Vehicle network charging and discharging control method and device, storage medium and electronic equipment

Abstract

The invention discloses a vehicle network charging and discharging control method and device, a storage medium and electronic equipment. The method comprises the steps that a first type of electric vehicles participating in centralized charging and discharging scheduling and a second type of electric vehicles not participating in centralized charging and discharging scheduling are determined, and the centralized charging and discharging scheduling is that an aggregation end of a power grid side executes charging and discharging control on the electric vehicles; determining an objective function representing the active power loss of the power grid side and the aggregation end income of the power grid side; obtaining a constraint condition based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; and determining a centralized scheduling strategy for the first type electric vehicle under the condition that the constraint condition is met and the objective function is minimum. According to the invention, the technical problem that the vehicle network interactive scheduling efficiency is not ideal in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of power grid control, and more specifically, to a vehicle grid charging and discharging control method, device, storage medium and electronic device. Background Art

[0002] The growing demand for charging electric vehicles has increased the burden on the distribution network. The charging time of electric vehicles usually coincides with the peak load time of the power grid. Disordered charging behavior exacerbates the peak-to-valley difference of the power grid load, which in turn leads to a series of problems such as voltage and frequency. At present, vehicle-grid interaction is that electric vehicles charge during low-load periods and discharge during peak periods, that is, peak-to-valley arbitrage. For the power grid side, vehicle-grid interaction can achieve a balanced and stable distribution network and effectively reduce peak loads and fill valleys. For the user side, vehicle-grid interaction can reduce charging costs.

[0003] There is a problem in the relevant technology that there is a lack of choice for electric vehicle owners for the type of demand response. In addition, the discharge of electric vehicles is not taken into account. Electric vehicles are only regarded as controllable loads, and their energy storage capacity is not utilized, resulting in unsatisfactory vehicle-grid interaction scheduling efficiency between electric vehicles and the power grid.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a vehicle-grid charging and discharging control method, device, storage medium and electronic device to at least solve the technical problem of unsatisfactory vehicle-grid interactive scheduling efficiency existing in the related art.

[0006] According to one aspect of an embodiment of the present application, a vehicle-grid charging and discharging control method is provided, comprising: determining a first type of electric vehicle participating in centralized charging and discharging scheduling, and a second type of electric vehicle not participating in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is a charging and discharging control performed on the electric vehicle by an aggregation terminal on the grid side; determining an objective function characterizing the active network loss on the grid side and the revenue of the aggregation terminal on the grid side; obtaining constraints based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; and determining a centralized scheduling strategy for the first type of electric vehicle when the constraints are met and the objective function is minimized.

[0007] Optionally, the determining of the first type of electric vehicles participating in the centralized charging and discharging scheduling, and the second type of electric vehicles not participating in the centralized charging and discharging scheduling, includes: obtaining battery parameters, travel times, and historical response types of each of the multiple electric vehicles; determining the historical charging and discharging information corresponding to the multiple electric vehicles within a predetermined time period; based on the battery parameters, the travel time, the historical response type, and the historical charging and discharging information, determining the first type of electric vehicles and the second type of electric vehicles included in the multiple electric vehicles.

[0008] Optionally, the constraint conditions are obtained based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle, including: determining the state of charge constraint for constraining the active network loss in the objective function based on the state of charge requirement set for the first type of electric vehicle indicated in the charging and discharging demand information; determining the first charging and discharging constraint for constraining the active network loss in the objective function based on the charging and discharging power of the first type of electric vehicle indicated in the charging and discharging demand information, and the required charging time; determining the second charging and discharging constraint for constraining the active network loss in the objective function based on the charging and discharging energy consumption of the second type of electric vehicle indicated in the charging and discharging consumption information; determining the constraint conditions based on the state of charge constraint, the first charging and discharging constraint, and the second charging and discharging constraint.

[0009] Optionally, the constraints also include cost constraints, and the method also includes: determining the first income generated by the first type of electric vehicles at the aggregation end based on the first number of the first type of electric vehicles, time-sharing electricity costs, charging and discharging powers, and charging and discharging durations; determining the second income generated by the second type of electric vehicles at the aggregation end based on the second number of the second type of electric vehicles, charging and discharging demands, and scheduling compensation; determining the consumption cost of the aggregation end based on the charging and discharging load consumed by the aggregation end, the electricity purchase cost, and the electricity sales cost; and determining the cost constraint condition for constraining the income of the aggregation end in the objective function based on the first income, the second income, and the consumption cost.

[0010] Optionally, the method also includes: determining the dispatchable amount of electricity and the dispatchable duration allowed for the centralized charging and discharging scheduling of the first type of electric vehicle; and determining the scheduling compensation based on the dispatchable amount of electricity, the dispatchable duration, a predetermined amount of electricity compensation coefficient, and a predetermined duration compensation coefficient.

[0011] Optionally, the aggregation end is also provided with a photovoltaic power generation device, the grid side includes the electricity generated by the photovoltaic power generation device, the constraint condition also includes a flow condition, and the method also includes: determining the active load and reactive load of the node in the grid side, and the active flow and reactive flow of the branch; based on the charging and discharging load of the first type of electric vehicle, the charging and discharging load of the second type of electric vehicle, the active flow, the reactive flow, the active load, the reactive load, and the active power and reactive power generated by the photovoltaic power generation device, determine the flow condition for constraining the active network loss in the objective function.

[0012] Optionally, the constraints also include output constraints, and the method also includes: determining the output constraints based on the upper and lower limits of the voltage of the node, the maximum current constraint of the branch, the photovoltaic maximum power point tracking MPPT output, and the apparent power capacity of the photovoltaic power generation equipment.

