CN117944506B - Electric vehicle charging guidance method and system based on power distribution-traffic system - Google Patents

Abstract

An electric vehicle charging guiding method and system based on a power distribution-traffic system constructs an electric vehicle charging system guiding model based on an urban power distribution network through charging demand distribution constraint, charging station service constraint and power distribution network constraint, an objective function of the electric vehicle charging system guiding model is that the system running cost is minimum, simulation calculation is conducted on the constructed electric vehicle charging system guiding model based on the urban power distribution network, and a space-time distribution scheme of the optimized charging load is output. The design can not only obtain the lowest running cost of the charging system through the cost of each sub-item of the system and effectively reduce the comprehensive cost of the charging system of the electric automobile, but also reduce the resource waste caused by capacity margin configuration and effectively improve the utilization rate of charging facilities.

Description

Electric vehicle charging guidance method and system based on power distribution-traffic system

Technical Field

The present invention relates to a charging system guiding method, and in particular to an electric vehicle charging guiding method and system based on a power distribution-transportation system.

Background Art

The large-scale access of electric vehicle charging loads will have a qualitative impact on the distribution network. Since the peak value of electric vehicle charging loads overlaps with the basic load of the power grid, the access of charging loads will lead to an increase in the peak value of the power grid load and an increase in the peak-to-valley difference of the load, thereby further aggravating the problem of "peak on peak"; on the other hand, the access of electric vehicle charging loads will significantly change the distribution of power flow in the distribution network, thereby affecting the network loss, node voltage and economy of the entire system.

The existing electric vehicle charging system usually includes two types of charging stations. The ordinary vehicle charging stations serve electric private cars, electric taxis and electric logistics vehicles, while the bus charging stations serve electric buses.

Although this electric vehicle charging system can meet the daily charging needs of different types of electric vehicles, it still has the following defects:

1. Since the peak value of electric vehicle charging load overlaps with the basic load of the power grid, the access of charging load will lead to an increase in the peak value of the power grid load and an increase in the peak-to-valley difference of the load, further aggravating the problem of "peak on peak".

2. In order to cope with the large-scale access of electric vehicle charging loads, the distribution network and charging stations are equipped with sufficient hardware facilities, resulting in the charging capacity of existing charging stations often being greater than the actual charging demand, and the utilization rate of charging facilities is low.

The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field.

Summary of the invention

The purpose of the present invention is to overcome the shortcomings of the prior art that the peak-to-valley difference of the power grid load is large and the utilization rate of the charging facilities is low, and to provide an electric vehicle charging guidance method and system based on a distribution-transportation system with a small peak-to-valley difference of the power grid load and a low utilization rate of the charging facilities.

To achieve the above objectives, the technical solution of the present invention is:

A method for guiding charging of electric vehicles based on a power distribution-transportation system, the method comprising the following steps:

S1, constructing an electric vehicle charging system guidance model, wherein the objective function of the electric vehicle charging system guidance model is to minimize the system comprehensive cost, and the constraints include charging demand allocation constraints, charging station service constraints and distribution network constraints;

The charging demand allocation constraints include the relationship constraints between the total charging demand and various charging demands, the planning constraints on various charging demands, and the total amount constraints of various charging demands corresponding to various time periods and starting and ending points. The charging station service constraints include power balance constraints and service capacity constraints for ordinary vehicle charging stations and bus charging stations. The distribution network constraints include constraints on the power output of the distribution network to the charging station, power balance constraints on the distribution network lines, power upper limit constraints on the distribution network lines, and voltage constraints on the distribution network busbars.

The expression of the objective function is:

min C _E +C _VD +C _VC +C _D ;

In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost;

S2, based on the constructed electric vehicle charging system guidance model, performs simulation calculations and outputs the optimized spatiotemporal distribution plan of the charging load.

The expressions of the system electricity purchase cost _CE , the distribution network facility redundancy cost _CVD , the charging facility redundancy cost _VC and the vehicle travel cost _CD are as follows:

In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVD is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CT _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, CA _e is the installed capacity of the ordinary vehicle charging pile connected to the distribution network node e, CB _e is the installed capacity of the bus charging pile connected to the distribution network node e, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l during time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of a charging pile for ordinary vehicles per unit capacity.

