Vehicle network bidirectional energy optimization interaction method and system of automatic driving electric vehicle

Abstract

The invention relates to a vehicle network bidirectional energy optimization interaction method and system for an automatic driving electric vehicle, and the method comprises the steps: firstly constructing a minimum comprehensive charging cost of the automatic driving electric vehicle as a target, and considering a vehicle owner driving mode and an unmanned driving mode of the electric vehicle, and the charging and V2G reverse discharging of the electric vehicle; and then solving the bidirectional energy optimization interaction model to obtain a vehicle network bidirectional energy optimization interaction scheme of the automatic driving electric vehicle. According to the invention, the automatic driving electric vehicle can be utilized to go to the charging station for charging and discharging in the idle period, so that the extra journey of a user can be effectively reduced, the time cost is reduced, the scheduling flexibility of charging and discharging loads is improved, the overall load flexibility of the charging station can be improved, and the cost of purchasing electricity from a superior power grid by the system is reduced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-grid bidirectional energy interaction, and in particular to a vehicle-grid bidirectional energy optimization interaction method and system for an autonomous driving electric vehicle. Background Art

[0002] The concept of bidirectional energy interaction between vehicle and grid originates from the increasingly close connection between electric vehicles and power systems. This interaction mode has gradually attracted attention, especially in response to the pressure on the power grid brought about by the rapid popularization of electric vehicles. With the rapid growth of electric vehicles in the world, traditional power grids are facing unprecedented challenges, such as fluctuations in load peaks and the complexity of power resource allocation. In order to meet these challenges, bidirectional energy interaction technology between vehicle and grid has emerged. It not only allows electric vehicles to obtain energy from the grid for charging, but also enables them to feed the stored electric energy back to the grid during peak hours of grid demand, thereby acting as a distributed energy storage device. This bidirectional energy flow mechanism can effectively alleviate the burden on the grid, balance power supply and demand, and reduce the impact of peak power demand on the grid.

[0003] The progress of autonomous driving technology is also eye-catching. The core of autonomous electric vehicle charging guidance technology is to intelligently select the best charging time and location through seamless communication between the vehicle system and the charging infrastructure. With the continuous iteration of technology, the safety and stability of autonomous driving have been significantly improved. In addition to the daily commuting of vehicle users, it is also necessary to explore the effective application of the advantages of autonomous electric vehicles to the field of two-way energy optimization interaction between the vehicle network. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned problems existing in the prior art and to provide a vehicle-grid bidirectional energy optimization interaction method and system for an autonomous driving electric vehicle.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a vehicle-grid bidirectional energy optimization interaction method for an autonomous driving electric vehicle, comprising:

[0007] S1. Constructing a bidirectional energy optimization interaction model of the power-transportation coupling network, wherein the bidirectional energy optimization interaction model of the power-transportation coupling network aims to minimize the comprehensive charging cost of the autonomous driving electric vehicle, and considers the owner driving mode of the electric vehicle, the unmanned driving mode, and the charging of the electric vehicle and the V2G reverse discharge;

[0008] S2. Solve the bidirectional energy optimization interaction model of the power-transportation coupling network and obtain the bidirectional energy optimization interaction solution for the vehicle-grid of the autonomous electric vehicle.

[0009] The objective function of the two-way energy optimization interaction model of the power-transportation coupling network includes:

[0010] min C E +C B +C T ;

[0011]

[0012] In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

[0013] The bidirectional energy optimization interaction model of the power-transportation coupling network takes into account vehicle charging and discharging guidance constraints, and the vehicle charging and discharging guidance constraints include:

[0014]

[0015]

[0016] In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs; T CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

[0017] The bidirectional energy optimization interaction model of the power-transportation coupling network also considers the charging station operation constraints of the electric vehicle battery throughout its life cycle. The charging station operation constraints include:

[0018]

[0019]

[0020] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0021]

[0022] In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UB The cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

[0023] The two-way energy optimization interaction model of the power-transportation coupling network also considers the power-transportation coupling network constraints, and the power-transportation coupling network constraints include:

[0024]

[0025] In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the node e in the distribution network during time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

[0026] In a second aspect, the present invention proposes a vehicle-grid bidirectional energy optimization interaction system for an autonomous electric vehicle, including a model building module and a model solving module;

[0027] The model building module is used to build a two-way energy optimization interaction model of the power-transportation coupling network, wherein the two-way energy optimization interaction model of the power-transportation coupling network aims to minimize the comprehensive charging cost of the autonomous driving electric vehicle, and takes into account the owner driving mode of the electric vehicle, the unmanned driving mode, and the charging of the electric vehicle and the V2G reverse discharge;

[0028] The model solving module is used to solve the bidirectional energy optimization interaction model of the power-traffic coupling network, and obtain the vehicle-grid bidirectional energy optimization interaction solution of the autonomous driving electric vehicle.