[0013] According to another aspect of an embodiment of the present application, a vehicle-grid charging and discharging control device is provided, comprising: a vehicle determination module, used to determine a first type of electric vehicle participating in centralized charging and discharging scheduling, and a second type of electric vehicle not participating in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is a charging and discharging control performed on the electric vehicle by the aggregation end on the grid side; a grid-side determination module, used to determine an objective function characterizing the active network loss on the grid side and the revenue of the aggregation end on the grid side; a constraint determination module, used to obtain constraint conditions based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; and a vehicle scheduling module, used to determine a centralized scheduling strategy for the first type of electric vehicle while satisfying the constraint conditions and minimizing the objective function.

[0014] According to another aspect of an embodiment of the present application, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the vehicle network charging and discharging control methods.

[0015] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the vehicle network charging and discharging control methods.

[0016] In the embodiment of the present application, a vehicle-grid charging and discharging control method is adopted, by determining the first type of electric vehicles participating in the centralized charging and discharging scheduling, and the second type of electric vehicles not participating in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is the aggregation end on the grid side performing charging and discharging control on the electric vehicles; determining the objective function that characterizes the active network loss on the grid side and the revenue of the aggregation end on the grid side; obtaining the constraint conditions based on the charging and discharging demand information of the first type of electric vehicles and the charging and discharging consumption information of the second type of electric vehicles; determining the centralized scheduling strategy for the first type of electric vehicles when the constraint conditions are met and the objective function is minimized. The purpose of optimizing the vehicle-grid charging and discharging control is achieved, and the technical effect of improving the vehicle-grid interactive control is realized, thereby solving the technical problem of unsatisfactory efficiency of vehicle-grid interactive scheduling existing in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flow chart of an optional vehicle network charging and discharging control method provided according to an embodiment of the present application;

[0019] Figure 2 is a schematic flow chart of an optional vehicle network charging and discharging control method provided according to an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of an optional vehicle network charging and discharging control device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0024] State of Charge (SOC) refers to the ratio of the current remaining power of the battery pack to the total power when it is fully charged. The SOC value range is between 0% and 100%.

[0025] Photovoltaic Maximum Power Point Tracking (MPPT) is used to improve the energy collection efficiency of photovoltaic systems. The output power of photovoltaic cells is closely related to the voltage and current of their operating points. There is a specific operating point, namely the maximum power point, at which photovoltaic cells can convert solar energy into electrical energy with the highest efficiency.

[0026] Dist-flow model, used for power system analysis of radial distribution network design, can accurately calculate the voltage and power distribution in the power grid and simplify the complex nonlinear power flow problem into a series of linear or quadratic constraints, which is convenient for optimization algorithm to solve.

[0027] Second-order cone relaxation is a mathematical optimization technique that transforms constraints into a second-order cone form, transforming a difficult non-convex problem into a relatively easy-to-solve convex problem.

[0028] Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) is a multi-objective genetic algorithm that improves the original NSGA by introducing a fast non-dominated sorting algorithm, the concept of crowding and an elite retention strategy.

[0029] An embodiment of the present application provides an embodiment of a method for controlling vehicle grid charging and discharging. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 1 is a flow chart of an optional vehicle network charging and discharging control method according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0031] Step S102, determining the first type of electric vehicles participating in the centralized charging and discharging scheduling and the second type of electric vehicles not participating in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is the aggregation end on the grid side performing charging and discharging control on the electric vehicles.

[0032] It can be understood that the first type of electric vehicles participating in the centralized charging and discharging scheduling are electric vehicles that choose incentive-based demand response, and the second type of electric vehicles that do not participate in the centralized charging and discharging scheduling are electric vehicles that choose price-based demand response. Price-based demand response mainly refers to time-of-use electricity prices, and electric vehicle owners choose the charging time period based on the level of electricity prices. Incentive-based demand response refers to electric vehicle owners who directly accept electricity consumption control or actively participate in electricity consumption adjustment according to incentive policies.

[0033] In an optional embodiment, determining a first type of electric vehicle participating in centralized charging and discharging scheduling, and a second type of electric vehicle not participating in centralized charging and discharging scheduling, includes: obtaining battery parameters, travel time, and historical response types of each of the multiple electric vehicles; determining historical charging and discharging information corresponding to each of the multiple electric vehicles within a predetermined time period; and determining the first type of electric vehicle and the second type of electric vehicle included in the multiple electric vehicles based on the battery parameters, travel time, historical response type, and historical charging and discharging information.

[0034] It can be understood that obtaining parameters such as battery parameters, travel time, and historical response types of multiple electric vehicles, as well as historical charging and discharging information corresponding to multiple electric vehicles within a predetermined time period, and determining the demand response type selected by the electric vehicle owner can provide the electric vehicle owner with personalized and efficient demand response services.