The expression of the charging demand allocation constraint is:

In the above formula, is the total traffic flow generated by charging demand on road l during time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _{t, l} is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _{t, od} is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _{t, od} is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _{t, od} is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _{t, od} is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is a parameter indicating whether a bus charging station is set up at road l. When it is 1, it indicates that a common charging station and a bus charging station are set up at road l. δ _M is the big M constant in the big M method.

The expression of the charging station service constraint is:

In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the node e of the distribution network from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EV is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, η is the energy transmission efficiency of the ordinary vehicle charging pile, _ηB is the energy transmission efficiency of the bus charging pile, εe _,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of the ordinary vehicle charging pile, _θB is the power factor angle of the bus charging pile, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles configured at the distribution network node e, _PA is the single pile rated power of the ordinary vehicle charging pile, and _PB is the single pile rated power of the bus charging pile.

The expression of the distribution network constraint is:

In the above formula, c is the distribution line connected to the distribution network node e, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the node e of the distribution network in time period t, is the reactive basic load connected to the node e of the distribution network within the time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _t,c is the voltage drop on the distribution network line c within the time period t, U _t,a is the bus voltage of the distribution network node a within the time period t, U _t,b is the bus voltage of the distribution network node b within the time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage.

An electric vehicle charging guidance system based on a power distribution-transportation system, the guidance system comprising a model building module and a simulation calculation module;

A model building module is used to build a distribution-transportation system model for electric vehicle charging. The objective function of the electric vehicle charging system guidance model is to minimize the system operation cost. The constraints include charging demand allocation constraints, charging station service constraints, and distribution network constraints.

The objective function of the electric vehicle charging system guidance model is to minimize the system comprehensive cost, and the constraints include charging demand allocation constraints, charging station service constraints and distribution network constraints;

The simulation calculation module is used to perform simulation calculations using the constructed distribution-traffic system model for electric vehicle charging, and output an optimized spatiotemporal distribution plan of the charging load.

The expression of the objective function is:

min C _E +C _VD +C _VC +C _D ;

In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CT _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, CA _e is the installed capacity of the ordinary vehicle charging pile connected to the distribution network node e, CB _e is the installed capacity of the bus charging pile connected to the distribution network node e, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l in time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of a charging pile for ordinary vehicles per unit capacity.

The expression of the charging demand allocation constraint is:

In the above formula, is the total traffic flow generated by charging demand on road l during time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _{t, l} is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _{t, od} is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _{t, od} is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _{t, od} is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _{t, od} is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is the parameter of whether there is a bus charging station at road l. When it is 1, it means that there are ordinary charging stations and bus charging stations at road l. δ _M is the big M constant in the big M method;

The expression of the charging station service constraint is:

In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the node e of the distribution network from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EV is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, _ηA is the energy transmission efficiency of the ordinary vehicle charging pile, _ηB is the energy transmission efficiency of the bus charging pile, εe _,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of the ordinary vehicle charging pile, _θB is the power factor angle of the bus charging pile, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles configured at the node e of the distribution network, _PA is the single pile rated power of the ordinary vehicle charging pile, and _PB is the single pile rated power of the bus charging pile;

The expression of the distribution network constraint is:

In the above formula, c is the distribution line connected to the distribution network node e, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the node e of the distribution network in time period t, is the reactive basic load connected to the node e of the distribution network within the time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _{t, c} is the voltage drop on the distribution network line c within the time period t, U _t, a is the bus voltage of the distribution network node a within the time period t, U _{t, b} is the bus voltage of the distribution network node b within the time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage.

An electric vehicle charging guidance device based on a power distribution-transportation system, the device comprising a processor and a memory;

The memory is used to store computer program code and transmit the computer program code to the processor;

The processor is used to execute an electric vehicle charging guidance method based on a power distribution-transportation system according to instructions in the computer program code.

A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for guiding charging of an electric vehicle based on a power distribution-transportation system is implemented.

