Automatic driving electric vehicle charging optimization access method and system considering remote charging

Abstract

The invention discloses an automatic driving electric vehicle charging optimization access method and system considering remote charging, and the method comprises the steps: building a charging optimization access model of an automatic driving electric vehicle for an electric power-traffic coupling network, considering the remote charging of the automatic driving electric vehicle, and taking the minimization of the comprehensive charging cost of the automatic driving electric vehicle as a target; and solving the charging optimization access model of the automatic driving electric vehicle to obtain a charging optimization access scheme including a charging facility optimization configuration scheme of the automatic driving electric vehicle and a charging optimization strategy of the automatic driving electric vehicle in an idle period. According to the method, photovoltaic output is integrated into the charging optimization access model of the automatic driving electric vehicle for the commuting type automatic driving electric vehicle with the daily travel rule, the charging time and place of the automatic driving electric vehicle in the idle period are guided, the power flow and the traffic flow of the power distribution network are optimized, and meanwhile the charging efficiency is improved. The comprehensive charging cost of the automatic driving electric vehicle is reduced, and the on-site consumption capability of new energy is improved.

Description

Technical Field

[0001] The present invention belongs to the field of charging optimization access strategy for autonomous driving electric vehicles, and specifically relates to a charging optimization access method and system for autonomous driving electric vehicles taking into account remote charging. Background Art

[0002] In recent years, the development of electric vehicles has made great progress, and its large-scale popularization and application have greatly increased the demand for charging facilities. With the increase in charging demand load, the pressure on the power grid continues to rise, especially in some old urban areas, where the power grid infrastructure is outdated, the end of the line does not have the conditions for configuring charging facilities or there are not enough charging facilities, which has aggravated the charging pressure of electric vehicles. In these areas, improving the carrying capacity of the power grid requires a lot of infrastructure transformation, which consumes huge manpower and material resources and is difficult to achieve in the short term.

[0003] In addition, many electric vehicle users do not have private charging piles, and they can only use public charging stations for charging. However, due to the uneven distribution of public charging stations, users often need to spend extra time going to charging stations for charging, which not only increases the user's travel time cost, but may also cause congestion at charging stations during peak hours, further affecting the user's experience of electric vehicles and charging efficiency.

[0004] With the advancement of self-driving electric vehicle technology, the fact that it does not require human driving makes the choice of charging time and location more flexible, especially for commuting trips with regular travel times and long idle periods of the vehicle, such as commuting to and from get off work. Self-driving electric vehicles can go to the charging station to charge by themselves during idle periods and return before the owner needs the car. This can not only improve the utilization efficiency of charging facilities, but also reduce the user's charging time, providing new possibilities for optimizing the layout of charging facilities and optimizing charging guidance. Summary of the invention

[0005] The purpose of the present invention is to provide a charging optimization access method and system for an autonomous driving electric vehicle taking into account remote charging in order to address the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention proposes a charging optimization access method for an autonomous driving electric vehicle considering remote charging, targeting a power-transportation coupling network, comprising:

[0008] S1. Considering the off-site charging of autonomous electric vehicles, with the goal of minimizing the comprehensive charging cost of autonomous electric vehicles, a charging optimization access model for autonomous electric vehicles is constructed;

[0009] S2. Solve the aforementioned charging optimization access model to obtain a charging optimization access plan, which includes an optimal configuration plan for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods.

[0010] In S1, the objective function of the charging optimization access model includes:

[0011] min C P [(1+α) γ -1] / αγ 2 +C E +C T +C A ;

[0012]

[0013]

[0014] C T =(T D +T C )PR T d;

[0015]

[0016] In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C E is the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C Charging time for vehicles that are charged outside of commuting trips, PR T is the unit time cost, L t，od，a is the incremental travel of strategy a in the commuting trip starting at time period t and with origin and destination pair od, PR A is the cost per unit mileage in the incremental trip, and a is the strategy of the autonomous electric vehicle to go to another place to charge during the idle period;

[0017] The constraints of the charging optimization access model include traffic constraints, charging constraints, and distribution network operation constraints considering photovoltaic output.

[0018] The traffic constraints include the number of autonomous electric vehicles with charging needs in the traffic flow and the traffic constraints of charging at other locations during idle time;

[0019] The charging constraints include time window constraints for off-site charging during idle periods, minimum proportion constraints for completing charging during commuting trips, and charging behavior constraints outside commuting trips;

[0020] The number of autonomous electric vehicles with charging requirements in the traffic flow is constrained as follows:

[0021]

[0022] The traffic constraints for charging in different places during the idle period are:

[0023]

[0024] In the above formula, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, τ D The proportion of autonomous electric vehicles that need charging among all vehicles; BF tb，od，a is the traffic flow generated by the vehicle returning from the charging station after the commuting trip of the starting and ending point pair od using strategy a is completed, and tb is the end time of the commuting trip;