[0035] Optionally, the above method for determining the first type of electric vehicle and the second type of electric vehicle may be multiple, for example, Figure 2 is a schematic flow chart of an optional vehicle network charging and discharging control method provided according to an embodiment of the present application, such as Figure 2As shown, electric vehicles can include the first type of electric vehicles participating in centralized charging and discharging scheduling, that is, electric vehicles that choose incentive-based demand response, and the second type of electric vehicles that do not participate in centralized charging and discharging scheduling, that is, electric vehicles that choose price-based demand response. First, input the relevant parameters of the electric vehicle, such as battery status, travel time, demand response parameters, etc. Secondly, based on the charging and discharging process of the previous day and the two demand response information, select the optimal demand response method for the day. If the price-based demand response is selected for the second type of electric vehicle, the scheduling optimization is performed by the on-board optimizer of the electric vehicle; if the incentive-based demand response is selected for the first type of electric vehicle, the charging and discharging cost and electricity price compensation are calculated. After all electric vehicles determine the type of demand response, the information will be transmitted to the aggregation end, and the aggregation end will perform unified charging and discharging scheduling with the goal of maximizing the net profit of the aggregation end and minimizing the network loss. Since the electric vehicles participating in the price-based demand response complete the charging and discharging scheduling by the on-board optimizer, they only need to transmit the charging load and charging income to the aggregation end.

[0036] Step S104, determining an objective function that characterizes the active network loss on the grid side and the aggregation terminal benefit on the grid side.

[0037] It can be understood that the goal of minimizing active network loss is achieved by emitting or absorbing reactive power by the PV inverter, while maintaining node voltage stability. Determining the active network loss on the grid side and the objective function at the aggregation end can improve the reliability of the grid and reduce the cost of the grid.

[0038] Optionally, the objective function may be of multiple types, with the following mathematical expression being taken as an example.

[0039] minF=ω·f 1 +(1-ω)·f 2

[0040] f 1 =-(I ps (d)+I is (d)-C b (d))

[0041]

[0042] Among them, ω is the weight factor that balances the trade-offs between different objectives; f 1 is the goal of maximizing the net profit of the electric vehicle aggregation end on the dth day, which is transformed into a minimization goal by changing its sign; f 2 The objective of minimizing the active power loss of the distribution network is I ps (d) is the total revenue from price-based demand response at the aggregation end on day d; I is (d) is the total revenue from incentive-based demand response at the aggregation end on day d; C b(d) is the total cost of the aggregator purchasing electricity from the grid on day d. j, k are node numbers; r jk is the branch resistance; I jk is the branch current. minF is the objective function to be minimized, and t is the corresponding time period.

[0043] Step S106, obtaining constraint conditions based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle.

[0044] It can be understood that after obtaining the parameter information of the first type electric vehicle and the second type electric vehicle, the constraint conditions are determined according to the charging and discharging demand information and consumption information of the vehicles.

[0045] In an optional embodiment, based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle, constraint conditions are obtained, including: determining a state of charge constraint for constraining active network losses in an objective function based on the state of charge requirement set for the first type of electric vehicle indicated in the charging and discharging demand information; determining a first charging and discharging constraint for constraining active network losses in an objective function based on the charging and discharging power of the first type of electric vehicle indicated in the charging and discharging demand information and the required charging time; determining a second charging and discharging constraint for constraining active network losses in an objective function based on the charging and discharging energy consumption of the second type of electric vehicle indicated in the charging and discharging consumption information; and determining constraint conditions based on the state of charge constraint, the first charging and discharging constraint, and the second charging and discharging constraint.

[0046] It can be understood that the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle are determined. The charging and discharging demand information refers to the state of charge requirement, charging and discharging power, and required charging time set for the first type of electric vehicle. Based on the charging and discharging demand information, the active network loss in the objective function is constrained to determine the state of charge constraint and the first charging and discharging constraint. The charging and discharging consumption information refers to the charging and discharging energy consumption of the second type of electric vehicle, and the second charging and discharging constraint is determined. Based on the state of charge constraint, the first charging and discharging constraint, and the second charging and discharging constraint, the constraint conditions are determined. By clarifying the constraints of different types of vehicles, the power supply of the power grid can be accurately matched.

[0047] Optionally, the first charge and discharge constraint constrained by the active network loss in the objective function, that is, the charge and discharge demand constraint of the incentive-type demand response electric vehicle, can be multiple, taking the following mathematical expression as an example:

[0048]

[0049] in, is the charging and discharging power of the i-th electric vehicle in time period t; P ch and P disare the charging and discharging power of electric vehicles respectively; are the arrival and departure times of the i-th electric vehicle respectively; is the SOC of the i-th electric vehicle in time period t. q is the charge and discharge coefficient, η c and η d are the charge and discharge coefficients respectively, and Δt is the time length.

[0050] Optionally, the SOC constraints of the incentive-type demand response electric vehicle (i.e., the first type of electric vehicle) when the scheduling ends can be multiple, taking the following mathematical expression as an example:

[0051]

[0052] in, The minimum SOC value of the i-th electric vehicle at the end of charging set on the d-th day; SOC when the electric vehicle finishes charging; S cap It is the maximum SOC of the electric vehicle.

[0053] Optionally, the second charge and discharge constraint constrained by the active network loss in the objective function, that is, the charge and discharge demand constraint of the price-type demand response electric vehicle (second type electric vehicle) can be multiple, taking the following mathematical expression as an example:

[0054]

[0055] S(d,t)=S ini (d)+ΔS(d,t)-S dr (d)

[0056]

[0057] Among them, ΔS(t') is the SOC change from the start of the electric vehicle dispatch to the time t'; t' in is the start time of electric vehicle dispatch; q is the charge and discharge coefficient; P(m) is the charge and discharge power of the price-type demand response electric vehicle at time m; E 0 is the initial capacity of the battery before scheduling begins; η c and η d are the charge and discharge coefficients respectively; S ini (d) is the initial SOC before scheduling starts on day d; ΔS(day) is the charge-discharge change of SOC on day d; S dr (day) is the driving energy consumption SOC on day; S(d,t) is the SOC level during period t on day d; S dr (d) is the energy consumption SOC on day d; S min and S maxare the minimum and maximum SOC values ​​of electric vehicles, respectively; d' is the dispatch day number; D is the number of dispatch days on a long time scale; is the time when the electric vehicle ends scheduling on day d', and Δt is the time length.

