A fast charging load charging guidance method based on adjustable hierarchical charging service fee

Abstract

The present invention relates to a fast-charging load charging guidance method based on an adjustable hierarchical charging service fee, comprising: step S1, according to the regional road network and distribution network constraints, a fast-charging load prediction model based on the travel chain and the Monte Carlo method is established to predict the changes in the user's travel time and space trajectory and the fast-charging demand; step S2, a user charging position weighted decision model is established to make charging position decisions, and the fast-charging load of each charging station is counted; step S3, a regional hierarchical charging service fee adjustment model is constructed with the optimization goal of minimizing the absolute value of the sum of the voltage deviations of the distribution network nodes in the region, and the charging service fee is optimized and adjusted; step S4, based on the adjusted charging service fee, the user charging position weighted decision model is used to decide the optimal user fast-charging load charging position. Compared with the prior art, the present invention has the advantages of effectively guiding users to charge reasonably, reducing user charging costs, and improving the voltage of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle fast charging, and in particular to a fast charging load charging guidance method based on adjustable graded charging service fees. Background Art

[0002] With the heavy consumption of fossil energy and the worsening of urban air quality, clean and low-carbon electric vehicles have been vigorously promoted by countries around the world. The development of electric vehicles has become a key way to save energy and reduce emissions in the transportation sector, and has also become an important driving force for the transformation of my country's industrial structure. However, the commissioning of high-power fast-charging charging piles, especially the large-scale access of high-power fast-charging equipment such as Tesla Supercharging Stations, will inevitably change the load characteristics of typical distribution networks and have a greater impact on the voltage quality of distribution networks. Therefore, on the basis of scientifically and accurately predicting the spatiotemporal distribution of fast-charging loads in the region, formulating a reasonable and effective charging service fee mechanism and guiding electric vehicle users to choose public charging stations for charging through reasonable decisions are of great significance for improving the power quality of urban distribution networks and delaying the capacity increase of distribution networks.

[0003] Existing studies have all taken balancing regional charging, improving the voltage quality of the distribution network, and reducing the peak-to-valley difference of the distribution network as optimization goals, and have constructed different charging service fee mechanisms, and have achieved certain results; however, the above studies usually only select individual time periods within 1 day for simulation analysis, and lack global consideration and research on the 24-hour fast charging load spatiotemporal distribution caused by adjustable charging service fees, distribution network voltage analysis, and the economic benefits of the participants. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a fast charging load charging guidance method based on adjustable graded charging service fees. This method can effectively guide users to change their charging locations, thereby changing the spatiotemporal distribution of fast charging loads, which is of great significance to improving the voltage quality of the distribution network and reducing users' charging costs.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present invention provides a fast charging load charging guidance method based on adjustable graded charging service fees, the method comprising the following steps:

[0007] Step S1: According to the constraints of the regional road network and distribution network, a fast charging load prediction model based on the travel chain and Monte Carlo method is established to predict the changes in the user's travel time and space trajectory and the fast charging demand;

[0008] Step S2: Based on the objective function of the user charging location weighted decision model with the lowest comprehensive cost of charging service fee, travel time and road power consumption, the charging location decision is made, the fast charging load of each charging station is counted, the fast charging load of each charging station is attributed to the corresponding power supply node, and the time series power flow calculation is performed;

[0009] Step S3: construct a regional hierarchical charging service fee adjustment model with the optimization target of minimizing the sum of absolute values ​​of voltage deviations of distribution network nodes in the region, and optimize and adjust the charging service fee;

[0010] Step S4: Based on the adjusted charging service fee, the user charging location weighted decision model is used to determine the optimal user fast charging load charging location.

[0011] Preferably, the step S1 specifically comprises:

[0012] Step S11: construct a regional road network model R = (D, L), where D represents a set of road network nodes, and L represents a set of road sections contained in the road network R, and obtain a road network weight adjacency matrix W including the association status of each node in the road network and the road resistance size of the road section, expressed as:

[0013]

[0014] In the formula, Represents road node d i ,d j If there is no direct path between two nodes, then is inf;

[0015] Step S12: construct a road network distribution network model, superimpose the charging load of each charging station and the node daily basic load, and obtain the comprehensive load of each node in the distribution network, which is expressed as:

[0016]

[0017] In the formula, They represent the basic load of the power supply node i of the distribution network at time t, the fast charging load of the electric vehicle cluster connected to the charging station, and the comprehensive load attributed to the node i after superposition, n G is the number of distribution network nodes;

[0018] Step S13: construct a fast charging load prediction model based on the travel chain and Monte Carlo method to characterize the changes in the user's travel time and space trajectory and the fast charging demand decision, and obtain the 24-hour total charging load of the target area and the charging load of each charging station. The expression is:

[0019]

[0020]

[0021] Where P c all (t) is the total charging load in the target area, P i,c (t) is the charging load of charging station i, N ct is the number of charging stations in the target area; is the number of electric vehicles connected to charging station i, is the charging mark of vehicle m at time t, P fast is the fast charging power of electric vehicles; t c Is the charging time in minutes.