[0025] The time window constraint for remote charging during the idle period is:

[0026]

[0027]

[0028] T CA =E D / P CN ;

[0029]

[0030] The minimum proportion constraint for completing charging during the commuting trip is:

[0031]

[0032] The charging behavior constraints outside the commuting trip include:

[0033] Charging demand constraints for additional charging trips:

[0034]

[0035] Charging location constraints for additional charging trips:

[0036]

[0037] Driving time constraints for additional charging trips:

[0038]

[0039] Charging time constraints for additional charging trips:

[0040]

[0041] In the above formula, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，a is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, v A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN is the rated charging power of the autonomous electric vehicle; D The minimum proportion of EVs that need to be charged during their commute is the number of EVs that need to be charged. The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, is a binary variable for determining candidate charging station nodes, T D The time it takes to travel to a charging station for vehicles that are charged outside of their commuting trips. Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l, T C Charging a vehicle outside of a commute takes time.

[0042] The distribution network operation constraints considering photovoltaic output include electric vehicle charging station power constraints, power balance constraints, photovoltaic output constraints, distribution network line capacity constraints, and distribution network node voltage constraints;

[0043] The power constraint of the electric vehicle charging station is:

[0044]

[0045] The power balance constraint is:

[0046]

[0047]

[0048] The photovoltaic output constraint is:

[0049]

[0050] The distribution network line capacity constraint is:

[0051]

[0052] The distribution network node voltage constraint is:

[0053]

[0054]

[0055]

[0056] In the above formula, is the active power output of the distribution network node e in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t. is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement for autonomous electric vehicles, is the correlation coefficient between the transportation network node n and the distribution network node e, TU is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the node e in the distribution network during time period t.

[0057] The traffic constraints also include total traffic flow constraints, and the charging constraints also include charging service capacity constraints and charging pile configuration constraints;

[0058] The total traffic flow constraint is:

[0059]

[0060]

[0061] In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FC l is the upper limit of traffic capacity of road l;

[0062] The charging service capability constraint is:

[0063]

[0064] The configuration constraints of the charging pile are:

[0065]

[0066]

[0067]

[0068] In the above formula, EL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement of autonomous electric vehicles, CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, P CN is the rated charging power of the autonomous electric vehicle, T U is the unit time, MP n is the upper limit of the number of charging piles configured at traffic node n, is a binary variable for determining candidate charging station nodes, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, and MS is the upper limit of the number of charging stations.

[0069] In a second aspect, the present invention proposes an optimized charging access system for autonomous electric vehicles considering off-site charging, targeting a power-transportation coupling network, including an optimization model building module and an optimization model solving module;

[0070] The optimization model building module is used to consider the off-site charging of the autonomous electric vehicle, and to build a charging optimization access model for the autonomous electric vehicle with the goal of minimizing the comprehensive charging cost of the autonomous electric vehicle;

[0071] The optimization model solving module is used to solve the aforementioned charging optimization access model to obtain a charging optimization access plan, which includes an optimization configuration plan for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods.

[0072] The optimization model construction module includes an objective function construction unit;

[0073] The objective function construction unit is used to construct the objective function of the following charging optimization access model:

[0074] min C P [(1+α) γ -1] / αγ 2 +C E +C T +C A ;

[0075]

[0076]

[0077] C T =(T D +T C )PR T d;

[0078]

[0079] In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C E is the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C Charging time for vehicles that are charged outside of commuting trips, PR T is the unit time cost, L t，od，a is the incremental travel of strategy a in the commuting trip starting at time period t and with origin and destination pair od, PR Ais the cost per unit mileage in the incremental trip, and a is the strategy of the autonomous electric vehicle to go to another place to charge during the idle period;

[0080] The optimization model construction module also includes a traffic constraint construction unit, a charging constraint construction unit, and a distribution network operation constraint construction unit considering photovoltaic output.

[0081] The traffic constraint construction unit is used to construct the number constraint of autonomous driving electric vehicles with charging demand in the traffic flow and the traffic constraint of charging in different places during the idle period;

[0082] The number of autonomous electric vehicles with charging requirements in the traffic flow is constrained as follows:

[0083]

[0084] The traffic constraints for charging in different places during the idle period are:

[0085]

[0086] In the above formula, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, τ D The proportion of autonomous electric vehicles that need charging among all vehicles; BF tb，od，a is the traffic flow generated by the vehicle returning from the charging station after the commuting trip of the starting and ending point pair od using strategy a is completed, and tb is the end time of the commuting trip;

[0087] The charging constraint construction unit is used to construct a time window constraint for off-site charging during an idle period, a minimum proportion constraint for completing charging during a commuting trip, and a charging behavior constraint outside a commuting trip;

[0088] The time window constraint for remote charging during the idle period is:

[0089]

[0090]

[0091] T CA =E D / P CN ;