[0058] In an optional embodiment, the constraints also include cost constraints, and the method also includes: determining a first benefit generated by the first type of electric vehicles at the aggregation end based on a first number of first type electric vehicles, time-sharing electricity costs, charging and discharging powers, and charging and discharging durations; determining a second benefit generated by the second type of electric vehicles at the aggregation end based on a second number of second type electric vehicles, charging and discharging demands, and scheduling compensation; determining the consumption cost of the aggregation end based on the charging and discharging load consumed by the aggregation end, the electricity purchase cost, and the electricity sales cost; and determining a cost constraint condition for constraining the benefit of the aggregation end in the objective function based on the first benefit, the second benefit, and the consumption cost.

[0059] It can be understood that the cost constraints include the first income, the second income and the consumption cost generated by the aggregation end, the first income, the second income, that is, the income of the aggregation end, the first number of the first type of electric vehicles, that is, the number of incentive-type demand response electric vehicles. The first income includes the first number of the first type of electric vehicles, the time-sharing electricity cost, the charging and discharging power, and the charging and discharging time; the second income includes the second number of the second type of electric vehicles, the charging and discharging demand, and the dispatching compensation; the consumption cost of the aggregation end includes the charging and discharging load consumed by the aggregation end, the electricity purchase cost, and the electricity sales cost. Determining the cost constraints based on the first income, the second income and the consumption cost realizes the refined management of costs and provides data support for the objective function.

[0060] Optionally, the above cost constraint condition, that is, the constraint condition of the aggregation end revenue and cost, can be multiple, taking the following mathematical expression as an example:

[0061]

[0062] Among them, I ps (d) is the total revenue from price-based demand response at the aggregation end on day d; I is (d) is the total revenue from incentivized demand response at the aggregator on day d; are the number of electric vehicles participating in price-based and incentive-based demand response on day d, respectively; are the start time and end time of the i-th electric vehicle participating in demand response, respectively; The power demand of the i-th electric vehicle; is the independent electricity price compensation for the i-th electric vehicle; P ch,A (d,t) and P dis,A (d, t) are the charging load and discharging load of the aggregation end at the tth period on the dth day, respectively; pch (t) and p dis (t) are the electricity purchase and sales prices from the power grid at the aggregation end.

[0063] In an optional embodiment, the method also includes: determining the dispatchable amount of electricity allowed for centralized charging and discharging scheduling of the first type of electric vehicle, and the dispatchable duration; determining the scheduling compensation based on the dispatchable amount of electricity, the dispatchable duration, the predetermined amount of electricity compensation coefficient, and the predetermined duration compensation coefficient.

[0064] It can be understood that based on the dispatchable amount of electricity allowed for centralized charging and discharging scheduling of the first type of electric vehicles, the dispatchable duration, the predetermined amount of electricity compensation coefficient, and the predetermined duration compensation coefficient, the dispatch compensation, that is, independent electricity price compensation, is calculated, which improves the flexibility and efficiency of power grid dispatch.

[0065] Optionally, the charging cost of the first type of electric vehicle, that is, the charging cost of the incentive-based demand response, can be calculated in a variety of ways. Electric vehicle users who participate in the incentive-based demand response can obtain incentive discounts to reduce charging costs. The charging cost C ds for:

[0066]

[0067] Where t” is the time interval; P(t”) is the charging power of the electric vehicle; Δt” is the scheduling time length; η c is the charging coefficient; r(t”) is the time-of-use electricity price for charging; is the incentive discount; i is the electric vehicle serial number; E i,st is the initial charge of the i-th vehicle; E dep is the amount of electricity required to be reached when the i-th electric vehicle is charged; C is the battery capacity of the electric vehicle; S dep It is the state of charge (SOC) that needs to be achieved at the end of charging.

[0068] Optionally, there can be multiple dispatch compensation (i.e., independent electricity price compensation) methods. Independent electricity price compensation mainly consists of compensation for dispatchable electricity and compensation for dispatchable time. That is, when the dispatchable electricity and dispatchable time of the electric vehicle exceed the minimum compensation value, the owner can get compensation. Independent electricity price compensation S Al It can be expressed as:

[0069] S Al =s 1 max(0,S i,st -S set )+s 2 max(0,T i,st -T set )

[0070] Among them, s 1 and 2 are the power compensation coefficient and time compensation coefficient respectively; S i,st is the initial SOC of the i-th vehicle; S set is the minimum compensation SOC set at the aggregation end; similarly, T i,st is the time length that the ith electric vehicle can participate in demand response; T set The minimum compensation time length set for the aggregation end.

[0071] In an optional embodiment, the aggregation end is also provided with a photovoltaic power generation device, the grid side includes the electricity generated by the photovoltaic power generation device, the constraint conditions also include flow conditions, and the method also includes: determining the active load and reactive load of the nodes on the grid side, and the active flow and reactive flow of the branches; based on the charging and discharging load of the first type of electric vehicle, the charging and discharging load of the second type of electric vehicle, the active flow, the reactive flow, the active load, the reactive load, and the active power and reactive power generated by the photovoltaic power generation device, determine the flow conditions for constraining the active network loss in the objective function.