[0022] Preferably, the set of user candidate charging stations corresponding to the fast charging demand decision in step S13 is:

[0023]

[0024] In the formula, The SOC is the remaining battery power when the user reaches the optional charging station from the charging location. sec is the power constraint; T i =t goto +t c +t back is the total time consumed, where t goto ,t c ,t back They are the time it takes for the user to go to the charging station, the time it takes to recharge, and the time it takes to drive to the destination after charging; T L The latest arrival time acceptable to the user.

[0025] Preferably, the step S13 characterizes the change of the user's travel spatiotemporal trajectory and the decision on fast charging demand as follows: based on the travel chain, the electric vehicle travel in the target area is traversed, the power consumption of each trip and the remaining battery power are calculated, and it is determined whether charging is needed. If charging is not needed, the next trip is carried out until all travel purposes are completed, otherwise the time and location of the electric vehicle fast charging are calculated; the path to the nearest charging station is planned by the Dijkstra algorithm, and the fast charging load of each charging station in the target area is updated after the power is replenished.

[0026] Preferably, the travel chain is characterized by the entire travel process of the traveler starting from the starting point, passing through several destinations, and then returning to the starting point, including a spatial characteristic chain, a time characteristic chain, and a charging characteristic chain;

[0027] In the spatial feature chain, represents the geographical location of vehicle m at destination k, expressed in two-dimensional rectangular coordinates; For vehicle m to Mileage, n dThe number of destinations included in the user's itinerary in one day;

[0028] In the time feature chain, Vehicle m leaves the parking point moment, The vehicle m arrives at the parking point time and the time spent at the stop point, For vehicle m, To destination Total time spent

[0029] In the charging feature chain, For vehicles to reach their destination The battery SOC at For vehicles from destination Drive to destination The total battery SOC change between the two is the algebraic sum of the amount of electricity consumed by the electric vehicle at the charging station and on the road.

[0030] Preferably, the user charging position weighted decision model expression in step S2 is:

[0031]

[0032] In the formula, C i , are the user charging cost under the same amount of time, the time cost consumed by the user to choose charging station i, and the battery SOC cost consumed by the user on the road, ω c ,ω T ,ω soc are the weight coefficients corresponding to the three costs respectively; the user charging cost C i Including electricity price cost and charging service fee cost.

[0033] Preferably, the user charging cost C i , Total time cost of users Battery SOC cost The quantitative expressions are:

[0034]

[0035]

[0036]

[0037] In the formula, is the charging service fee of charging station i at time t, ΔE is the amount of electricity replenished by a single electric vehicle each time, E h is the quantitative value of the electric vehicle power; θ Lis the unit travel time value corresponding to leisure time, in yuan / h, k t is the time value coefficient, S p is the annual income of the worker, T p is the annual working hours of workers; ΔSOC i The amount of electricity consumed by the user during charging, ρ slow It is the charging price in slow charging mode.

[0038] Preferably, step S3 includes the following sub-steps:

[0039] Step S31, superimposing the fast charging load obtained by the electric vehicle fast charging load prediction model and the target distribution network basic load to obtain the comprehensive load of each node in the distribution network, and constructing a charging station hierarchical charging service fee model;

[0040] Step S32, attributing the comprehensive load to the corresponding power supply node of the distribution network, and calculating the voltage of each node of the distribution network through the time series power flow;

[0041] Step S33: Taking the minimum absolute value of the voltage deviation of the nodes in the regional distribution network as the optimization target, the charging service fee is optimized and adjusted based on the adjustment mechanism.

[0042] Preferably, the charging station hierarchical charging service fee model in step S31 is specifically:

[0043]

[0044] Where ρ is the charging service fee; ρ1, ρ2, ρ3, and ρ4 are the four-level charging service fees respectively, satisfying ρ min ≤ρ1<ρ2<ρ3<ρ4≤ρ max , where ρ min is the lower limit of charging service fee, ρ max is the upper limit of charging service fee; P is the daily comprehensive load of the power supply node for 24 hours; P min and P max They are the minimum load and maximum load in a day respectively;

[0045] The boundary expression of charging service fee classification is:

[0046] P i+1 =P i +ΔP

[0047]

[0048] Where ΔP is the difference between the upper and lower boundaries of adjacent charging service fees, n p is the level of charging service fee, set to 4.