[0092]

[0093] The minimum proportion constraint for completing charging during the commuting trip is:

[0094]

[0095] The charging behavior constraints outside the commuting trip include:

[0096] Charging demand constraints for additional charging trips:

[0097]

[0098] Charging location constraints for additional charging trips:

[0099]

[0100] Driving time constraints for additional charging trips:

[0101]

[0102] Charging time constraints for additional charging trips:

[0103]

[0104] In the above formula, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，a is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, v A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN is the rated charging power of the autonomous electric vehicle; D The minimum proportion of EVs that need to be charged during their commute is the number of EVs that need to be charged. The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, is a binary variable for determining candidate charging station nodes, T D The time it takes to travel to a charging station for vehicles that are charged outside of their commuting trips. Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l, T C Charging a vehicle outside of a commute takes time.

[0105] The distribution network operation constraint construction unit considering photovoltaic output is used to construct electric vehicle charging station power constraints, power balance constraints, photovoltaic output constraints, distribution network line capacity constraints, and distribution network node voltage constraints;

[0106] The power constraint of the electric vehicle charging station is:

[0107]

[0108] The power balance constraint is:

[0109]

[0110]

[0111] The photovoltaic output constraint is:

[0112]

[0113] The distribution network line capacity constraint is:

[0114]

[0115] The distribution network node voltage constraint is:

[0116]

[0117]

[0118]

[0119] In the above formula, is the active power output of the distribution network node e in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t. is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement for autonomous electric vehicles, is the correlation coefficient between the transportation network node n and the distribution network node e, T U is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the node e in the distribution network during time period t.

[0120] The traffic constraint construction unit is further used to construct the total traffic flow constraint, and the charging constraint construction unit is further used to construct the charging service capacity constraint and the configuration constraint of the charging pile;

[0121] The total traffic flow constraint is:

[0122]

[0123]

[0124] In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FC l is the upper limit of traffic capacity of road l;

[0125] The charging service capability constraint is:

[0126]

[0127] The configuration constraints of the charging pile are:

[0128]

[0129]

[0130]

[0131] In the above formula, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement of autonomous electric vehicles, CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, P CN is the rated charging power of the autonomous electric vehicle, T U is the unit time, MP n is the upper limit of the number of charging piles configured at traffic node n, is a binary variable for determining candidate charging station nodes, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, and MS is the upper limit of the number of charging stations.

[0132] Compared with the prior art, the present invention has the following beneficial effects:

[0133] The present invention proposes a charging optimization access method and system for autonomous driving electric vehicles considering off-site charging. The method considers off-site charging of autonomous driving electric vehicles for the power-transportation coupling network, and constructs a charging optimization access model for autonomous driving electric vehicles with the goal of minimizing the comprehensive charging cost of autonomous driving electric vehicles. By solving the charging optimization access model for autonomous driving electric vehicles, an optimal configuration scheme for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods are obtained. On the one hand, the method considers off-site charging of autonomous driving electric vehicles for commuting autonomous driving electric vehicles with daily travel rules, constructs a charging optimization access model for autonomous driving electric vehicles, guides the charging time and location of autonomous driving electric vehicles during idle periods, optimizes the distribution network trend and traffic flow, and reduces the comprehensive charging cost of autonomous driving electric vehicles; on the other hand, the method integrates photovoltaic output into the charging optimization access model for autonomous driving electric vehicles, and optimizes the site selection and capacity setting scheme of charging facilities for autonomous driving electric vehicles in the power-transportation coupling network, so as to reduce the setting of charging facilities at weak nodes of the power grid and increase the configuration of charging facilities at nodes with rich photovoltaic resources without affecting the charging demand of electric vehicles, thereby reducing the impact of charging load on the power grid and improving the local consumption capacity of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 This is a topological diagram of the power-transportation coupling network described in Example 1.

[0135] Figure 2 This is the photovoltaic output curve diagram used in Example 1.

[0136] Figure 3 This is the time-of-use electricity price diagram used in Example 1.

[0137] Figure 4 The present invention is a flowchart of the method.

[0138] Figure 5 This is a commuting itinerary diagram of the autonomous driving electric vehicle described in Example 1.

[0139] Figure 6 This is a schematic diagram of the charging facility planning results of the power-transportation coupling network described in Example 1.

[0140] Figure 7 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0141] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0142] The present invention proposes a charging optimization access method and system for autonomous driving electric vehicles taking into account off-site charging. For commuting autonomous driving electric vehicles with regular daily travel patterns, three charging modes of autonomous driving electric vehicles are considered, including on-site charging during idle periods of commuting, off-site charging during idle periods of commuting, and charging outside the commuting. With the goal of minimizing the comprehensive charging cost of the autonomous driving electric vehicle, a charging optimization access model for the autonomous driving electric vehicle is constructed. By solving the charging optimization access model for the autonomous driving electric vehicle, the charging guidance strategy for the autonomous driving electric vehicle during idle periods, as well as the site selection and sizing scheme of charging facilities for the autonomous driving electric vehicle in the power-transportation coupling network are optimized.