[0072] It can be understood that the grid side includes the electricity generated by photovoltaic power generation equipment. Based on the active load and reactive load of the nodes on the grid side, the active flow and reactive flow of the branches, and the charging and discharging load of electric vehicles, the flow conditions are determined. By precisely controlling the flow, the loss of electricity in the transmission process is reduced, and the active network loss is reduced.

[0073] Optionally, the aggregation end may be a charging station, and the photovoltaic power generation equipment may be a photovoltaic panel installed on the charging station, and the photovoltaic power generated by the photovoltaic panel may be input into the power grid for consumption.

[0074] Optionally, there may be multiple methods for determining power flow constraints, including branch power flow constraints based on Dist-flow.

[0075]

[0076] Where k is the node number of the distribution network; is the total load of the aggregation end in period t, including Price type charge and discharge load and Incentive charge and discharge load; n ps and n is is the number of price-based and incentive-based charging and discharging vehicles in the corresponding period t; is the active power generated by photovoltaics; is the normal active load of node k; P jk (t) and P kl (t) is the branch active power flow; Produce reactive power for photovoltaic inverters; is the normal reactive load of node k; Q jk (t) and Q kl (t) is the branch reactive power flow; V j (t) and V k (t) is the voltage of nodes j and k; r jk 、x jk are branch impedance and admittance respectively.

[0077] In an optional embodiment, the constraints also include output constraints, and the method also includes: determining the output constraints based on the upper and lower voltage limits of the node, the maximum current constraint of the branch, the photovoltaic maximum power point tracking MPPT output, and the apparent power capacity of the photovoltaic power generation equipment.

[0078] It can be understood that the output constraints are voltage, current and photovoltaic output constraints. The output constraints are determined based on the voltage upper and lower limits of the node, the maximum current constraint of the branch, the photovoltaic maximum power point tracking (MPPT) output, and the apparent power capacity of the photovoltaic power generation equipment. The setting of output conditions helps prevent overvoltage, overcurrent and other faults in the power system.

[0079] Optionally, the output constraint conditions, namely, voltage, current and photovoltaic output constraint conditions, may be multiple, and the following mathematical expression is taken as an example:

[0080]

[0081] in, is the voltage upper and lower limit constraints of node k; is the maximum current constraint of branch jk; is the photovoltaic maximum power point tracking output of node k; is the apparent power capacity of the inverter at node k.

[0082] Step S108, determining a centralized dispatching strategy for the first type of electric vehicles while satisfying the constraint conditions and minimizing the objective function.

[0083] It can be understood that, when the constraints are met and the objective function is minimized, the model is solved to determine the centralized dispatching strategy for the first type of electric vehicles.

[0084] Optionally, the model solving method can be various, including solving nonlinear multi-objective optimization problems, where the objective function f 2, power flow constraints and photovoltaic output constraints can be relaxed through the second-order cone, and then solved by Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ). NSGA-Ⅱ has excellent search ability, ensures the diversity of Pareto solutions, and is suitable for multi-objective problems.

[0085] Through the above step S102, the first type of electric vehicles participating in the centralized charging and discharging scheduling and the second type of electric vehicles not participating in the centralized charging and discharging scheduling are determined, wherein the centralized charging and discharging scheduling is the charging and discharging control performed by the aggregation end on the grid side on the electric vehicles; step S104, determine the objective function that characterizes the active network loss on the grid side and the benefit of the aggregation end on the grid side; step S106, based on the charging and discharging demand information of the first type of electric vehicles and the charging and discharging consumption information of the second type of electric vehicles, obtain the constraint conditions; step S108, determine the centralized scheduling strategy for the first type of electric vehicles when the constraint conditions are met and the objective function is minimized. The purpose of optimizing the charging and discharging control of the vehicle network can be achieved, and the technical effect of improving the interactive control of the vehicle network is achieved, thereby solving the technical problem of unsatisfactory efficiency of the interactive scheduling of the vehicle network existing in the related technology.

[0086] Based on the above embodiments and optional embodiments, the present application proposes an optional implementation method, which combines the vehicle-grid interactive scheduling model of price-based demand response and incentive-based demand response, while taking into account the distributed photovoltaics in the area where the charging piles are located, thereby achieving the regulation of the distribution network voltage, with the goal of maximizing the aggregator's profit and minimizing the network loss.

[0087] First, input the relevant parameters of the electric vehicle, such as battery status, travel time, demand response parameters, etc. Secondly, based on the charging and discharging process of the previous day and the two demand response information, select the optimal demand response method for the day. If price-based demand response is selected, the scheduling optimization is performed by the on-board optimizer of the electric vehicle; if incentive-based demand response is selected, the charging and discharging costs and electricity price compensation are calculated. After all electric vehicles determine the type of demand response, the information will be transmitted to the aggregation end, and the aggregation end will conduct unified scheduling of charging and discharging with the goal of maximizing the net profit of the aggregation end and minimizing the network loss. Since the electric vehicles participating in the price-based demand response complete the charging and discharging scheduling by the on-board optimizer, they only need to transmit the charging load and charging income to the aggregation end.

[0088] Demand response can be divided into two categories: price-based and incentive-based. Price-based demand response mainly refers to time-of-use electricity prices, and electric car owners choose charging time periods based on the price of electricity. Incentive-based demand response refers to electric car owners who directly accept electricity consumption control or actively participate in electricity consumption adjustment according to incentive policies, thereby obtaining incentive discounts or independent electricity price compensation.