[0049] Preferably, the adjustment mechanism in step S33 is:

[0050]

[0051]

[0052] In the formula, is the charging service fee adjustment amount of charging station n at time t during the i-th adjustment; and are the charging service fees of charging station n at time t after the i-th and i-1-th adjustments respectively; ΔV b It is defined as the unit voltage deviation, Δρ is the unit deviation charging service fee adjustment step; ΔV i,t is the voltage per unit value deviation of distribution network node i at time t, and the expression is Among them, V i,t is the node voltage per unit value of node i at time t, V b is the per-unit value of the reference voltage of each node; [x] means rounding x.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] 1) The fast charging load charging guidance method based on adjustable charging service fee proposed in the present invention can effectively guide users to charge reasonably, reduce users' charging costs and improve the voltage quality of the distribution network, which has a beneficial impact on the distribution network, electric vehicle users and charging station operators;

[0055] 2) The fast charging load prediction model based on the travel chain and Monte Carlo method constructed by the present invention, starting from the perspective of the travel chain, uses the Monte Carlo method to characterize the changes in the travel time and space trajectories and fast charging demand decisions of urban electric private car users, and more scientifically and accurately predicts the spatiotemporal distribution of fast charging load in the region;

[0056] 3) The weighted decision model for user charging location adopted by the present invention takes into account the three costs of charging service fee, travel time and power consumption on the road, and makes charging location decisions based on the comprehensive benefits of fast-charging users, thereby increasing the enthusiasm of users to respond to the strategy;

[0057] 4) The present invention takes the minimum absolute value of the sum of voltage deviations of the distribution network nodes in the region as the optimization target of the regional hierarchical charging service fee adjustment model, which is of great significance to improving the overall voltage quality of the distribution network and balancing regional power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is the overall framework diagram of the present invention;

[0059] Figure 2 Schematic diagram of the travel chain structure;

[0060] Figure 3 Calculate the flow chart for Monte Carlo simulation;

[0061] Figure 4 A diagram of the charging service fee structure for charging stations;

[0062] Figure 5 Provides a charging location decision and charging load optimization distribution process for fast charging users based on regional charging service fees;

[0063] Figure 6 It is the road network-distribution network system diagram;

[0064] Figure 7 The spatiotemporal distribution of fast charging load at each charging station under fixed and regional graded charging service fees;

[0065] Figure 8 The location where charging demand is generated for each fast charging demand user in the target area;

[0066] Fig. 9 The temporal and spatial distribution of regional total fast charging load under fixed and regional graded charging service fees;

[0067] Fig.10 Voltage distribution of nodes for fast charging load access to distribution network;

[0068] Fig.11 Charging service fees for each charging station;

[0069] Fig.12 The changes before and after the charging station has 24-hour access to EVs. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0071] Example

[0072] like Figure 1 As shown, this embodiment provides a fast charging load charging guidance method based on adjustable hierarchical charging service fees, including:

[0073] 1. Electric vehicle fast charging load prediction model based on regional road network and distribution network constraints

[0074] 1.1 Regional traffic network model

[0075] The travel characteristics of electric vehicle users are often constrained by the regional traffic network. When predicting the charging load, it is necessary to take into account the impact of the traffic network on the charging load and establish a regional traffic network model. The regional network structure can be expressed as R = (D, L), where D represents the set of network nodes (road intersections or starting and ending points), numbered 1, 2, 3, ..., n; L represents the set of road sections contained in the road network R, and the road resistance of each section in the road network, that is, the distance The expression is:

[0076]

[0077] Assuming that the roads in the regional traffic network are all two-way lanes, the association of each node in the network and the road resistance of the road section can be represented by W, where W is the adjacency matrix of the network weights, expressed as:

[0078]

[0079] in, Represents road node d i ,d j The distance between the two nodes. If there is no direct path between the two nodes, then is inf, there is no path between the same road nodes, then Road node d i and d j The geographical location in the region can be expressed as (x i ,y i ) and (x j, y j ), the path distance between two nodes is expressed as:

[0080]

[0081] 1.2 Road network-distribution network model

[0082] Assume that the nodes of the regional distribution network supply power to the basic load within the power supply partition and the fast charging load of electric vehicles at the fast charging stations. When calculating the comprehensive load of each node in the distribution network, the charging load of each charging station is attributed to the corresponding distribution network power supply node and superimposed with the daily basic load of the node to obtain the overall comprehensive load of the distribution network, which is expressed as:

[0083]

[0084] Among them, P b i (t), P c i (t) They represent the basic load of the power supply node i of the distribution network at time t, the fast charging load of the electric vehicle cluster connected to the charging station, and the comprehensive load attributed to the node i after superposition, nG The number of distribution network nodes.

[0085] 1.3 Fast charging load prediction model based on travel chain and Monte Carlo simulation

[0086] The travel chain is an important means of describing the user's travel mode. It describes the entire travel process of the traveler starting from the starting point, passing through several destinations, and then returning to the starting point. It truly reveals the continuity characteristics of the entire process of urban transportation and reflects the continuous dynamics of user transportation. The schematic diagram of the electric vehicle travel chain structure proposed by the present invention is as follows: Figure 2 As shown, it includes space characteristic chain, time characteristic chain and charging characteristic chain.