[0143] Embodiment 1:

[0144] This embodiment takes a 14-node distribution network and a 23-node transportation network as the research object, and its topological structure is as follows: Figure 1 As shown in Figure 2, the charging optimization method for autonomous electric vehicles is used to determine the optimization plan for charging facilities in the power-traffic coupling network and the charging strategy for autonomous electric vehicles during idle periods. The photovoltaic output curve used is as follows: Figure 2 As shown in Figure 3 As shown. The parameters are set as follows: the investment period is 10 years, the discount rate is 0.05, the unit time is 1 hour, the typical days in a year are 365 days, the rated voltage of the distribution network is 10 kV, the upper and lower limits of the distribution network bus voltage are 10.5 kV and 9.5 kV respectively, the maximum power of a single charging pile is 80 kW, the average charging energy demand of electric vehicles is 40 kWh, the configuration cost of a single autonomous electric vehicle charging pile is 15,000 yuan, the unit time cost is 20 yuan / hour, the unit cost consumed by the unit distance traveled in the incremental journey is 0.5 yuan / kilometer, the average driving speed of autonomous electric vehicles in the incremental journey is 40 kilometers / hour, the proportion of all autonomous electric vehicles with charging needs is 20%, the minimum proportion of electric vehicles that complete charging within the commuting journey to all electric vehicles that need to be charged is 60%, and the upper limit of the number of charging stations is 3.

[0145] like Figure 4 As shown, a charging optimization access method for an autonomous driving electric vehicle considering off-site charging is performed in the following steps:

[0146] 1. For commuting autonomous electric vehicles with regular daily travel patterns, consider the off-site charging of autonomous electric vehicles in the power-traffic coupling network, and build a charging optimization model for autonomous electric vehicles with the goal of minimizing the comprehensive charging cost of autonomous electric vehicles;

[0147] like Figure 5As shown in the figure, the complete commuting trip of the autonomous electric vehicle includes the driving process from the starting point to the end point and the idle period until the user of the autonomous electric vehicle is ready to leave the end point and start the next trip. Three charging modes of the autonomous electric vehicle are considered, including on-site charging during the idle period of the commuting trip, off-site charging during the idle period of the commuting trip, and charging outside the commuting trip. The regular car usage habits of the commuting trip are used to achieve off-site charging without affecting the user's use of the car.

[0148] During idle time, the self-driving electric vehicle can be charged on the spot after the owner leaves the vehicle or automatically drive to another location for charging, thus not consuming the owner's extra time. The mileage of the self-driving electric vehicle that goes to another location for charging is the incremental travel of the electric vehicle.

[0149] Some self-driving electric vehicles that need charging cannot be charged during their spare time due to limited charging facilities. They need to go to the charging station to charge after the commuting trip, which results in an extra charging trip and consumes extra time costs.

[0150] In view of this, the charging optimization access model of autonomous electric vehicles is set as shown in equations (1)-(5):

[0151] min C P [1+α) γ -1] / αγ 2 +C E +C T +C A Formula (1);

[0152]

[0153]

[0154] C T =(T D +T C )PR T d Formula (4);

[0155]

[0156] Formula (1) is the objective function of the charging optimization access model, Formula (2) is the configuration cost of the charging facilities of the autonomous electric vehicle, Formula (3) is the cost of purchasing electricity from the superior power grid, Formula (4) is the cost of the additional charging time, and Formula (5) is the driving cost of the incremental trip, where the incremental trip refers to the mileage of the autonomous electric vehicle that goes to a different place for charging. In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C Eis the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C Charging time for vehicles that are charged outside of commuting trips, PR T is the unit time cost, L t，od，a is the incremental travel of strategy a in the commuting trip starting in time period t with the origin and destination pair od, PR A is the cost per unit mileage in the incremental trip, a is the strategy of the autonomous electric vehicle to go to a different place for charging during idle time, that is, what route is taken to where to charge;

[0157] The traffic constraints of the charging optimization access model include:

[0158]

[0159]

[0160]

[0161]

[0162] In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FCl is the upper limit of the traffic capacity of road l, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, τ D The proportion of autonomous electric vehicles that need charging among all vehicles, BF tb，od，a is the traffic flow generated when the vehicle returns from the charging station after the commuting trip using strategy a for the start-end point pair od ends, tb is the end time of the commuting trip, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，a is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, v A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN Rated charging power for autonomous electric vehicles;

[0163] In this constraint, equations (6)-(7) are the total traffic flow constraints on each road in each period, equation (8) is the number constraint of autonomous electric vehicles with charging needs in the traffic flow, and equation (9) is the traffic constraint for round trips of charging at different locations during idle periods;