[0089] The charging cost of the first type of electric vehicle, that is, the charging cost of the incentive demand response, can be calculated in a variety of ways. Electric vehicle users participating in the incentive demand response can obtain incentive discounts to reduce charging costs. The charging cost C ds for:

[0090]

[0091] Where t” is the time interval; P(t”) is the charging power of the electric vehicle; Δt” is the scheduling time length; η c is the charging coefficient; r(t”) is the time-of-use electricity price for charging; is the incentive discount; i is the electric vehicle serial number; E i,st is the initial charge of the i-th vehicle; E dep is the amount of electricity required to be reached when the i-th electric vehicle is charged; C is the battery capacity of the electric vehicle; S dep It is the state of charge (SOC) that needs to be achieved at the end of charging.

[0092] There are many methods for dispatch compensation (i.e. independent electricity price compensation). Independent electricity price compensation mainly consists of compensation for dispatchable electricity and compensation for dispatchable time. That is, when the dispatchable electricity and dispatchable time of electric vehicles exceed the minimum compensation value, the owner can get compensation. Independent electricity price compensation S Al It can be expressed as:

[0093] S Al =s 1 max(0,S i,st -S set )+s 2 max(0,T i,st -T set )

[0094] Among them, s 1 and 2 are the power compensation coefficient and time compensation coefficient respectively; S i,st is the initial SOC of the i-th vehicle; S set is the minimum compensation SOC set at the aggregation end; similarly, T i,st is the time length that the ith electric vehicle can participate in demand response; T set The minimum compensation time length set for the aggregation end.

[0095] The goal of minimizing active network loss is achieved by emitting or absorbing reactive power through the PV inverter, while maintaining node voltage stability. Determining the active network loss on the grid side and the objective function at the aggregation end can improve the reliability of the grid and reduce the cost of the grid.

[0096] The above objective function can be expressed mathematically as follows:

[0097] minF=ω·f 1 +(1-ω)·f 2

[0098] f 1 =-(I ps (d)+I is (d)-C b (d))

[0099]

[0100] Among them, ω is the weight factor that balances the trade-offs between different objectives; f 1 is the goal of maximizing the net profit of the electric vehicle aggregation end on the dth day, which is transformed into a minimization goal by changing its sign; f 2 The objective of minimizing the active power loss of the distribution network is I ps (d) is the total revenue from price-based demand response at the aggregation end on day d; I is (d) is the total revenue from incentive-based demand response at the aggregation end on day d; C b (d) is the total cost of the aggregator purchasing electricity from the grid on day d. j, k are node numbers; r jk is the branch resistance; I jk is the branch current. minF is the objective function to be minimized, and t is the corresponding time period.

[0101] After obtaining parameter information of the first type of electric vehicle and the second type of electric vehicle, the constraints are determined according to the charging and discharging demand information and consumption information of the vehicles. The constraints may include multiple ones, such as cost constraints, flow constraints, output constraints, incentive-based demand response electric vehicle SOC constraints when scheduling ends, incentive-based demand response electric vehicle charging and discharging demand constraints (first charging and discharging constraints), and price-based demand response electric vehicle charging and discharging demand constraints (second charging and discharging constraints).

[0102] The above cost constraints, i.e. the constraints on the revenue and cost of the aggregation end, are expressed mathematically as follows:

[0103]

[0104]

[0105] Among them, I ps (d) is the total revenue from price-based demand response at the aggregation end on day d; I is (d) is the total revenue from incentivized demand response at the aggregator on day d; are the number of electric vehicles participating in price-based and incentive-based demand response on day d, respectively; are the start time and end time of the i-th electric vehicle participating in demand response, respectively; The power demand of the i-th electric vehicle; is the independent electricity price compensation for the i-th electric vehicle; P ch,A (d,t) and P dis,A (d, t) are the charging load and discharging load of the aggregation end at the tth period on the dth day, respectively; p ch (t) and p dis (t) are the electricity purchase and sales prices from the power grid at the aggregation end.

[0106] The above power flow constraints include branch power flow constraints based on Dist-flow, which can be expressed as follows:

[0107]

[0108] Where k is the node number of the distribution network; is the total load of the aggregation end in period t, including Price type charge and discharge load and Incentive charge and discharge load; n ps and n is is the number of price-based and incentive-based charging and discharging vehicles in the corresponding period t; is the active power generated by photovoltaics; is the normal active load of node k; P jk (t) and P kl (t) is the branch active power flow; Produce reactive power for photovoltaic inverters; is the normal reactive load of node k; Q jk (t) and Q kl (t) is the branch reactive power flow; V j (t) and V k (t) is the voltage of nodes j and k; r jk 、x jk are branch impedance and admittance respectively.

[0109] The above output constraints, namely, voltage, current and photovoltaic output constraints, can be various, taking the following mathematical expression as an example:

[0110]

[0111] in, is the voltage upper and lower limit constraints of node k; is the maximum current constraint of branch jk; is the photovoltaic maximum power point tracking output of node k; is the apparent power capacity of the inverter at node k.

[0112] The SOC constraints of the above-mentioned incentive-type demand response electric vehicle (i.e., the first type of electric vehicle) when the scheduling ends can be various, taking the following mathematical expression as an example:

[0113]

[0114] in, The minimum SOC value of the i-th electric vehicle at the end of charging set on the d-th day; SOC when the electric vehicle finishes charging; S cap It is the maximum SOC of the electric vehicle.

[0115] The first charge and discharge constraint constrained by active network loss in the objective function, that is, the charge and discharge demand constraint of the incentive-type demand response electric vehicle can be expressed mathematically as follows:

[0116]

[0117] in, is the charging and discharging power of the i-th electric vehicle in time period t; P ch and P dis are the charging and discharging power of electric vehicles respectively; are the arrival and departure times of the i-th electric vehicle respectively; is the SOC of the i-th electric vehicle in time period t. q is the charge and discharge coefficient, η c and η d are the charge and discharge coefficients respectively, and Δt is the time length.