[0087] In the spatial feature chain, represents the geographical location of vehicle m at destination k, expressed in two-dimensional rectangular coordinates; For vehicle m to Mileage, n d is the number of destinations included in the user's itinerary in one day. Assume that the user's daily starting point and end point are both residential areas, and he drives to various destinations for daily work, entertainment and other activities. In the time feature chain, Vehicle m leaves the parking point moment, The vehicle m arrives at the parking point time and the time spent at the stop point, For vehicle m, To destination In the charging feature chain, For vehicles to reach their destination The battery SOC at For vehicles from destination Drive to destination The total battery SOC change between the two is the algebraic sum of the amount of electricity consumed by the electric vehicle at the charging station and on the road.

[0088] In minutes, the total charging load P of the target area for 24 hours c all (t) and the charging load P of each charging station i,c (t) The calculation expression is:

[0089]

[0090]

[0091] Among them, N ct is the number of charging stations in the target area, is the number of electric vehicles connected to charging station i, is the charging mark of vehicle m at time t.

[0092] The entire fast charging load prediction of electric private cars based on the travel chain and Monte Carlo simulation is as follows: Figure 3 shown.

[0093] 2. Regional hierarchical charging service fee model based on comprehensive load and distribution network voltage quality

[0094] In view of the commercial operation needs of electric vehicles, different charging stations may require different charging service fees (including charging electricity prices). The present invention proposes a regional hierarchical charging service fee model based on comprehensive load (including basic load and charging load) and distribution network voltage quality and a charging service fee adjustment mechanism, specifically:

[0095] Firstly, the fast charging load obtained by the proposed electric vehicle fast charging load prediction model is superimposed on the target distribution network basic load, and the initial hierarchical charging service fee model is obtained using the superimposed comprehensive load;

[0096] Then, the comprehensive load is attributed to the corresponding power supply node of the distribution network, and the voltage of each node of the distribution network is calculated through the time series power flow;

[0097] Finally, taking the minimum sum of absolute values ​​of voltage deviations of distribution network nodes in the region as the optimization goal, the price mechanism is used to guide users with fast charging needs to make decisions on charging locations and replenish power, thereby improving the voltage quality of the distribution network and reducing the charging costs of electric vehicle users.

[0098] 2.1. A charging station hierarchical charging service fee model based on the integration of base load and charging load;

[0099] In a certain area, the default charging service fee of the charging station can be composed of four price levels as shown in formula (7). Among them, ρ is the charging service fee, ρ1, ρ2, ρ3, ρ4 are the four-level charging service fees respectively; P is the 24-hour daily comprehensive load of the power supply node, P min and P max They are the minimum load and maximum load in a day respectively.

[0100]

[0101] The charging service fee mentioned in the present invention already includes the price of electricity purchased by electric vehicle operators from the power grid. In order to take into account the interests of electric vehicle users and operators, the charging service fee must be limited to a certain range. min The lower limit of charging service fee should be higher than the cost of purchasing electricity from the grid to ensure the operator's profit. maxThe upper limit of charging service fee should be lower than the calculated oil price to protect the interests of users and increase the penetration rate of electric vehicles. The charging service fee constraint of charging stations at all levels can be expressed by formula (8):

[0102] ρ min ≤ρ1<ρ2<ρ3<ρ4≤ρ max (8)

[0103] The relationship between charging service fee and comprehensive load can be expressed by formula (9):

[0104] P i+1 =P i +ΔP (9)

[0105]

[0106] In the formula, ΔP is the difference between the upper and lower boundaries of adjacent charging service fees. To some extent, ΔP can be simplified to formula (10), n p is the level of charging service fee, and the value here is 4. The structure diagram of the 24-hour charging service fee level classification of a charging station is as follows Figure 4 shown.

[0107] 2.2. Charging service fee adjustment mechanism based on distribution network node voltage;

[0108] The present invention analyzes the impact of large-scale electric vehicle fast charging load access on the voltage quality of the distribution network, and then adjusts the charging service fee of each charging station. With the access of large-scale high-power disordered fast charging loads, the temporal and spatial distribution of the charging load will inevitably be uneven, resulting in a serious concentration of charging loads at individual charging stations at a certain moment, which seriously endangers the voltage quality of the power supply nodes of the distribution network. After the fast charging load and the basic load are connected to the distribution network, the voltage level of the distribution network node is analyzed through flow calculation, and the charging service fee of each charging station is adjusted according to the adjustment strategy shown in formulas (26) to (27), guiding electric vehicle users to charge reasonably, thereby balancing the temporal and spatial distribution of the fast charging load and improving the voltage level of the distribution network.

[0109] The voltage per unit value deviation expression of distribution network node i at time t is:

[0110]

[0111] V i,t is the node voltage per unit value of node i at time t, V b is the per-unit value of the reference voltage of each node, which is set to 1 in this embodiment.