[0164] The charging constraints of the charging optimization access model include:

[0165]

[0166]

[0167] T CA =E D / P CN Formula (12);

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] In the above formula, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, T U is the unit time, κ D The proportion of self-driving electric vehicles that need to be charged during commuting trips is the smallest among all electric vehicles that need to be charged. is a binary variable for determining candidate nodes of charging stations. When , it means that a charging pile can be configured at the traffic node n, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, MS is the upper limit of the number of charging stations, Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l;

[0179] In this constraint, equations (10)-(13) are the time window constraints for off-site charging during the idle period, that is, the driving time and charging time of off-site charging shall not exceed the total duration of the idle period, equation (14) is the charging service capacity constraint, equation (15) is the minimum proportion constraint for autonomous electric vehicles to complete charging during the commuting trip, equations (16)-(18) are the configuration constraints of charging piles, and equations (19)-(22) are the charging behavior constraints outside the commuting trip, among which equation (19) is the charging demand constraint for the additional charging trip, equation (20) is the charging location constraint for the additional charging trip, equation (21) is the driving time constraint for the additional charging trip, and equation (22) is the charging time constraint for the additional charging trip;

[0180] The charging optimization model considers the distribution network operation constraints of photovoltaic output, takes photovoltaic output into account in the power balance, and improves the energy absorption capacity through the coordination of charging load and photovoltaic output, including:

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189] In the above formula, is the correlation coefficient between the transportation network node n and the distribution network node e, T U is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the distribution network node e in time period t;

[0190] In this constraint, equation (23) is the power constraint of the electric vehicle charging station, equations (24)-(25) are the power balance constraints, equation (26) is the photovoltaic output constraint, equation (27) is the distribution network line capacity constraint, and equations (28)-(30) are the voltage constraints of the distribution network nodes.

[0191] 2. By solving the above-mentioned charging optimization access model for autonomous electric vehicles, the distribution network lines and traffic network road expansion plans for autonomous electric vehicle charging facilities are obtained as follows: Figure 6 As shown, the three configured charging stations are located at P3 (T5), P4 (T7) and P11 (T17), with 18, 13 and 21 charging piles respectively.

[0192] In order to verify the effectiveness of the present invention, the method proposed in the present invention (method 1) and the method (method 2) that does not consider the off-site charging of the autonomous driving electric vehicle during the idle period, that is, all the autonomous driving electric vehicles are charged through extra trips, are applied to the 14-node distribution network-23-node transportation network example for comparison, and the comparison of their economic indicators is shown in Table 1;

[0193] Table 1 Comparison of economic indicators

[0194]

[0195] As can be seen from Table 1, the annual comprehensive charging cost of the method of the present invention is relatively lower, reducing by 10.58%. Specifically, in terms of annual electricity purchase cost, method 1 reduces 9.34% compared with method 2; in terms of annual additional charging time cost, method 1 reduces 17.46% compared with method 2; in terms of annual incremental travel cost, method 1 reduces 27.98% compared with method 2.

[0196] The above results show that the method proposed in the present invention achieves the effect of minimizing the comprehensive charging cost of the autonomous driving electric vehicle without affecting the charging demand of the electric vehicle.

[0197] Embodiment 2:

[0198] like Figure 7 As shown, the charging optimization access system for autonomous driving electric vehicles considering off-site charging is aimed at the power-transportation coupling network, including an optimization model building module and an optimization model solving module;

[0199] The optimization model building module is used to consider the off-site charging of the autonomous electric vehicle, and to build a charging optimization access model for the autonomous electric vehicle with the goal of minimizing the comprehensive charging cost of the autonomous electric vehicle;

[0200] The optimization model solving module is used to solve the aforementioned charging optimization access model to obtain a charging optimization access plan, which includes an optimization configuration plan for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods.

[0201] The optimization model construction module includes an objective function construction unit;

[0202] The objective function construction unit is used to construct the objective function of the following charging optimization access model:

[0203] min C P [(1+α) γ -1] / αγ 2 +C E +C T +CA ;

[0204]

[0205]

[0206] C T =(T D +T C )PR T d;

[0207]

[0208] In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C E is the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C The time required to charge a vehicle outside of a commuting trip is PRT, which is the unit time cost. t，od，a is the incremental travel of strategy a in the commuting trip starting at time period t and with origin and destination pair od, PR A is the cost per unit mileage in the incremental trip, and a is the strategy of the autonomous electric vehicle to go to another place to charge during the idle period;

[0209] The optimization model construction module includes a traffic constraint construction unit, a charging constraint construction unit, and a distribution network operation constraint construction unit considering photovoltaic output.