[0118] The second charge and discharge constraint constrained by the active network loss in the objective function, that is, the charge and discharge demand constraint of the price-type demand response electric vehicle (second type electric vehicle) can be multiple, taking the following mathematical expression as an example:

[0119]

[0120] S(d,t)=S ini (d)+ΔS(d,t)-S dr (d)

[0121]

[0122] Among them, ΔS(t') is the SOC change from the start of the electric vehicle dispatch to the time t'; t' in is the start time of electric vehicle dispatch; q is the charge and discharge coefficient; P(m) is the charge and discharge power of the price-type demand response electric vehicle at time m; E 0 is the initial capacity of the battery before scheduling begins; η c and η dare the charge and discharge coefficients respectively; S ini (d) is the initial SOC before scheduling starts on day d; ΔS(day) is the charge-discharge change of SOC on day d; S dr (day) is the driving energy consumption SOC on day; S(d,t) is the SOC level during period t on day d; S dr (d) is the energy consumption SOC on day d; S min and S max are the minimum and maximum SOC values ​​of electric vehicles, respectively; d' is the dispatch day number; D is the number of dispatch days on a long time scale; is the time when the electric vehicle ends scheduling on day d', and Δt is the time length.

[0123] Under the condition that the constraints are met and the objective function is minimized, the model is solved to determine the centralized dispatching strategy for the first type of electric vehicles.

[0124] The above optional implementation methods achieve at least the following effects: electric vehicle users are grouped according to different demand response types, and long-term vehicle-grid interaction control optimization is implemented for different vehicle groups. The charging and discharging conditions of electric vehicles are taken into consideration. Electric vehicles are not only regarded as controllable loads, but the energy storage capacity of electric vehicles is fully utilized, so that vehicle-grid interaction can be scheduled and decided under the premise of safety, stability and economy.

[0125] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0126] In this embodiment, a vehicle network charging and discharging control device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0127] According to an embodiment of the present application, a device embodiment for implementing a vehicle network charging and discharging control method is also provided. Figure 3 is a schematic diagram of a vehicle network charging and discharging control device according to an embodiment of the present application, such as Figure 3 As shown, the above-mentioned vehicle-grid charging and discharging control device includes: a vehicle determination module 302, a grid-side determination module 304, a constraint determination module 306, and a vehicle scheduling module 308. The device is described below.

[0128] The vehicle determination module 302 is used to determine the first type of electric vehicles that participate in the centralized charging and discharging scheduling, and the second type of electric vehicles that do not participate in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is the aggregation end on the grid side performing charging and discharging control on the electric vehicles;

[0129] A grid side determination module 304, connected to the vehicle determination module 302, is used to determine an objective function representing active network loss on the grid side and the aggregation terminal benefit on the grid side;

[0130] The constraint determination module 306 is connected to the grid side determination module 304 and is used to obtain the constraint condition based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle;

[0131] The vehicle dispatching module 308 is connected to the constraint determination module 306 and is used to determine a centralized dispatching strategy for the first type of electric vehicles while satisfying the constraint conditions and minimizing the objective function.

[0132] In a vehicle-grid charging and discharging control device provided by an embodiment of the present application, a vehicle determination module is set up to determine the first type of electric vehicles participating in the centralized charging and discharging scheduling, and the second type of electric vehicles not participating in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is the charging and discharging control performed by the aggregation end on the grid side on the electric vehicles; the grid side determination module is used to determine the objective function that characterizes the active network loss on the grid side and the revenue of the aggregation end on the grid side; the constraint determination module is used to obtain the constraint conditions based on the charging and discharging demand information of the first type of electric vehicles and the charging and discharging consumption information of the second type of electric vehicles; the vehicle scheduling module is used to determine the centralized scheduling strategy for the first type of electric vehicles when the constraint conditions are met and the objective function is minimized. The purpose of optimizing the vehicle-grid charging and discharging control is achieved, and the technical effect of improving the vehicle-grid interactive control is realized, thereby solving the technical problem of unsatisfactory efficiency of vehicle-grid interactive scheduling existing in the related technology.

[0133] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0134] It should be noted that the above vehicle determination module 302, the grid side determination module 304, the constraint determination module 306, and the vehicle scheduling module 308 correspond to steps S102 to S108 in the embodiment, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules can be run in a computer terminal as part of the device.

[0135] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0136] The above-mentioned vehicle-grid charging and discharging control device may also include a processor and a memory. The vehicle determination module 302, the grid side determination module 304, the constraint determination module 306, the vehicle scheduling module 308, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0137] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0138] An embodiment of the present application provides a non-volatile storage medium on which a program is stored, and when the program is executed by a processor, a vehicle network charging and discharging control method is implemented.

[0139] The embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and can be run on the processor. When the processor executes the program, the following steps are implemented: determining a first type of electric vehicle participating in centralized charging and discharging scheduling, and a second type of electric vehicle not participating in centralized charging and discharging scheduling, wherein centralized charging and discharging scheduling is a charging and discharging control performed by the aggregation end on the grid side on the electric vehicle; determining an objective function that characterizes the active network loss on the grid side and the revenue of the aggregation end on the grid side; obtaining constraint conditions based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; and determining a centralized scheduling strategy for the first type of electric vehicle when the constraint conditions are met and the objective function is minimized. The device in this article can be a server, a PC, etc.