[0112] By judging whether the node voltage deviation of the fast charging load access point meets the distribution network architecture constraints, the charging service fee is adjusted.it The expression is:

[0113]

[0114] The specific charging service fee adjustment strategy of each fast charging station can be described by formula (13) and formula (14). is the charging service fee adjustment amount of charging station n at time t during the i-th adjustment. and are the charging service fees of charging station n at time t after the i-th and i-1-th adjustments respectively. ΔV b It is defined as the unit voltage deviation, with a value of 0.01, and Δρ is the unit deviation charging service fee adjustment step, which is 0.01 yuan. [x] means rounding x.

[0115]

[0116]

[0117] 3. Research on charging location decision-making based on the lowest comprehensive cost for fast-charging users

[0118] 3.1、User's alternative charging station set B;

[0119] Assuming the user has remaining battery power when arriving at the optional charging station from the location where charging is required To be greater than 10%, the power constraint can be expressed by equation (15):

[0120]

[0121] The total time T taken by a fast charging user to generate charging demand, drive to a charging station, recharge, and reach the destination i The time limit should be lower than the user's acceptable latest arrival time constraint T L , which can be expressed as formula (16):

[0122] t goto +t c +t back =T i <T L (16)

[0123] t goto ,t c ,t back are the time it takes for the user to go to the charging station, the time it takes to charge, and the time it takes to drive to the destination after charging. Under the above constraints, the user's candidate charging station set B can be expressed by formula (17):

[0124]

[0125] 3.2 User Charging Decision Model X Ws ;

[0126] When making charging decisions, users often consider factors such as time, charging service fee cost, and remaining battery power, and choose the charging station with the lowest comprehensive charging cost to charge. Therefore, the present invention proposes a charging location decision method based on the lowest comprehensive charging cost of fast charging users themselves, and describes the influence of charging service fees, total consumption time, and remaining battery power on the user's choice of charging station location through a weighted decision model. Its decision model can be expressed by model (18):

[0127]

[0128] Among them, C i Charging costs for users, including electricity price costs and service fee costs; The total time cost for users; The SOC cost of the battery consumed by the user during the journey; c ,ω T ,ω soc are the weight coefficients of the three costs respectively.

[0129] Time Value Factor

[0130] In view of the problem that time cost, charging cost and battery power cost cannot be directly weighted due to different dimensions, the present invention considers converting battery power cost and time cost into costs with the same dimensions as charging cost attributes. The income method is used to quantify the user's time cost, and the time value coefficient (a certain percentage of personal income under the current market economy construction) is used to quantify the time cost. The specific calculation method can be expressed by formula (19):

[0131] θ L =k t S p / T p (19)

[0132] Among them, θ L is the unit travel time value corresponding to leisure time, yuan / h; k t is the time value coefficient. In view of the imperfect market economy construction and wage distribution system in my country, it is recommended to take 50%; Sp is the annual income of the worker, and the average annual income of Shanghai is 87,222 yuan; Tp is the annual working hours of the worker, which is set to 365*8h. Therefore, the time cost consumed by the user to choose charging station i can be quantified as formula (20):

[0133]

[0134] The battery SOC cost consumed by users who require fast charging during charging can be quantified as the cost required to compensate for the same amount of power consumed during the charging process by slow charging. The specific calculation formula is (21):

[0135] Csoci=ΔSOC i ·ρ slow ·E h (twenty one)

[0136] In the formula, ΔSOC i The amount of electricity consumed by the user during charging, ρ slow It is the charging price in slow charging mode, which is 0.63 yuan / kWh.

[0137] Users who need fast charging go to charging stations to charge, and their charging cost is C i It can be expressed as formula (22), is the charging service fee of charging station i at time t, and ΔE is the amount of electricity replenished by a single electric vehicle each time.

[0138]

[0139] Therefore, after converting the time cost, battery power cost, and charging cost into the same dimension through equations (20) to (23), the user charging location decision model can be modified to equation (23):

[0140]

[0141] The process of fast charging user charging location decision and charging load optimization distribution based on regional charging service fee guidance is as follows: Figure 5 shown.

[0142] 4. A typical area and a typical IEEE 30-node distribution network are simulated to verify the effectiveness of the method of the present invention.

[0143] Road network and distribution network systems such as Figure 5 The target area is a 25km*25km plot, which includes 72 road nodes and 144 roads. The target area includes 18 residential areas, 18 work areas and 14 other areas. There are 12 fast charging stations in the target area, as shown in the figure below. Figure 7 As shown, they are numbered S1, S2, ..., S 11 ,S 12 , respectively powered by nodes 3, 5, 7, 8, 12, 18, 21, and 24 of a typical IEEE 30-node distribution network, and some nodes are set to power multiple fast-charging stations. Assuming that each charging station has sufficient charging piles to meet the fast-charging needs of users under the current electric vehicle penetration rate in the region, all charging stations are equipped with a single fast-charging pile with a charging power of 60kW.