[0210] The traffic constraint construction unit is used to construct the total traffic flow constraint, the number constraint of autonomous driving electric vehicles with charging demand in the traffic flow, and the traffic constraint of charging in a different place during the idle period;

[0211] The total traffic flow constraint is:

[0212]

[0213]

[0214] The number of autonomous electric vehicles with charging requirements in the traffic flow is constrained as follows:

[0215]

[0216] The traffic constraints for charging in different places during the idle period are:

[0217]

[0218] In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k of od in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FC l is the upper limit of the traffic capacity of road l, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, τ D The proportion of autonomous electric vehicles that need charging among all vehicles, BF tb，od，a is the traffic flow generated when the vehicle returns from the charging station after the commuting trip using strategy a for the start-end point pair od ends, tb is the end time of the commuting trip, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，a is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, v A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN Rated charging power for autonomous electric vehicles.

[0219] The charging constraint construction unit is used to construct a time window constraint for off-site charging during idle periods, a minimum proportion constraint for completing charging during a commuting trip, a charging behavior constraint outside a commuting trip, a charging service capacity constraint, and a configuration constraint for a charging pile;

[0220] The time window constraint for remote charging during the idle period is:

[0221]

[0222]

[0223] T CA =E D / P CN ;

[0224]

[0225] The minimum proportion constraint for completing charging during the commuting trip is:

[0226]

[0227] The charging behavior constraints outside the commuting trip include:

[0228] Charging demand constraints for additional charging trips:

[0229]

[0230] Charging location constraints for additional charging trips:

[0231]

[0232] Driving time constraints for additional charging trips:

[0233]

[0234] Charging time constraints for additional charging trips:

[0235]

[0236] The charging service capability constraint is:

[0237]

[0238] The configuration constraints of the charging pile are:

[0239]

[0240]

[0241]

[0242] In the above formula, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, T U is the unit time, κ D The proportion of self-driving electric vehicles that need to be charged during commuting trips is the smallest among all electric vehicles that need to be charged. is a binary variable for determining candidate nodes of charging stations. When , it means that a charging pile can be configured at the traffic node n, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, MS is the upper limit of the number of charging stations, Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l.

[0243] The distribution network operation constraint construction unit considering photovoltaic output is used to construct electric vehicle charging station power constraints, power balance constraints, photovoltaic output constraints, distribution network line capacity constraints, and distribution network node voltage constraints;

[0244] The power constraint of the electric vehicle charging station is:

[0245]

[0246] The power balance constraint is:

[0247]

[0248]

[0249] The photovoltaic output constraint is:

[0250]

[0251] The distribution network line capacity constraint is:

[0252]

[0253] The distribution network node voltage constraint is:

[0254]

[0255]

[0256]

[0257] In the above formula, is the correlation coefficient between the transportation network node n and the distribution network node e, T U is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the node e in the distribution network during time period t.

Claims

1. A charging optimization access method for autonomous driving electric vehicles considering remote charging, characterized in that: The method is directed to a power-transportation coupling network, comprising: S1. Considering the off-site charging of autonomous electric vehicles, with the goal of minimizing the comprehensive charging cost of autonomous electric vehicles, a charging optimization access model for autonomous electric vehicles is constructed; S2. Solve the aforementioned charging optimization access model to obtain a charging optimization access plan, which includes an optimal configuration plan for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods.

2. The method for optimizing charging access for an autonomous driving electric vehicle considering remote charging according to claim 1, characterized in that: In S1, the objective function of the charging optimization access model includes: min C P [(1+a) γ -1] / ag 2 +C E +C T +C A ; C T =(T D +T C )PR T d; In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C E is the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C Charging time for vehicles that are charged outside of commuting trips, PR T is the unit time cost, L t，od，a is the incremental travel of strategy a in the commuting trip starting at time period t and with origin and destination pair od, PR A is the cost per unit mileage in the incremental trip, and a is the strategy of the autonomous electric vehicle to go to another place to charge during the idle period; The constraints of the charging optimization access model include traffic constraints, charging constraints, and distribution network operation constraints considering photovoltaic output.

3. The method for optimizing charging access for an autonomous driving electric vehicle considering remote charging according to claim 2, characterized in that: The traffic constraints include the number of autonomous electric vehicles with charging needs in the traffic flow and the traffic constraints of charging at other locations during idle time; The charging constraints include time window constraints for off-site charging during idle periods, minimum proportion constraints for completing charging during commuting trips, and charging behavior constraints outside commuting trips; The number of autonomous electric vehicles with charging requirements in the traffic flow is constrained as follows: The traffic constraints for charging in different places during the idle period are: In the above formula, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k in time period t, is the correlation coefficient between path k and road l, τ D The proportion of autonomous electric vehicles that need charging among all vehicles; BF tb，od，a is the traffic flow generated by the vehicle returning from the charging station after the commuting trip of the starting and ending point pair od using strategy a is completed, and tb is the end time of the commuting trip; The time window constraint for remote charging during the idle period is: T CA =E D / P CN ; The minimum proportion constraint for completing charging during the commuting trip is: The charging behavior constraints outside the commuting trip include: Charging demand constraints for additional charging trips: Charging location constraints for additional charging trips: Driving time constraints for additional charging trips: Charging time constraints for additional charging trips: In the above formula, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，a is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, V A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN is the rated charging power of the autonomous electric vehicle; D The minimum proportion of EVs that need to be charged during their commute is the number of EVs that need to be charged. The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, is a binary variable for determining candidate charging station nodes, T D The time it takes to travel to a charging station for vehicles that are charged outside of their commuting trips. Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l, T C Charging a vehicle outside of a commute takes time.