[0029] The objective function of the two-way energy optimization interaction model of the power-transportation coupling network includes:

[0030] min C E +C B +C T ;

[0031]

[0032] In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

[0033] The bidirectional energy optimization interaction model of the power-transportation coupling network takes into account vehicle charging and discharging guidance constraints, and the vehicle charging and discharging guidance constraints include:

[0034]

[0035] In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs; T CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

[0036] The bidirectional energy optimization interaction model of the power-transportation coupling network also considers the charging station operation constraints of the electric vehicle battery throughout its life cycle. The charging station operation constraints include:

[0037]

[0038] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0039]

[0040]

[0041] In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UB The cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

[0042] The two-way energy optimization interaction model of the power-transportation coupling network also considers the power-transportation coupling network constraints, which include:

[0043]

[0044]

[0045] In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the node e in the distribution network during time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The bidirectional energy optimization interaction model of the power-traffic coupling network constructed by the vehicle-grid bidirectional energy optimization interaction method of the self-driving electric vehicle proposed in the present invention takes into account the owner's driving mode, the unmanned driving mode, and the charging and V2G reverse discharge of the electric vehicle. On the one hand, the unmanned driving mode of the self-driving electric vehicle is used to go to the charging station for charging and discharging during idle time, which can effectively reduce the user's extra trips and reduce time costs, while increasing the scheduling flexibility of charging and discharging loads; on the other hand, it makes full use of the advantages of the self-driving electric vehicle, allowing it to go to the charging station for charging and discharging operations during the user's non-use period, thereby realizing the vehicle-grid bidirectional interaction optimization under the premise of meeting traffic constraints.

[0048] 2. The bidirectional energy optimization interaction model of the power-transportation coupling network constructed by the vehicle-grid bidirectional energy optimization interaction method of the autonomous driving electric vehicle proposed in the present invention adopts a cascade utilization method to configure the power batteries retired from vehicles at charging stations for secondary utilization, and takes into account the impact of the number of charge and discharge cycles on the life of energy storage. It adopts a multi-distributed power supply configuration method of photovoltaic-energy storage-autonomous driving electric vehicle V2G to improve the overall load flexibility of the charging station and reduce the cost of the system purchasing electricity from the upper power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the structure of a 20-node distribution network-13-node transportation network in Example 1 of the present invention.

[0050] Figure 2 This is the photovoltaic output characteristic curve in Example 1 of the present invention.

[0051] Figure 3 It is the time-of-use electricity price in Example 1 of the present invention.

[0052] Figure 4 The present invention is a flowchart of the method.

[0053] Figure 5 Schematic diagram of the architecture and operation mode of the charging system of the present invention.

[0054] Figure 6 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0056] The present invention proposes a vehicle-grid bidirectional energy optimization interaction method for an autonomous driving electric vehicle. The method utilizes the electric vehicle power battery to perform V2G reverse charging operations to achieve flexible bidirectional interaction between the vehicle and the grid. At the same time, the autonomous driving electric vehicle can go to the charging station to perform charging and discharging operations during idle periods without user intervention, which saves the time cost of charging for users and improves the utilization rate of equipment during non-peak hours for charging stations. The annual comprehensive charging cost of the system is effectively reduced while the queuing time is reduced. By optimizing the configuration of battery charging and discharging facilities and reasonably arranging the charging and discharging location and time of the autonomous driving electric vehicle (i.e., a bidirectional energy optimization interaction solution), the hardware configuration cost, electricity purchase cost of the charging service provider and the time cost of the electric vehicle user due to charging operations are reduced while meeting the distribution network access requirements and traffic constraints.