Compared with the prior art, the present invention has the following beneficial effects:

1. In the electric vehicle charging guidance method based on the power distribution-transportation system of the present invention, in the objective function established in S1, the objective function of the power distribution-transportation system model of electric vehicle charging is constructed by combining the system power purchase cost _CE , the redundant cost of distribution network facilities _CVD , the redundant cost of charging facilities _CVC and the vehicle passage cost _CD , and the system sub-item costs are calculated by combining the time period of concentrated charging load and the power grid data. In the S2 simulation calculation, the minimum operating cost of the charging system is obtained by solving the objective function, and then the flexible charging demand during the peak charging period is guided to the non-peak period, so as to achieve the effect of minimizing the comprehensive cost caused by the charging behavior of the power distribution-transportation system. Therefore, this design can obtain the minimum operating cost of the charging system through the sub-item costs of the system, and effectively reduce the comprehensive cost of the electric vehicle charging system.

2. In the electric vehicle charging guidance method based on the power distribution-transportation system of the present invention, in the S2 simulation calculation, after solving the objective function to obtain the minimum operating cost of the charging system, the spatiotemporal distribution of the charging load is optimized and reconstructed according to the minimum operating cost, so that the peak distribution of the electric vehicle charging load is more even, and the increase in the peak value of the grid load after the grid is connected to the charging load is reduced. Therefore, this design can optimize and reconstruct the spatiotemporal distribution of the charging load according to the minimum operating cost of the charging system, and effectively reduce the peak-to-valley difference of the grid load.

3. In the electric vehicle charging guidance method based on the power distribution-transportation system of the present invention, by optimizing the time and space distribution of electric vehicle charging demand in the city, the flexibility of charging loads of different types of electric vehicles is utilized to optimize and guide their charging behavior, thereby improving the utilization rate of charging facilities. Therefore, this design can reduce the waste of resources caused by capacity margin configuration and effectively improve the utilization rate of charging facilities.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of the guiding method in the present invention.

FIG. 2 is a simplified diagram of the power distribution-transportation system for charging electric vehicles to which the present invention is directed.

FIG. 3 is a structural block diagram of the guidance system in the present invention.

FIG. 4 is a structural block diagram of the guiding device in the present invention.

FIG. 5 is a timing characteristic curve of charging demand of various types of vehicles in Example 1.

FIG. 6 shows the time-of-use electricity price used in Example 1.

FIG. 7 is a topological diagram of a 30-node distribution network-22-node transportation network in Example 1.

FIG. 8 is a schematic diagram of the planned locations of the charging stations in Example 1.

FIG. 9 is a timing curve diagram of charging load of two methods in Example 1.

DETAILED DESCRIPTION

The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

The optimized spatiotemporal distribution scheme of charging load output by this method refers to the optimal scheme of guiding and optimizing the charging window of electric vehicles at the time level while taking into account the flexible charging demand, and guiding electric vehicles to the charging station by using the shortest path method at the spatial level;

The flexible charging demand refers to the fact that the charging time and location of electric vehicles depend on the choice of the owner, so the temporal and spatial distribution of charging load has a certain flexibility;

In S2 of the present invention, simulation calculation is performed based on the constructed electric vehicle charging system guidance model by MATLAB/CPLEX;

The structure of the distribution-transportation system for charging electric vehicles to which the present invention is directed is shown in FIG2 , including electric private cars, electric taxis, electric buses, electric logistics vehicles, ordinary vehicle charging stations, bus charging stations, distribution networks and transportation networks; there are two types of charging stations in the system, of which ordinary vehicle charging stations serve electric private cars, electric taxis and electric logistics vehicles, and bus charging stations serve electric buses. The present invention takes into account the comprehensive costs including the system power purchase cost, the redundant cost of distribution network facilities, the redundant cost of charging facilities and the vehicle passage cost, and takes into account the charging demand characteristics of different types of electric vehicles, optimizes the configuration of charging facilities and guides the flexible charging load, thereby optimizing and reconstructing the spatiotemporal distribution of the charging load to minimize the overall system cost.