[0144] According to the "Interim Regulations on the Construction and Management of Electric Vehicle Charging Facilities in Shanghai", the maximum charging service fee shall not exceed 1.6 yuan / kWh, so 1.5 / kWh is taken as the fixed charging service fee for each charging station in the present invention (including charging electricity price and service fee). In this mode, electric vehicles will be charged in a nearby manner. When adopting regional graded charging service fees, the peak-time electricity price of Shanghai's general industrial and commercial sectors (0.912 yuan / kWh) is used as the lower limit of the charging service fee; in the example, the electric vehicle consumes 0.25 kWh per kilometer, and the upper limit of the charging service fee after calculation can be obtained. max To protect the interests of users and charging stations, the initial ρ1 and ρ4 of each charging station are set to 1.4 yuan / kWh and 1.6 yuan / kWh.

[0145] The number of electric vehicles in the target area is 122,900, and their daily initial locations are all in residential areas. It is assumed that users drive a new Nissan Leaf electric private car with a battery capacity of 40kWh, an average speed of 50km / h, and a power consumption of 0.25kWh / km per mile.

[0146] 1) Comparative analysis of fast charging load at each charging station under fixed charging service fee and tiered charging service fee

[0147] According to the fast charging load spatiotemporal distribution prediction model, the regional fast charging load spatiotemporal distribution of each charging station based on a fixed charging service fee under the constraints of the traffic network is obtained. Based on the proposed regional hierarchical charging service fee model and the user charging location decision model, fast charging users are guided to charge reasonably and the optimized fast charging load spatiotemporal distribution is obtained. The 24-hour spatiotemporal distribution of fast charging load at each charging station under fixed charging service fees and hierarchical charging service fees and their distribution characteristics are shown in the figure below. Figure 7 And as shown in Table 1.

[0148] Table 1

[0149]

[0150] Depend on Figure 7 As shown in Table 1, under a fixed charging service fee, the charging load of each fast charging station is generally distributed in a single peak shape. The peak time varies due to the different locations of the charging stations, with the earliest occurring at 15:13 and the latest at 18:31. There are large differences in the peak charging load and total charging capacity of different charging stations.

[0151] From Table 1 and Figure 8From the distribution of user charging locations under a fixed charging service fee, it can be seen that users near charging stations 2, 6, 7, 8, and 11 have greater charging demands. When users choose charging stations nearby, the number of vehicles connected to these stations and the fast charging load are correspondingly large, especially for charging station 6, which has the largest number of users with charging needs within a radius of 4 kilometers, at 14,336. The random daily fast charging negative charge volume of this station is also the highest among all stations, reaching 168,600 kWh.

[0152] 2) Comparative analysis of regional total fast charging load under fixed charging service fee and tiered charging service fee

[0153] The spatiotemporal distribution results of the total 24-hour fast charging load in the target area under fixed charging service fees and tiered charging service fees are shown in Figure 2, and the distribution characteristics of the regional total fast charging load are shown in Table 2.

[0154] Table 2

[0155]

[0156] There are 26,528 fast-charging users in the region, accounting for 21.58% of all electric vehicle users. Fig. 9 It can be seen that under a fixed charging service fee, the disordered charging load generated by users choosing the nearest charging location is generally distributed in a unimodal shape. The peak time of the charging load is 16:40, the peak value is 79.20MW, and the total charging capacity is 795.78MWh.

[0157] 3) Impact of fast charging load on distribution network voltage quality under fixed charging service fee and tiered charging service fee

[0158] The time series flow calculation is performed on the fast charging load under different conditions of typical distribution network access in the target area. The 24-hour node voltage changes of the distribution network under the connection of only basic load and regional fast charging load under different charging service fee modes are shown in the figure. The node voltage deviation when the electric vehicle load is connected to the distribution network in different states is shown in Table 3.

[0159] Table 3

[0160]

[0161]

[0162] When the distribution network is only connected to the basic load, the node voltage quality is generally good. However, since nodes 8, 19, and 30 are the terminal nodes of the entire distribution network and the basic load is heavy, the node voltage quality is relatively poor. The load peaks occur at 10:00 and 21:00, respectively, when the voltage deviation of each node in the entire network is the largest. Among them, the node with the lowest voltage in the entire network is node 8, which appears at 10:00 and its voltage per unit value is 0.960.

[0163] 4) Based on fixed charging service fee

[0164] Fig.10 (a) The node voltage distribution diagram when the random fast charging load generated by the fast charging demand user who chooses the nearest charging station for charging under the principle of proximity under a fixed charging service fee is connected to the distribution network. With the access of high-power fast charging loads, the voltage quality of the distribution network nodes has further deteriorated. Nodes 7, 8, 18, 19, and 20 have large voltage deviations, among which the voltage drop at node 18 is the most serious. Its node voltage per unit value reaches 0.883 at 17:30, and its node voltage per unit value is lower than 0.93 for 18 consecutive times. If no corresponding measures are taken to improve the voltage quality, the safety and stability of the distribution network operation will be greatly reduced. And as shown in Table 3, with the access of random fast charging loads, the total node voltage deviation reaches 37.804, which increases by 15.726 under the access of basic load.