4. The method for optimizing charging access for an autonomous driving electric vehicle considering remote charging according to claim 2, characterized in that: The distribution network operation constraints considering photovoltaic output include electric vehicle charging station power constraints, power balance constraints, photovoltaic output constraints, distribution network line capacity constraints, and distribution network node voltage constraints; The power constraint of the electric vehicle charging station is: The power balance constraint is: The photovoltaic output constraint is: The distribution network line capacity constraint is: The distribution network node voltage constraint is: In the above formula, is the active power output of the distribution network node e in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t. is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement for autonomous electric vehicles, is the correlation coefficient between the transportation network node n and the distribution network node e, T U is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the node e in the distribution network during time period t.

5. The method for optimizing charging access for autonomous driving electric vehicles considering remote charging according to claim 2 is characterized in that: The traffic constraints also include total traffic flow constraints, and the charging constraints also include charging service capacity constraints and charging pile configuration constraints; The total traffic flow constraint is: In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FC l is the upper limit of traffic capacity of road l; The charging service capability constraint is: The configuration constraints of the charging pile are: In the above formula, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement of autonomous electric vehicles, CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, P CN is the rated charging power of the autonomous electric vehicle, T U is the unit time, MP n is the upper limit of the number of charging piles configured at traffic node n, is a binary variable for determining candidate charging station nodes, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, and MS is the upper limit of the number of charging stations.

6. An optimized charging access system for autonomous electric vehicles considering off-site charging, characterized in that: The system is aimed at the power-transportation coupling network and includes an optimization model building module and an optimization model solving module; The optimization model building module is used to consider the off-site charging of the autonomous electric vehicle, and to build a charging optimization access model for the autonomous electric vehicle with the goal of minimizing the comprehensive charging cost of the autonomous electric vehicle; The optimization model solving module is used to solve the aforementioned charging optimization access model to obtain a charging optimization access plan, which includes an optimization configuration plan for charging facilities of autonomous driving electric vehicles and a charging optimization strategy for autonomous driving electric vehicles during idle periods.

7. The self-driving electric vehicle charging optimization access system considering remote charging according to claim 6 is characterized in that: The optimization model construction module includes an objective function construction unit; The objective function construction unit is used to construct the objective function of the following charging optimization access model: min C P [(1+a) γ -1] / ag 2 +C E +C T +C A ; C T =(T D +T C )PR T d; In the above formula, C P is the configuration cost of charging facilities for autonomous electric vehicles, α is the discount rate, γ is the investment period, and C E is the cost of purchasing electricity from the upper grid, C T The cost of the extra charging time, C A is the driving cost of the incremental trip; CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, PR CP is the configuration cost of a single charging pile, d is the number of typical days in a year, is the active power output of the distribution network node e in time period t, is the photovoltaic output of node e in the distribution network during time period t, is the electricity price in period t, T D The driving time to the charging station for vehicles charging outside the commuting trip, T C Charging time for vehicles that are charged outside of commuting trips, PR T is the unit time cost, L t，od，a is the incremental travel of strategy a in the commuting trip starting at time period t and with origin and destination pair od, PR A is the cost per unit mileage in the incremental trip, and a is the strategy of the autonomous electric vehicle to go to another place to charge during the idle period; The optimization model construction module also includes a traffic constraint construction unit, a charging constraint construction unit, and a distribution network operation constraint construction unit considering photovoltaic output.