Embodiment 1:

The electric vehicle charging guidance method based on the urban distribution network proposed in the present invention takes into account the charging demands of four types of electric private cars, electric taxis, electric buses and electric logistics vehicles. The corresponding charging demand timing characteristic curve is shown in FIG5 ;

The charging load of electric private cars is mainly concentrated in four time periods: 5:00 to 7:00, 15:00 to 17:00, 18:00 to 20:00 and 22:00 to 1:00. Among them, from 5:00 to 7:00 and 15:00 to 17:00, some private cars are guided by charging prices to charge quickly at public charging piles for a short time, from 18:00 to 20:00, private cars use public charging piles to replenish electricity after get off work, and some cars choose to charge at public charging piles during the low period after 22:00 in the evening due to the guidance of peak and valley electricity prices. The charging load of electric taxis is mainly concentrated in four time periods: from 4:00 to 7:00 in the morning, from 15:00 to 17:00 in the afternoon, from 18:00 to 20:00 in the evening, and from 23:00 to 2:00 in the morning. Among them, from 4:00 to 7:00 in the morning is the load peak generated by some taxi parking spaces charging to meet the morning rush hour, from 15:00 to 17:00 in the afternoon is the load peak generated by some taxis charging to meet the evening rush hour, and from 18:00 to 20:00 in the evening is the shift change after the evening rush hour, and the night shift drivers charge the vehicles for Short-term charging: from 11 p.m. to 2 a.m., taxi drivers use the low-peak electricity prices at night to charge their vehicles, resulting in a peak charging load period throughout the day. Due to the relatively small number of buses operating at night and the influence of low-peak electricity prices at night, in order to reduce operating costs, bus operating companies choose to conduct centralized charging at night, resulting in a peak charging load throughout the day from 10 p.m. to 2 a.m.; in the early morning, as the battery power gradually saturates, the charging load continues to decline, and the charging load reaches a valley value at 6 a.m. to cope with the upcoming morning rush hour; during the day, buses are in continuous operation, travel frequently, and consume a lot of electricity, so intermittent charging is required; most logistics vehicles generally carry out cargo transportation tasks during the day. Due to the large traffic volume during peak hours in the morning and evening, logistics vehicles often choose to travel during off-peak hours and charge during this time period, so their daytime charging peak occurs from 5 a.m. to 7 a.m. and from 3 p.m. to 8 p.m. On the other hand, under the influence of low-peak electricity prices at night, logistics vehicles begin to charge in a centralized manner, resulting in another load peak from 10 p.m. to 1 a.m.;

In summary, the flexible allocation time periods for charging requirements of four types of electric private cars, electric buses, electric taxis and electric logistics vehicles set by the present invention are shown in Table 1:

Table 1 Flexibility allocation time periods for various types of charging demands

In order to verify the electric vehicle charging guidance method based on urban distribution network proposed in the present invention, the method is applied to a 30-node distribution network-22-node transportation network example, and its topological structure is shown in FIG7 ;

The parameters of the 30-node distribution network-22-node transportation network example are as follows: the unit capacity configuration costs of ordinary vehicle charging piles and bus charging piles are 30,000 yuan/kW and 20,000 yuan/kW respectively; the investment cost of the distribution transformer per unit capacity and the investment cost of the distribution line are 20,000 yuan/MW and 40,000 yuan/MW respectively; the operating life of the distribution transformer, distribution line, ordinary vehicle charging pile and bus charging pile is 87,600 hours; the unit time is 1 hour; the driving cost per unit distance is 0.2 yuan/km; the average energy demand of electric private cars, electric taxis, electric buses and electric logistics vehicles for a single charge is 40 kWh respectively , 60 kWh, 60 kWh and 100 kWh; the energy transmission efficiency of ordinary vehicle charging piles and bus charging piles is 0.9; the power factor of ordinary vehicle charging piles and bus charging piles is 0.9; the single pile rated power of ordinary vehicle charging piles and bus charging piles is 60 kW and 120 kW respectively; the rated voltage of the distribution network is 10 kV, and the upper and lower limits of the distribution network bus voltage are 10.5 kV and 9.5 kV respectively; the time-of-use electricity price purchased from the upper power grid is shown in Figure 6; in the 30-node distribution network-22-node transportation network, the configuration locations of ordinary vehicle charging stations and bus charging stations and the number of charging piles are shown in Figure 8 and Table 2:

Table 2 Charging station configuration location and internal charging pile configuration quantity

The electric vehicle charging guidance method based on the power distribution-transportation system in the present invention is taken as method 1, and the disordered charging guidance method that does not consider the optimization of the spatiotemporal distribution of charging demand is taken as method 2 for comparison with method 1;