[0140] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: determining a first type of electric vehicle participating in centralized charging and discharging scheduling, and a second type of electric vehicle not participating in centralized charging and discharging scheduling, wherein centralized charging and discharging scheduling is for the aggregation end on the grid side to perform charging and discharging control on the electric vehicle; determining an objective function that characterizes the active network loss on the grid side and the revenue of the aggregation end on the grid side; obtaining constraints based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; and determining a centralized scheduling strategy for the first type of electric vehicle when the constraints are met and the objective function is minimized.

[0141] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0142] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0145] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0146] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0147] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0148] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0149] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0150] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A vehicle network charging and discharging control method, characterized in that: include: Determine a first type of electric vehicle that participates in centralized charging and discharging scheduling, and a second type of electric vehicle that does not participate in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is that the aggregation end on the grid side performs charging and discharging control on the electric vehicles; Determine an objective function representing the active network loss on the grid side and the aggregation terminal benefit on the grid side; Obtaining constraint conditions based on the charging and discharging demand information of the first type of electric vehicle and the charging and discharging consumption information of the second type of electric vehicle; Under the condition that the constraint condition is satisfied and the objective function is minimized, a centralized dispatching strategy for the first type of electric vehicles is determined.

2. The method according to claim 1, characterized in that The first type of electric vehicles determined to participate in the centralized charging and discharging scheduling, and the second type of electric vehicles not participating in the centralized charging and discharging scheduling, include: Obtain battery parameters, travel time, and historical response types of multiple electric vehicles; Determining historical charging and discharging information corresponding to each of the plurality of electric vehicles within a predetermined time period; The first type electric vehicle and the second type electric vehicle included in the plurality of electric vehicles are determined based on the battery parameters, the travel time, the historical response type, and the historical charge and discharge information.

3. The method according to claim 1, characterized in that: The constraint conditions are obtained based on the charging and discharging demand information of the first type electric vehicle and the charging and discharging consumption information of the second type electric vehicle, including: Determining a state of charge constraint for constraining the active network loss in the objective function based on a state of charge requirement indicated in the charging and discharging requirement information and set for the first type of electric vehicle; Determining a first charging and discharging constraint for constraining the active network loss in the objective function based on the charging and discharging power of the first type of electric vehicle indicated in the charging and discharging demand information and the required charging time; Determining a second charging and discharging constraint constraining the active network loss in the objective function based on the charging and discharging energy consumption of the second type electric vehicle indicated in the charging and discharging consumption information; The constraint condition is determined based on the state of charge constraint, the first charge and discharge constraint, and the second charge and discharge constraint.

4. The method according to claim 1, characterized in that: The constraint condition further includes a cost constraint condition, and the method further includes: Determine a first income generated by the first type of electric vehicles at the aggregation end based on the first number of the first type of electric vehicles, time-sharing electricity cost, charging and discharging power, and charging and discharging time; Determining a second revenue generated by the second type of electric vehicles at the aggregation end based on a second number of the second type of electric vehicles, a charging and discharging demand, and a dispatch compensation; Determine the consumption cost of the aggregation end based on the charging and discharging load consumed by the aggregation end, the electricity purchase cost, and the electricity sales cost; Based on the first benefit, the second benefit, and the consumption cost, determine the cost constraint condition for constraining the aggregation end benefit in the objective function.

5. The method according to claim 4, characterized in that The method further comprises: Determining the dispatchable amount of electricity and dispatchable duration allowed for the centralized charging and discharging dispatch of the first type of electric vehicles; The scheduling compensation is determined based on the schedulable power, the schedulable duration, a predetermined power compensation coefficient, and a predetermined duration compensation coefficient.

6. The method according to claim 1, characterized in that The aggregation end is also provided with a photovoltaic power generation device, the grid side includes the power generated by the photovoltaic power generation device, the constraint condition also includes a flow condition, and the method further includes: Determine the active load and reactive load of the node and the active power flow and reactive power flow of the branch in the power grid side; Based on the charging and discharging load of the first type of electric vehicle, the charging and discharging load of the second type of electric vehicle, the active power flow, the reactive power flow, the active load, the reactive load, and the active power and reactive power generated by the photovoltaic power generation equipment, the power flow condition for constraining the active network loss in the objective function is determined.

7. The method according to claim 6, characterized in that The constraint condition also includes an output constraint condition, and the method further includes: The output constraint condition is determined based on the upper and lower voltage limits of the node, the maximum current constraint of the branch, the photovoltaic maximum power point tracking MPPT output, and the apparent power capacity of the photovoltaic power generation equipment.

8. A vehicle network charging and discharging control device, characterized in that: include: A vehicle determination module, used to determine a first type of electric vehicle that participates in centralized charging and discharging scheduling, and a second type of electric vehicle that does not participate in the centralized charging and discharging scheduling, wherein the centralized charging and discharging scheduling is a charging and discharging control performed on the electric vehicle by an aggregation terminal on the grid side; A grid-side determination module, used to determine an objective function representing the active network loss of the grid-side and the aggregation terminal benefit of the grid-side; A constraint determination module, configured to obtain a constraint condition based on the charging and discharging demand information of the first type electric vehicle and the charging and discharging consumption information of the second type electric vehicle; The vehicle dispatching module is used to determine a centralized dispatching strategy for the first type of electric vehicles while satisfying the constraint conditions and minimizing the objective function.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the vehicle network charging and discharging control method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle network charging and discharging control method as described in any one of claims 1 to 7.

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