8. The self-driving electric vehicle charging optimization access system considering remote charging according to claim 7 is characterized in that: The traffic constraint construction unit is used to construct the number constraint of autonomous driving electric vehicles with charging demand in the traffic flow and the traffic constraint of charging in different places during the idle period; The number of autonomous electric vehicles with charging requirements in the traffic flow is constrained as follows: The traffic constraints for charging in different places during the idle period are: In the above formula, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k in time period t, is the correlation coefficient between path k and road l, τ D The proportion of autonomous electric vehicles that need charging among all vehicles; BF tb，od，a is the traffic flow generated by the vehicle returning from the charging station after the commuting trip of the starting and ending point pair od using strategy a is completed, and tb is the end time of the commuting trip; The charging constraint construction unit is used to construct a time window constraint for off-site charging during an idle period, a minimum proportion constraint for completing charging during a commuting trip, and a charging behavior constraint outside a commuting trip; The time window constraint for remote charging during the idle period is: T CA =E D / P CN ; The minimum proportion constraint for completing charging during the commuting trip is: The charging behavior constraints outside the commuting trip include: Charging demand constraints for additional charging trips: Charging location constraints for additional charging trips: Driving time constraints for additional charging trips: Charging time constraints for additional charging trips: In the above formula, is the driving time consumed by the incremental trip using strategy a in the commuting trip starting from the start-end point pair od in time period t, T CA is the average charging time for electric vehicles, is the length of the idle time in the commuting trip starting from the starting and ending point pair od in time period t, L t，od，k is the incremental travel of strategy a in the commuting trip starting from the starting and ending point pair od in time period t, v A is the average speed of the autonomous electric vehicle in the incremental trip, E D is the average charging energy requirement of autonomous electric vehicles, P CN is the rated charging power of the autonomous electric vehicle; D The minimum proportion of EVs that need to be charged during their commute is the number of EVs that need to be charged. The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, is a binary variable for determining candidate charging station nodes, T D The time it takes to travel to a charging station for vehicles that are charged outside of their commuting trips. Additional travel paths for off-site charging The correlation coefficient with road l, is the average travel time of road l, T C Charging a vehicle outside of a commute takes time.

9. The self-driving electric vehicle charging optimization access system considering remote charging according to claim 7 is characterized in that: The distribution network operation constraint construction unit considering photovoltaic output is used to construct electric vehicle charging station power constraints, power balance constraints, photovoltaic output constraints, distribution network line capacity constraints, and distribution network node voltage constraints; The power constraint of the electric vehicle charging station is: The power balance constraint is: The photovoltaic output constraint is: The distribution network line capacity constraint is: The distribution network node voltage constraint is: In the above formula, is the active power output of the distribution network node e in time period t, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, AF t，od，a is the traffic flow generated when the self-driving electric vehicle using strategy a for charging at a different location goes to the charging station during the commuting trip between the starting and ending points od in time period t. is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement for autonomous electric vehicles, is the correlation coefficient between the transportation network node n and the distribution network node e, T U is the unit time, are respectively the active power and reactive power on line w at time period t, are the basic active load and reactive load connected to the distribution network node e at time period t, is the photovoltaic output of the distribution network node e in time period t, w is all the lines connected to the distribution network node e, is the maximum photovoltaic output connected to the distribution network node e, LC w is the capacity of the distribution network line w, ΔU t，w is the voltage drop on the distribution network line w in time period t, are the resistance and reactance of the distribution network line w, U N is the rated voltage of the distribution network busbar, U t，a , U t，b are the bus voltages of nodes a and b in the distribution network in time period t, respectively. Nodes a and b are the two endpoints of the distribution network line w, respectively. U m , U M They are the upper and lower limits of the bus voltage of the distribution network, U t，e is the bus voltage of the node e in the distribution network during time period t.

10. The self-driving electric vehicle charging optimization access system considering remote charging according to claim 7 is characterized in that: The traffic constraint construction unit is further used to construct the total traffic flow constraint, and the charging constraint construction unit is further used to construct the charging service capacity constraint and the configuration constraint of the charging pile; The total traffic flow constraint is: In the above formula, FT t，l is the total traffic flow of road l in time period t, F t，od，k is the traffic flow of the starting and ending points on the driving path k in time period t, is the correlation coefficient between path k and road l, AF t，od，a , BF t，od，a They are the traffic flow generated when the self-driving electric vehicle using strategy a charges remotely to the charging station and the traffic flow generated when returning from the charging station in the commuting trip of the starting and ending point pair od in time period t, is the correlation coefficient between the remote charging strategy a and the road l, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. is the starting and ending point pair of the additional trip, is the path of the extra trip, FC l is the upper limit of traffic capacity of road l; The charging service capability constraint is: The configuration constraints of the charging pile are: In the above formula, FL t，od is the number of autonomous electric vehicles charged on-site among the vehicles commuting between the origin and destination points od in time period t, is the correlation coefficient between the origin-destination pair od and the traffic node n, is the correlation coefficient between the remote charging strategy a and the traffic node n, The additional traffic flow generated by charging autonomous electric vehicles after their commuting trips for those that need to charge but are unable to do so during off-peak hours. The starting and ending points of the additional charging trip The correlation coefficient with traffic node n, E D is the average charging energy requirement of autonomous electric vehicles, CP n The number of autonomous electric vehicle charging stations configured for traffic network node n, P CN is the rated charging power of the autonomous electric vehicle, T U is the unit time, MP n is the upper limit of the number of charging piles configured at traffic node n, is a binary variable for determining candidate charging station nodes, δ M is the large M constant, N is the total number of candidate traffic nodes for charging stations, and MS is the upper limit of the number of charging stations.

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