[0057] At present, the charge and discharge cycle life of the ternary lithium battery commonly used in new energy vehicles generally reaches 2,000-3,000 times. According to the calculation of 200,000 kilometers in the entire life cycle of electric vehicles, only 600-800 cycles are required. Using the huge redundant cycle life of electric vehicle power batteries for V2G can effectively improve its economic benefits. V2G requires electric vehicles to charge during low load periods and discharge during peak load periods. This charging and discharging method does not match the daily driving habits of users. After 2,000 cycles, the power battery of electric vehicles still has 80% health. The power battery discarded by the vehicle is configured at the charging station for secondary use by adopting the method of cascade utilization, and the impact of the number of charge and discharge cycles on the life of energy storage is considered.

[0058] Embodiment 1:

[0059] In this embodiment, the topology structure is as follows Figure 1 The 20-node distribution network and 13-node transportation network shown in the figure are constructed as objects. Figure 3 The architecture of the charging system shown (parameters: the typical number of days in a year is 365; the unit time is 1 hour; the duration of a single charge and a single V2G reverse discharge is 0.3 hours and 0.8 hours respectively; the proportion of the minimum charging demand met in a typical day is 0.4; the rated charging power of a single charging pile is 120 kW; the rated V2G reverse discharge power of a single charging pile is 40 kW; the energy conversion efficiency of the electric vehicle charging pile is 0.9; the maximum discharge power and maximum charging power of a single energy storage are 20 kW; the lower and upper limits of the energy state of the energy storage are 0.1 and 0.9 respectively ; The installed capacity of a single energy storage unit is 60 kWh; The initial power of a single energy storage unit is 20 kWh; The unit time cost is 20 yuan / hour; The price of a single battery is 80,000 yuan; The cycle life of the battery as a vehicle power battery and a charging station energy storage is 2,000 times and 1,000 times respectively; The design capacity of the battery is 60 kWh; The health of the battery as a vehicle power battery and a charging station energy storage is 0.9 and 0.7 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 photovoltaic output characteristic curve and time-of-use electricity price used are as follows Figure 2 and Figure 3 As shown in Figure 2), a bidirectional energy optimization interaction method for the vehicle-grid of autonomous electric vehicles is implemented, such as Figure 4 As shown, the following steps are included:

[0060] 1. Construct a two-way energy optimization interaction model for the power-transportation coupling network

[0061] The bidirectional energy optimization interaction model considers the power-transportation coupling network as a whole, and its optimization goal is to minimize the comprehensive charging cost of the autonomous electric vehicle, which includes the cost of the charging station purchasing electricity from the upper grid. E , Battery loss cost C B , User time cost C T The schematic diagram of the charging system architecture and operation mode is as follows: Figure 5 As shown in the figure, self-driving electric vehicles can go to charging stations to perform charging and V2G reverse discharge in two ways: one is that the electric vehicle owner drives the vehicle and performs charging or discharging operations during the journey; the other is that the electric vehicle adopts the self-driving mode and automatically goes to the charging station to perform charging or discharging operations during the idle time when the owner is not using the vehicle. Since only the first case consumes the user's time cost in the above two cases, when calculating the user's time cost, only the time cost caused by performing charging or discharging operations during the journey is considered. The objective function of the model is shown in equations (1) to (4):

[0062] min C E +C B +C T (1)

[0063]

[0064] In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

[0065] The constraints of the model include:

[0066] (1) Vehicle charging and discharging guidance constraints

[0067] The model established by the present invention considers two battery states of electric vehicles: insufficient power and sufficient power. The former indicates that the electric vehicle needs to be charged and is not suitable for V2G reverse discharge, while the latter indicates that the electric vehicle does not need to be charged and can be V2G reverse discharged. Formula (5)-Formula (12) are the vehicle charging and discharging guidance constraints of the model. Formula (5)-Formula (6) are the constraints on the number of electric vehicles with insufficient power and sufficient power at each node respectively; Formula (7)-Formula (8) are the constraints on the number of electric vehicles in use and idle states at each node respectively; Formula (9) is the constraint on the minimum charging demand that can be met in a typical day; Formula (10)-Formula (11) are the constraints on the number of electric vehicles that can be charged and V2G reverse discharged at the charging station respectively; Formula (12) is the upper limit constraint on the number of charging piles in the charging station, that is, the number of vehicles that are charged and V2G reverse discharged in any period of time cannot exceed the number of charging piles in the charging station. Therefore, the vehicle charging and discharging guidance constraints are specifically set as follows:

[0068]

[0069] In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. If it is 0, the starting point of path k is not or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs. If it is 0, the node where the target charging station of path k is located is not cs.CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

[0070] (2) Charging station operation constraints

[0071] In this constraint, equations (13) to (18) are the charging station operation constraints of the optimization model. Equation (13) is the power balance constraint of the charging station; equation (14) is the charging power constraint of the charging station; equation (15) is the V2G reverse discharge power constraint of the charging station; equation (16) is the actual photovoltaic output constraint; equations (17) and (18) are the charging and discharging power constraints and charge state constraints of the energy storage system in the charging station, respectively; equation (19) is the cost per unit capacity of the battery during the charge and discharge cycle; equations (20) to (21) are the battery loss cost constraints during the charging and replacement cycle of the battery in each application stage; equations (22) to (23) are the charging energy and discharge energy constraints of the energy storage in the charging station. The specific settings of the charging station operation constraints are as follows:

[0072]

[0073] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E )(19)

[0074]

[0075]

[0076] In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UB The cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

[0077] (3) Power-transportation coupling network constraints

[0078] Formula (29)-Formula (34) are the distribution network operation constraints of the vehicle-grid bidirectional energy optimization interaction model for the full life cycle of the autonomous driving electric vehicle proposed in this invention. Formula (29) and (30) are the active and reactive power balance constraints of the distribution network nodes, respectively, Formula (31) is the distribution network line capacity constraint, and Formula (32)-(34) are the distribution network node voltage constraints. The specific settings of the power-transportation coupling network constraints are as follows:

[0079]

[0080] In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the node e in the distribution network during time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

[0081] 2. Solve the bidirectional energy optimization interaction model of the power-traffic coupling network and obtain the bidirectional energy optimization interaction solution for the vehicle-grid of autonomous driving electric vehicles. The two charging stations are located on road T6-T8 (12 charging piles) and road T10-T11 (16 charging piles).

[0082] In order to verify the effectiveness of the method proposed in the present invention, the one-way charging guidance strategy that only considers the owner's driving is applied as strategy 2 to the 20-node distribution network-13-node transportation network, and the economic efficiency is compared with the method proposed in the present invention (strategy 1). The results are shown in Table 1:

[0083] Table 1 Economic comparison

[0084] Strategy Annual electricity purchase cost (yuan) Annual battery loss cost (yuan) Annual time cost (yuan) Annual comprehensive charging cost (yuan) Strategy 1 <![CDATA[7.02×10 6 ]]> <![CDATA[5.53×10 6 ]]> <![CDATA[2.37×10 6 ]]> <![CDATA[1.49×10 7 ]]> Strategy 2 <![CDATA[8.89×10 6 ]]> <![CDATA[5.07×10 6 ]]> <![CDATA[4.14×10 6 ]]> <![CDATA[1.81×10 6 ]]> ;

[0085] By comparison, it can be seen that the annual electricity purchase cost, annual battery loss cost and comprehensive charging cost are all lower when strategy 1 is adopted. In terms of the annual electricity purchase cost of the charging station, the annual time cost is reduced by 21.03% when strategy 1 is adopted compared with strategy 2; in terms of annual time cost, the annual comprehensive charging cost is reduced by 42.75% when strategy 1 is adopted compared with strategy 2. From the perspective of annual comprehensive charging cost, the annual comprehensive charging cost is reduced by 17.68% when strategy 1 is adopted compared with strategy 2. Therefore, the vehicle-grid bidirectional energy optimization interaction method of the autonomous driving electric vehicle proposed in the present invention not only meets the constraints of the power-transportation network, but also utilizes the electric vehicle autonomous driving function to realize the bidirectional flexible interaction between the vehicle and the grid, thereby effectively reducing the annual comprehensive charging cost of the system.