Method 2 does not consider the flexibility of charging demand, does not guide and optimize the charging window of electric vehicles at the time level, and uses the shortest path method to guide electric vehicles to charging stations at the spatial level. The above two methods are applied to the 30-node distribution network-22-node transportation network example. The charging load curves of the two methods are shown in Figure 9, and the comparison of economic indicators is shown in Table 3:

Table 3 Economic comparison

As shown in Figure 9, when method 1 is used, compared with method 2, the charging load of electric vehicles changes in multiple time periods: in the early morning, when method 1 is used, the peak value of the charging power is small, while in the afternoon, the charging load power is generally large when method 1 is used; from the perspective of peak-to-valley difference, when method 1 is used, the peak-to-valley difference is reduced by 19.81% compared with method 2, which fully alleviates the "peak-to-peak" problem caused by the access of electric vehicle charging load to the distribution network load; in terms of economic efficiency, as shown in Table 3, except for the vehicle passage cost, the economic efficiency when method 1 is used is significantly better than that when method 2 is used; compared with method 2, the system power purchase cost, distribution network facility redundancy cost, and charging facility redundancy cost when method 1 are used are reduced by 2.07%, 1.19%, and 4.74%, respectively; and in terms of total cost, the economic efficiency when method 1 is used is also significantly better than that of method 2, and its system comprehensive cost is reduced by 1.80%; the above results show that the electric vehicle charging optimization configuration and guidance method based on urban distribution network proposed in the present invention achieves the effect of reducing the comprehensive cost caused by the charging behavior of electric vehicles in the distribution-transportation system.

Embodiment 2:

Referring to FIG. 1 , a method for guiding charging of electric vehicles based on a power distribution-transportation system includes the following steps:

Count the charging load, count the charging load of each charging pile corresponding to the urban distribution network, make the charging demand of different models into a charging demand time series characteristic curve table, and count the time periods when the charging load of different models is concentrated based on the charging demand time series characteristic curve table;

The guiding objective function of the electric vehicle charging system is constructed. The objective function of the distribution-traffic system model of electric vehicle charging is constructed by combining the system power purchase cost _CE , the distribution network facility redundant cost _CVD , the charging facility redundant cost _VC and the vehicle travel cost _CD . The expression of the objective function is:

min C _E +C _VD +C _VC +C _D ;

Calculate the cost of each item of the system, and calculate the system power purchase cost _CE , the distribution network facility redundancy cost _CVD , the charging facility redundancy cost _VC and the vehicle travel cost _CD respectively. The expression of each item of the system cost is:

In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CL _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, CA _e is the installed capacity of the ordinary vehicle charging pile connected to the distribution network node e, CB _e is the installed capacity of the bus charging pile connected to the distribution network node e, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l in time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of ordinary vehicle charging piles per unit capacity;

The charging demand allocation constraint is calculated, and the charging demand allocation constraint is constructed through the relationship constraint between the total charging demand and various charging demands, the road traffic capacity constraint, the total amount constraint of various charging demands in various time periods and start-end point pairs, and the planning constraints of various charging demands. The expression of the charging demand allocation constraint is:

In the above formula, is the total traffic flow generated by charging demand on road l during time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _t,l is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _t,od is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _t,od is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _t,od is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _t,od is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is the parameter of whether there is a bus charging station at road l. When it is 1, it means that there are ordinary charging stations and bus charging stations at road l. δ _M is the big M constant in the big M method;

The charging station service constraint is calculated and constructed through the power balance constraint and the service capacity constraint of the ordinary vehicle charging station and the bus charging station. The expression of the charging station service constraint is:

In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the node e of the distribution network from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EV is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, _ηA is the energy transmission efficiency of the ordinary vehicle charging pile, _ηB is the energy transmission efficiency of the bus charging pile, εe _,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of the ordinary vehicle charging pile, _θB is the power factor angle of the bus charging pile, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles configured at the node e of the distribution network, _PA is the single pile rated power of the ordinary vehicle charging pile, and _PB is the single pile rated power of the bus charging pile;

The distribution network constraints are calculated and constructed through the constraints on the power output of the distribution network to the charging station, the power balance constraints of the distribution network lines, the power upper limit constraints of the distribution network lines and the voltage constraints of the distribution network busbars. The expression of the distribution network constraints is:

In the above formula, c is the distribution line connected to the distribution network node e, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the distribution network node e in time period t, is the reactive basic load connected to the node e of the distribution network in time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _t,c is the voltage drop on the distribution network line c in time period t, U _t,a is the bus voltage of the distribution network node a in time period t, U _t,b is the bus voltage of the distribution network node b in time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage;

Construct an electric vehicle charging system guidance model, combining the construction of an electric vehicle charging system guidance objective function, system sub-item costs, charging demand allocation constraints, charging station service constraints and distribution network constraints to construct an electric vehicle charging system guidance model;

Through simulation calculation, the objective function is solved to obtain the minimum operating cost of the electric vehicle charging system, and the spatiotemporal distribution of the charging load is optimized and reconstructed through the minimum operating cost of the charging system, and the optimized spatiotemporal distribution plan of the charging load is output.

Embodiment 3:

Referring to FIG3 , an electric vehicle charging guidance system based on a power distribution-transportation system, the guidance system includes a model building module and a simulation calculation module;

A model building module is used to build a distribution-transportation system model for electric vehicle charging. The objective function of the electric vehicle charging system guidance model is to minimize the system operation cost. The constraints include charging demand allocation constraints, charging station service constraints, and distribution network constraints.

The expression of the objective function is:

min C _E +C _VD +C _VC +C _D ;

In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CT _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, CA _e is the installed capacity of the ordinary vehicle charging pile connected to the distribution network node e, CB _e is the installed capacity of the bus charging pile connected to the distribution network node e, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l during time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of a charging pile for ordinary vehicles per unit capacity.

The expression of the charging demand allocation constraint is:

In the above formula, is the total traffic flow generated by charging demand on road l during time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _{t, l} is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _{t, od} is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _{t, od} is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _{t, od} is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _{t, od} is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is the parameter of whether there is a bus charging station at road l. When it is 1, it means that there are ordinary charging stations and bus charging stations at road l. δ _M is the big M constant in the big M method;

The expression of the charging station service constraint is:

In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the node e of the distribution network from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EV is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, _ηA is the energy transmission efficiency of the ordinary vehicle charging pile, _ηB is the energy transmission efficiency of the bus charging pile, εe _,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of the ordinary vehicle charging pile, _θB is the power factor angle of the bus charging pile, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles configured at the node e of the distribution network, _PA is the single pile rated power of the ordinary vehicle charging pile, and _PB is the single pile rated power of the bus charging pile;

The expression of the distribution network constraint is:

In the above formula, c is the distribution line connected to the distribution network node e, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the node e of the distribution network in time period t, is the reactive basic load connected to the node e of the distribution network within the time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _t,c is the voltage drop on the distribution network line c within the time period t, U _t,a is the bus voltage of the distribution network node a within the time period t, U _t,b is the bus voltage of the distribution network node b within the time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage.

The simulation calculation module is used to perform simulation calculations using the constructed distribution-traffic system model for electric vehicle charging, and output an optimized spatiotemporal distribution plan of the charging load.

Embodiment 4:

Referring to FIG4 , an electric vehicle charging guidance device based on a power distribution-transportation system, the device includes a processor and a memory;

The memory is used to store computer program code and transmit the computer program code to the processor;

The processor is used to execute an electric vehicle charging guidance method based on a power distribution-transportation system according to instructions in the computer program code.

Embodiment 5:

A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for guiding charging of an electric vehicle based on a power distribution-transportation system is implemented.

The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims (6)