[0165] 5) Charging service fees based on regional classification

[0166] By adjusting the charging service fee to guide users to change their charging locations, the voltage distribution of each node after the actual charging load is connected to the distribution network is as follows: Fig.10 As shown in (b). Through the guidance of charging service fees, the voltage quality of the distribution network under the fast charging load access is greatly improved. The voltage per unit value of each node in the whole network at all times is higher than 0.93, and the overall voltage deviation is 33.525, which is 4.279 lower than the overall voltage deviation when the fast charging load is connected under the fixed charging service fee. The voltage quality optimization effect is obvious.

[0167] Fig.11 is the 24-hour charging service fee for each charging station when the total voltage deviation of the target area distribution network is the smallest. When each charging station adopts a fixed charging service fee and a tiered charging service fee, the total number of electric vehicles connected to each charging station within 24 hours changes as follows: Fig.12 As shown in the figure. The charging stations of the former are numbered 1, 2, 3…, 11, 12, and the charging stations of the latter are numbered 1*, 2*, 3*…, 11*, 12*. It can be seen that after the introduction of the adjustment strategy of graded charging service fees, users compare the comprehensive charging costs of going to different charging stations and choose the charging station with the lowest comprehensive cost to charge, thereby realizing the transfer of charging load in time and space.

[0168] Table 4

[0169]

[0170]

[0171] Table 5

[0172] benefit Profit / yuan Total revenue / yuan Fixed charging service fee 468008.64 1193760.00 Regional tiered charging service fees 357224.04 1082975.40 Growth percentage / % -23.67 -9.28

[0173] Tables 4 and 5 are comparisons of the charging costs of electric vehicle users and the power supply revenue of charging station operators under fixed charging service fees and regional hierarchical charging service fees, respectively. On the one hand, from the perspective of user charging costs, when each charging station adopts a fixed charging service fee, the users with fast charging needs in the target area determine the location of the charging station based on the principle of proximity, and the total charging cost is 1,377,603.76 yuan; when the regional hierarchical charging service fee proposed by the present invention is adopted, the total cost is 1,275,876.55 yuan. Based on the regional hierarchical charging service fee, the user charging service fee cost saves 110,784.6 yuan compared with the nearby power supply, saving 9.28%; however, due to the choice of charging stations with lower charging service fees, users have to sacrifice time costs and drive vehicles to farther charging stations, so the latter's travel time cost is increased compared with the former; from the perspective of the battery costs of the two, the difference is not large. However, in terms of the total cost of the two, the total charging cost of the latter is reduced by 7.38% compared with the former, and the user charging benefits are obvious.

[0174] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A fast charging load charging guidance method based on adjustable graded charging service fees, characterized in that: The method comprises the following steps: Step S1: According to the constraints of the regional road network and distribution network, a fast charging load prediction model based on the travel chain and Monte Carlo method is established to predict the changes in the user's travel time and space trajectory and the fast charging demand, specifically: Step S11: construct a regional road network model R = (D, L), where D represents a set of road network nodes, and L represents a set of road sections contained in the road network R, and obtain a road network weight adjacency matrix W including the association status of each node in the road network and the road resistance size of the road section, expressed as: In the formula, Represents road node d i ,d j If there is no direct path between two nodes, then is inf; Step S12: construct a road network distribution network model, superimpose the charging load of each charging station and the node daily basic load, and obtain the comprehensive load of each node in the distribution network, which is expressed as: Where P b i (t), P c i (t) They represent the basic load of the power supply node i of the distribution network at time t, the fast charging load of the electric vehicle cluster connected to the charging station, and the comprehensive load attributed to the node i after superposition, n G is the number of distribution network nodes; Step S13: construct a fast charging load prediction model based on the travel chain and Monte Carlo method to characterize the changes in the user's travel time and space trajectory and the fast charging demand decision, and obtain the 24-hour total charging load of the target area and the charging load of each charging station. The expression is: Where P c all (t) is the total charging load in the target area, P i,c (t) is the charging load of charging station i, N ct is the number of charging stations in the target area; is the number of electric vehicles connected to charging station i, is the charging mark of vehicle m at time t, P fast is the fast charging power of electric vehicles; t c is the charging time in minutes; Step S2: Based on the objective function of the user charging location weighted decision model with the lowest comprehensive cost of charging service fee, travel time and road power consumption, the charging location decision is made, the fast charging load of each charging station is counted, the fast charging load of each charging station is attributed to the corresponding power supply node, and the time series power flow calculation is performed; The user charging position weighted decision model expression is: In the formula, C i , are the user charging cost under the same amount of time, the time cost consumed by the user to choose charging station i, and the battery SOC cost consumed by the user on the road, ω c ,ω T ,ω soc are the weight coefficients corresponding to the three costs respectively; the user charging cost C i Including electricity price cost and charging service fee cost; Step S3: construct a regional hierarchical charging service fee adjustment model with the optimization goal of minimizing the sum of absolute values ​​of voltage deviations of distribution network nodes in the region, and optimize and adjust the charging service fee, including the following sub-steps: Step S31, superimposing the fast charging load obtained by the electric vehicle fast charging load prediction model and the target distribution network basic load to obtain the comprehensive load of each node in the distribution network, and constructing a regional hierarchical charging service fee adjustment model; Step S32, attributing the comprehensive load to the corresponding power supply node of the distribution network, and calculating the voltage of each node of the distribution network through the time series power flow; Step S33, taking the minimum absolute value of the voltage deviation of the distribution network nodes in the region as the optimization target, and optimizing and adjusting the charging service fee based on the adjustment mechanism; Step S4: Based on the adjusted charging service fee, the user charging location weighted decision model is used to determine the optimal user fast charging load charging location.