[0086] Embodiment 2:

[0087] like Figure 6 As shown, a vehicle-grid bidirectional energy optimization interaction system for an autonomous driving electric vehicle, the system comprising a model building module and a model solving module;

[0088] The model building module is used to build a two-way energy optimization interaction model of the power-transportation coupling network, wherein the two-way energy optimization interaction model of the power-transportation coupling network aims to minimize the comprehensive charging cost of the autonomous driving electric vehicle, and takes into account the owner driving mode of the electric vehicle, the unmanned driving mode, and the charging of the electric vehicle and the V2G reverse discharge;

[0089] The objective function of the two-way energy optimization interaction model of the power-transportation coupling network includes:

[0090] min C E +C B +C T ;

[0091]

[0092] In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

[0093] The bidirectional energy optimization interaction model of the power-transportation coupling network considers vehicle charging and discharging guidance constraints, charging station operation constraints considering the full life cycle of electric vehicle batteries, and power-transportation coupling network constraints;

[0094] The vehicle charging and discharging guidance constraints include:

[0095]

[0096] In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs; T CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

[0097] The charging station operation constraints include:

[0098]

[0099] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0100]

[0101]

[0102] In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UBThe cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

[0103] The power-transportation coupling network constraints include:

[0104]

[0105] In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the node e in the distribution network during time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

[0106] The model solving module is used to solve the bidirectional energy optimization interaction model of the power-traffic coupling network, and obtain the vehicle-grid bidirectional energy optimization interaction solution of the autonomous driving electric vehicle.

Claims

1. A vehicle-grid bidirectional energy optimization interaction method for an autonomous driving electric vehicle, characterized in that: The method comprises: S1. Constructing a bidirectional energy optimization interaction model of the power-transportation coupling network, wherein the bidirectional energy optimization interaction model of the power-transportation coupling network aims to minimize the comprehensive charging cost of the autonomous driving electric vehicle, and considers the owner driving mode of the electric vehicle, the unmanned driving mode, and the charging of the electric vehicle and the V2G reverse discharge; S2. Solve the bidirectional energy optimization interaction model of the power-transportation coupling network and obtain the bidirectional energy optimization interaction solution for the vehicle-grid of the autonomous electric vehicle.

2. The vehicle-grid bidirectional energy optimization interaction method of an autonomous driving electric vehicle according to claim 1, characterized in that: The objective function of the two-way energy optimization interaction model of the power-transportation coupling network includes: my C E +C B +C T ; In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

3. The vehicle-grid bidirectional energy optimization interaction method of an autonomous driving electric vehicle according to claim 2, characterized in that: The bidirectional energy optimization interaction model of the power-transportation coupling network takes into account vehicle charging and discharging guidance constraints, and the vehicle charging and discharging guidance constraints include: In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs; T CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

4. The vehicle-grid bidirectional energy optimization interaction method of an autonomous driving electric vehicle according to claim 3 is characterized by: The bidirectional energy optimization interaction model of the power-transportation coupling network also considers the charging station operation constraints of the electric vehicle battery throughout its life cycle. The charging station operation constraints include: In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UB The cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

5. The vehicle-grid bidirectional energy optimization interaction method for an autonomous driving electric vehicle according to claim 4 is characterized in that The two-way energy optimization interaction model of the power-transportation coupling network also considers the power-transportation coupling network constraints, which include: In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the distribution network node e in time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

6. A vehicle-grid bidirectional energy optimization interaction system for an autonomous driving electric vehicle, characterized in that: The system includes a model building module and a model solving module; The model building module is used to build a two-way energy optimization interaction model of the power-transportation coupling network, wherein the two-way energy optimization interaction model of the power-transportation coupling network aims to minimize the comprehensive charging cost of the autonomous driving electric vehicle and considers the vehicle charging and discharging guidance constraints; The model solving module is used to solve the bidirectional energy optimization interaction model of the power-traffic coupling network, and obtain the vehicle-grid bidirectional energy optimization interaction solution of the autonomous driving electric vehicle.

7. The vehicle-grid bidirectional energy optimization interaction system for an autonomous driving electric vehicle according to claim 6, characterized in that: The objective function of the two-way energy optimization interaction model of the power-transportation coupling network includes: In the above formula, C E The cost of purchasing electricity from the upper grid for the charging station; C B is the battery loss cost; C T is the user's time cost; d is the number of typical days in a year; t is any time period in a typical day; is the active power output by the distribution network to the charging station at node e during time period t; is the electricity purchase price in period t; C BV and C BE They are the daily battery loss costs when the battery is used as an onboard power battery for electric vehicles and as energy storage for charging stations; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station for charging during time period t; The vehicle flow of electric vehicles driven by car owners who take path k to the charging station to perform V2G reverse discharge during time period t; is the correlation coefficient between path k and road l; is the travel time of road l during time period t; T CH and T DI are the duration of a single charge and a single V2G reverse discharge respectively; PR T The unit time cost.