1. A method for guiding electric vehicle charging based on a power distribution-transportation system, characterized in that: The boot method comprises the following steps: S1, constructing an electric vehicle charging system guidance model, wherein the objective function of the electric vehicle charging system guidance model is to minimize the system comprehensive cost, and the constraints include charging demand allocation constraints, charging station service constraints and distribution network constraints; The charging demand allocation constraints include the relationship constraints between the total charging demand and various charging demands, the planning constraints on various charging demands, and the total amount constraints of various charging demands corresponding to various time periods and starting and ending points. The charging station service constraints include power balance constraints and service capacity constraints for ordinary vehicle charging stations and bus charging stations. The distribution network constraints include constraints on the power output of the distribution network to the charging station, power balance constraints on the distribution network lines, power upper limit constraints on the distribution network lines, and voltage constraints on the distribution network busbars. The expression of the objective function is: min C _E +C _VD +C _VC +C _D ; In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost; The expressions of the system electricity purchase cost _CE , the distribution network facility redundancy cost _CVD , the charging facility redundancy cost _VC and the vehicle travel cost _CD are as follows: In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CT _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, CA _e is the installed capacity of the ordinary vehicle charging pile connected to the distribution network node e, CB _e is the installed capacity of the bus charging pile connected to the distribution network node e, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l in time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of ordinary vehicle charging piles per unit capacity; The expression of the charging station service constraint is: In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the node e of the distribution network from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EV is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, _ηA is the energy transmission efficiency of the ordinary vehicle charging pile, _ηB is the energy transmission efficiency of the bus charging pile, εe _,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of the ordinary vehicle charging pile, _θB is the power factor angle of the bus charging pile, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles at the node e of the distribution network, _PA is the single pile rated power of the ordinary vehicle charging pile, _PB is the single pile rated power of the bus charging pile, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric logistics vehicle traveling via path k for the starting and ending points in time period t; The expression of the distribution network constraint is: In the above formula, c is the distribution line connected to the distribution network node w, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the node e of the distribution network in time period t, is the reactive basic load connected to the node e of the distribution network in time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _{t, c} is the voltage drop on the distribution network line c in time period t, U _{t, a} is the bus voltage of the distribution network node a in time period t, U _{t, a} is the bus voltage of the distribution network node b in time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage; S2, based on the constructed electric vehicle charging system guidance model, performs simulation calculations and outputs the optimized spatiotemporal distribution plan of the charging load. 2. The method for guiding electric vehicle charging based on a power distribution-transportation system according to claim 1, characterized in that: The expression of the charging demand allocation constraint is: In the above formula, is the total traffic flow generated by charging demand on road l during time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _t,l is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _t,od is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _t,od is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _t,od is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _t,od is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is a parameter indicating whether a bus charging station is set up at road l. When it is 1, it indicates that a common charging station and a bus charging station are set up at road l. δ _M is the big M constant in the big M method. 3. An electric vehicle charging guidance system based on a power distribution-transportation system, characterized in that: The guidance system includes a model building module and a simulation calculation module; A model building module is used to build a distribution-transportation system model for electric vehicle charging. The objective function of the electric vehicle charging system guidance model is to minimize the system operation cost. The constraints include charging demand allocation constraints, charging station service constraints, and distribution network constraints. The expression of the objective function is: _minCE + _CVD + _VC + _CD ; In the above formula, _CE is the system electricity purchase cost, _CVD is the redundancy cost of distribution network facilities, _CVC is the redundancy cost of charging facilities, and _CD is the vehicle travel cost. is the unit price of electricity purchase cost in period t, is the power support obtained by the charging facility at the distribution network node e from the power grid during the period t, T _UN is the unit time, CT _e is the installed capacity of the distribution transformer at the distribution network node e, is the power support obtained by the charging facility at the node e of the distribution network from the power grid during the period t, PR _CT is the investment cost of the distribution transformer per unit capacity, T _ST is the operating life of the distribution transformer, CL _c is the capacity of the distribution line c, is the active power transmitted by the distribution line c in time period t, PR _CL is the investment cost of the distribution line, T _SL is the operating life of the distribution line, is the charging power of the ordinary vehicle charging pile at the node e of the distribution network in time period t, is the charging power of the bus charging pile at the node e of the distribution network in time period