2. A fast charging load charging guidance method based on adjustable graded charging service fees according to claim 1, characterized in that: The set of user candidate charging stations corresponding to the fast charging demand decision in step S13 is: In the formula, The SOC is the remaining battery power when the user reaches the optional charging station from the charging location. sec is the power constraint; T i =t goto +t c +t back is the total time consumed, where t goto ,t c ,t back They are the time it takes for the user to go to the charging station, the time it takes to recharge, and the time it takes to drive to the destination after charging; T L The latest arrival time acceptable to the user.

3. According to claim 1, a fast charging load charging guidance method based on adjustable graded charging service fees is characterized in that: The step S13 describes the changes in the user's travel time and space trajectory and the decision on fast charging demand as follows: based on the travel chain, the electric vehicle travel in the target area is traversed, the power consumption of each trip and the remaining battery power are calculated, and it is determined whether charging is needed. If charging is not needed, the next trip is carried out until all travel purposes are completed, otherwise the time and location of the electric vehicle generating fast charging are calculated; the path to the nearest charging station is planned by the Dijkstra algorithm, and the fast charging load of each charging station in the target area is updated after the power is replenished.

4. A fast charging load charging guidance method based on adjustable graded charging service fees according to claim 2, characterized in that: The travel chain is characterized by the entire travel process of a traveler starting from a starting point, passing through several destinations, and then returning to the starting point, including a spatial feature chain, a temporal feature chain, and a charging feature chain; In the spatial feature chain, represents the geographical location of vehicle m at destination k, expressed in two-dimensional rectangular coordinates; For vehicle m to Mileage, n d The number of destinations included in the user's itinerary in one day; In the time feature chain, Vehicle m leaves the parking point moment, The vehicle m arrives at the parking point time and the time spent at the stop point, For vehicle m, To destination Total time spent In the charging feature chain, For vehicles to reach their destination The battery SOC at For vehicles from destination Drive to destination The total battery SOC change between the two is the algebraic sum of the amount of electricity consumed by the electric vehicle at the charging station and on the road.

5. A fast charging load charging guidance method based on adjustable graded charging service fees according to claim 1, characterized in that: The user charging cost C i , Total user time cost Battery SOC cost The quantitative expressions are: In the formula, is the charging service fee of charging station i at time t, ΔE is the amount of electricity replenished by a single electric vehicle each time, E h is the quantitative value of the electric vehicle power; θ L is the unit travel time value corresponding to leisure time, in yuan / h, k t is the time value coefficient, S p is the annual income of the worker, T p is the annual working hours of workers; ΔSOC i The amount of electricity consumed by the user during charging, ρ slow It is the charging price in slow charging mode.

6. A fast charging load charging guidance method based on adjustable graded charging service fees according to claim 1, characterized in that: The regional graded charging service fee adjustment model in step S31 is specifically: Where ρ is the charging service fee; ρ1, ρ2, ρ3, and ρ4 are the four-level charging service fees respectively, satisfying ρ min ≤ρ1<ρ2<ρ3<ρ4≤ρ max , where ρ min is the lower limit of charging service fee, ρ max is the upper limit of charging service fee; P is the daily comprehensive load of the power supply node for 24 hours; P min and P max They are the minimum load and maximum load in a day respectively; The boundary expression of charging service fee classification is: P i+1 =P i +ΔP Where ΔP is the difference between the upper and lower boundaries of adjacent charging service fees, n p is the level of charging service fee, set to 4.

7. A fast charging load charging guidance method based on adjustable graded charging service fees according to claim 1, characterized in that: The adjustment mechanism in step S33 is: In the formula, is the charging service fee adjustment amount of charging station n at time t during the i-th adjustment; and are the charging service fees of charging station n at time t after the i-th and i-1-th adjustments respectively; ΔV b It is defined as the unit voltage deviation, Δρ is the unit deviation charging service fee adjustment step; ΔV i,t is the voltage per unit value deviation of distribution network node i at time t, and the expression is Among them, V i,t is the node voltage per unit value of node i at time t, V b is the per-unit value of the reference voltage of each node; [x] means rounding x.

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