load being concentrated at the peak load time of the distribution network. Selecting different charging paths also changes the charging distribution characteristics of EVs in the distribution network. Figure 6 It can be seen from the figure that the distribution of EVs has changed from being concentrated in the peak load period of the distribution network before guidance to being mostly distributed in the low-peak hours of 8:00-10:00 and 13:00-15:00, with a few concentrated in charging at night, which effectively reduces the operating pressure of the power grid, improves the safe and stable operation of the power grid, and enables the distribution network to maintain a dynamic balance. And from the shortest path selection, it can be seen that, as shown in the attached figure, Figure 7 When the EVs in each functional area reach the charging target, they will choose the nearest charging station with the lowest charging fee for charging.

[0051] 3) When coupling the transportation network and the distribution network, the influence on the distribution network voltage is studied by corresponding to the transportation network and the distribution network nodes. Figure 8 It can be seen that after the guidance, the 24-hour voltage of each node in the distribution network did not exceed the limit and was distributed between 0.95pu and 1.05pu, which effectively improved the node voltage quality and improved the safe and stable operation of the system.

[0052] The above-mentioned calculation examples are only preferred examples of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for guiding EV charging behavior under different charging station electricity prices in a coupled network. It is characterized by By considering the grid-traffic coupling network to establish a hierarchical road model, the distribution of EV charging demand is simulated based on the EV unit mileage model under different road levels combined with Monte Carlo simulation; taking the minimum comprehensive cost as the objective function, by setting different charging station electricity prices, the improved Floyd algorithm is used to solve the shortest charging path of EV, and the optimal distribution of EV is obtained based on the shortest path, so that the grid voltage can fluctuate within the allowable range, ensuring the orderliness of EV charging load. The following steps are included: In the first stage, the parameters of the transportation network and power network are input to establish a dual-grid coupling model and a single EV model. Based on the Monte Carlo simulation of the charging demand distribution of electric vehicles in a single network, a preliminary analysis of the EV charging behavior is conducted. The impact of different parameter characteristics of different types of EVs on the charging load is jointly considered, and the OD start-end matrix is ​​used to simulate the travel characteristics of EVs, thereby obtaining the preliminary distribution of EV charging. In the second stage, the objective function is to minimize the comprehensive cost, taking into account the mileage, time and charging cost, and fully considering the constraints of the remaining power. By setting different charging station electricity prices, the Floyd algorithm is used to solve the shortest charging path for EVs. The charging distribution of EVs is re-derived based on the shortest path, so that it does not violate people's travel patterns and the charging does not cause a major impact on the power grid. Finally, based on the real-time system voltage distribution, the solutions that do not meet the voltage indicators are promptly eliminated. The above steps guide the charging behavior of the electric vehicle single power consumption model after entering the coupling network. As the Floyd algorithm calculates the shortest path for EV charging, unnecessary energy consumption of EV is reduced. The steps are repeated until the grid voltage equation is satisfied.

2. The EV charging behavior guidance method considering different charging station electricity prices in a coupled network according to claim 1, characterized in that: The minimum comprehensive cost form under the coupled network model is: G=(V,E,L,R), V={v i |i=1,2,...,m}, And={and ij |and i ∈V,e j ∈V,i≠j}, L={l ij |l ij ∈E}, R={r ij |r ij ∈E}, C EV =(O,D,t0,C p ,C o ,S L ) cap t =cap t-1 -Δl·ε r f1=min(α1F1+α2F2+α3F3) In the formula, V is the set of nodes at intersections of the traffic network; E is the set of sections of the traffic network, which represents the connection relationship between two intersections; L is the set of lengths of sections of the traffic network; R is the set of road grade weights of the traffic network; v i Indicates the road intersection node number; e ij Represents the connection relationship between road intersection node i and road intersection node j; l ij Indicates road e ij The length of r ij Indicates road e ij m is the road grade; m is the number of nodes in the transportation network; Indicates the unit power consumption of EV on each level of road. is the average speed of road section e during time period t; i and j are the starting and ending points of the road respectively; V i,j (t) is the driving speed of the EV on road (i, j) during period t; is the zero flow velocity of road (i, j); C i,j is the traffic capacity of road (i, j); q i,j (t) / C i,j is the saturation of the road in time period t; r, a, b are parameter values ​​of different road types; O is the starting point of the EV driving journey; D is the end point of the EV driving journey; t0 represents the starting time; C p Indicates the battery capacity of the EV; C o represents the initial capacity of EV; S L Indicates the state of charge of the electric vehicle when it meets the charging requirement; α and β are gamma distribution parameters; σ and μ are normal distribution parameters; is the EV traffic volume from node i to node j during period k; is the travel probability between node i and node j. When i=j, it means the probability requirement that EV has no travel demand in period k; Δl is the driving distance of EV in period t; r is the unit power consumption; F1 is the mileage cost of EV users; F2 is the time cost of EV driving; F3 is the EV charging cost; α1, α2, α3 are weight coefficients, and the sum of the three is 1; It represents the charging demand variable of EV, which is 1 when the nth EV chooses to charge at charging station k, otherwise it is 0; represents the mileage of the nth EV to the charging station k after it sets out for charging demand; represents the distance of the nth EV from charging station k to the end point D; represents the time taken by the nth EV to charge from the starting point to the charging station k; represents the time it takes for the nth EV to complete charging to the end point D at charging station k; represents the charging time of the nth EV at charging station k; represents the SOC value of the nth EV when it completes charging at charging station k; The power of the nth EV when it arrives at charging station k; M k is the charging electricity price of charging station k, in kW·h / yuan.

3. The EV charging behavior guidance method considering different charging station electricity prices in a coupled network according to claim 1, characterized in that: The EV driving constraints: In the formula, C p Indicates the battery capacity of the EV; S L Indicates the state of charge of an electric vehicle when it reaches the charging requirement; Indicates the power consumption per unit distance of EV on each level of road.

4. The EV charging behavior guidance method considering different charging station electricity prices in a coupled network according to claim 1, characterized in that: The distance constraints are: In the formula, It represents the distance from the location where the EV triggers the charging demand to the destination point D.

5. The EV charging behavior guidance method considering different charging station electricity prices in a coupled network according to claim 1, characterized in that: The charging quantity constraint is:

6. The EV charging behavior guidance method considering different charging station electricity prices in a coupled network according to claim 1, characterized in that: The node voltage constraint is: In i min ≤V i ≤V i max Where V i min 、V i max are the lower and upper limits of the node voltage at the AC node i.

7. The EV charging behavior guidance method based on the power-traffic coupling network considering different charging station electricity prices according to claim 1, characterized in that: The power flow constraint is: Where P i , Q i is the active power and reactive power given by node i, V i 、V j is the voltage amplitude of nodes i and j, δ ij is the phase angle difference of the voltage between nodes i and j, G ij , B ij is the conductance and susceptance between nodes i and j.

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