t, _TCA is the operating life of the ordinary vehicle charging pile, _TCB is the operating life of the bus charging pile, is the total traffic flow generated by charging demand on road l in time period t; L _l is the length of road l, PR _D is the driving cost per unit distance, and PR _CA is the investment cost of ordinary vehicle charging piles per unit capacity; The charging demand allocation constraints include the relationship constraints between the total charging demand and various charging demands, the planning constraints on various charging demands, and the total amount constraints of various charging demands corresponding to various time periods and starting and ending points. The charging station service constraints include power balance constraints and service capacity constraints for ordinary vehicle charging stations and bus charging stations. The distribution network constraints include constraints on the power output of the distribution network to the charging station, power balance constraints on the distribution network lines, power upper limit constraints on the distribution network lines, and voltage constraints on the distribution network busbars. The expression of the charging station service constraint is: In the above formula, is the active power support obtained by each type of charging station at the node e of the distribution network from the power grid during time period t, is the reactive power support obtained by various types of charging stations at the distribution network node e from the power grid during time period t, _ES is the average energy demand of a single charge of an electric private car, _EC is the average energy demand of a single charge of an electric taxi, _EG is the average energy demand of a single charge of an electric bus, _EW is the average energy demand of a single charge of an electric logistics vehicle, _TUN is the unit time, _ηA is the energy transmission efficiency of ordinary vehicle charging piles, _ηB is the energy transmission efficiency of bus charging piles, _εe,l is the association matrix between the distribution network node e and the road l in the traffic network, _θA is the power factor angle of ordinary vehicle charging piles, _θB is the power factor angle of bus charging piles, _CAe is the installed capacity of ordinary vehicle charging piles connected to the distribution network node e, _CBe is the installed capacity of bus charging piles connected to the distribution network node e, is the number of common vehicle charging piles configured at the distribution network node e, is the number of bus charging piles at the node e of the distribution network, _PA is the single pile rated power of the ordinary vehicle charging pile, _PB is the single pile rated power of the bus charging pile, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period t, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric logistics vehicle traveling via path k for the starting and ending points in time period t; The expression of the distribution network constraint is: In the above formula, c is the distribution line connected to the distribution network node e, is the active power transmitted by distribution line c during time period t, is the reactive power transmitted by distribution line c during period t, is the active basic load connected to the distribution network node e in time period t, is the reactive basic load connected to the node e of the distribution network in time period t, CL _c is the capacity of the distribution line c, r _c is the resistance of the distribution network line c, x _c is the reactance of the distribution network line c, ΔU _t,c is the voltage drop on the distribution network line c in time period t, U _t,a is the bus voltage of the distribution network node a in time period t, U _t,b is the bus voltage of the distribution network node b in time period t, nodes a and b are the two endpoints of the distribution network line c respectively, U _N is the rated voltage of the distribution network bus, U _m is the upper limit of the distribution network bus voltage, and U _M is the lower limit of the distribution network bus voltage; The simulation calculation module is used to perform simulation calculations using the constructed distribution-traffic system model for electric vehicle charging, and output an optimized spatiotemporal distribution plan of the charging load. 4. The electric vehicle charging guidance system based on the power distribution-transportation system according to claim 3 is characterized in that: The expression of the charging demand allocation constraint is: In the above formula, is the total traffic flow on road l due to charging demand in time period t, is the traffic flow generated on road l by the flexible charging demand of electric private cars traveling via path k in time period od, is the traffic flow generated on road l by the flexible charging demand of electric taxis traveling via path k for the starting and ending points in time period t, is the traffic flow generated on road l by the flexible charging demand of the electric bus traveling via path k for the starting and ending points in time period t, is the traffic flow generated by the flexible charging demand of the electric logistics vehicle traveling via path k at the starting and ending point pair od in period t on road l, BF _t,l is the basic traffic flow at road l in period t, CF _l is the traffic capacity of road l, DS _t,od is the total charging demand of electric private cars at the starting and ending point pair od in period t, DC _t,od is the total charging demand of electric taxis at the starting and ending point pair od in period t, DG _t,od is the total charging demand of electric buses at the starting and ending point pair od in period t, DW _t,od is the total charging demand of electric logistics vehicles at the starting and ending point pair od in period t, is the parameter of whether there is a common charging station at road l, is a parameter indicating whether a bus charging station is set up at road l. When it is 1, it indicates that a common charging station and a bus charging station are set up at road l. δ _M is the big M constant in the big M method. 5. An electric vehicle charging guidance device based on a power distribution-transportation system, characterized in that: The device includes a processor and a memory; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the electric vehicle charging guidance method based on the power distribution-transportation system according to claim 1 or 2 according to the instructions in the computer program code. 6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the electric vehicle charging guidance method based on the power distribution-transportation system as claimed in claim 1 is implemented.