8. The vehicle-grid bidirectional energy optimization interaction system for an autonomous driving electric vehicle according to claim 7, characterized in that: The bidirectional energy optimization interaction model of the power-transportation coupling network takes into account vehicle charging and discharging guidance constraints, and the vehicle charging and discharging guidance constraints include: In the above formula, and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; is the correlation coefficient between path k and the starting point or. If it is 1, the starting point of path k is or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the trip and the node where the charging station is located, respectively. and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t, respectively; and are the number of electric vehicles with insufficient and sufficient power at the starting point or in time period t respectively; and is the number of electric vehicles in use and idle states at the starting point or in time period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t; R is the proportion of the minimum charging demand that is met in a typical day; is the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, the node where the target charging station of path k is located is cs; T CH and T DI are the average duration of single charge and V2G reverse discharge respectively; T U is the unit time; is the number of charging piles at the charging station at node cs.

9. The vehicle-grid bidirectional energy optimization interaction system for an autonomous driving electric vehicle according to claim 8, characterized in that: The bidirectional energy optimization interaction model of the power-transportation coupling network also considers the charging station operation constraints of the electric vehicle battery throughout its life cycle. The charging station operation constraints include: In the above formula, is the power output by the distribution network to the charging station at node e during period t; is the photovoltaic power actually consumed in the charging station at node e during period t; is the output or input power of the charging station at node e during period t. When When , the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node e during period t; C Energy conversion efficiency of electric vehicle charging stations; is the charging load of electric vehicles in the charging station at node e during period t; and are the number of charging piles used for charging and V2G reverse discharge at the charging station at node cs in time period t, respectively; is the correlation coefficient between the node cs where the charging station is located and the node e of the distribution network. If it is 1, the charging station at the node cs is connected to the node e of the distribution network; P UC is the rated charging power of a single charging pile; P UD is the rated V2G reverse discharge power of a single charging pile; is the maximum photovoltaic power in the charging station at node e during period t; P CM and P DM They are the maximum discharge power and maximum charging power of a single energy storage unit respectively; is the number of energy storage systems configured in the charging station at the distribution network node e; η L and η H are the lower and upper limits of the energy storage state, respectively; EC E is the installed capacity of a single energy storage unit; EC O is the initial capacity of a single energy storage unit; PR UB The cost per unit capacity of the battery during charge and discharge cycles; PR B is the price of a single battery; CY V and CY E B are the cycle life of the battery when used as a vehicle power battery and charging station energy storage; C is the design capacity of a single battery; κ V and κ E They are the health of the battery when used as an on-board power battery and as energy storage in a charging station; and They are the charging energy and discharging energy of the energy stored in the charging station at node e during period t, and one of them must be 0 during the same period; is a binary variable of the energy storage operation state. When the energy storage is discharged, Energy storage charging; A t,e It is the auxiliary variable used in McCormick envelope method.

10. The vehicle-grid bidirectional energy optimization interaction system of an autonomous driving electric vehicle according to claim 9, characterized in that: The two-way energy optimization interaction model of the power-transportation coupling network also considers the power-transportation coupling network constraints, which include: In the above formula, and are the active and reactive power on line w at time period t respectively; and They are the basic active and reactive loads connected to the node e of the distribution network at time period t; is the power output from the distribution network to the charging station at node e during period t; w is all the lines 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 during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U t,e is the bus voltage of the node e in the distribution network during time period t; U m and U M They are the upper and lower limits of the distribution network bus voltage respectively; is the correlation coefficient between path k and road l; FT t,l is the total traffic flow on road l during time period t; and The vehicle flows of owner-driven electric vehicles and self-driving electric vehicles taking path k to the charging station for charging in time period t, respectively; and are the vehicle flows of owner-driven electric vehicles and self-driving electric vehicles that use path k to go to the charging station to perform V2G reverse discharge during time period t; FC l is the upper limit of the traffic capacity of road l.

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