Electric vehicle ordered charging method and system based on vehicle network interaction

Abstract

The invention discloses an electric vehicle ordered charging method and system based on vehicle network interaction. The method comprises the following steps: firstly, acquiring electric vehicle user data, charging equipment data and power distribution network operation basic data; inputting the electric vehicle user layer data, the charging equipment layer data and the power distribution network operation basic data into a pre-constructed electric vehicle multi-target ordered charging model, and determining a charging strategy of the electric vehicle; the pre-constructed electric vehicle multi-target ordered charging model gives consideration to power distribution network operation cost, load fluctuation, electric vehicle charging emergency degree and user satisfaction, and an electric vehicle aggregator is introduced as an intermediary between an electric vehicle and a power grid. According to the method, multiple conflicting targets can be processed, namely, the operation cost of the power distribution network is minimized, load fluctuation is stabilized, the charging emergency degree of the electric vehicle is considered, and the user satisfaction degree is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of orderly charging of electric vehicles, and relates to an orderly charging method and system for electric vehicles based on vehicle-grid interaction. Background Art

[0002] Due to the transformation of the global energy structure and the pressure of environmental protection, electric vehicles, as a clean energy means of transportation, have rapidly increased their market share. Vigorously developing load regulation technology represented by electric vehicles has become an important way to promote the transformation of the national energy structure and achieve the "dual carbon" goal. Load regulation technology, also known as load management or load control technology, refers to a technical means of monitoring, analyzing and controlling the power load in the power system to achieve a reasonable arrangement and adjustment of the power load. The main purpose of this technology is to flatten the load curve, that is, to use the power load in a balanced manner to improve the economy and safety of power grid operation and the investment benefits of power companies.

[0003] Load control technology can be divided into two categories: traditional control technology and new control technology. Traditional control technology mainly includes peak-valley filling control technology, power dispatching technology, electricity metering technology, etc. They optimize the distribution of power load through traditional dispatching means and management methods. New control technology includes intelligent power load control technology, interactive load control technology, distributed power load control technology, etc. These technologies use advanced intelligent and information means, such as smart grid, big data analysis, cloud computing and the Internet of Things, to achieve more refined and dynamic management and control of power loads.

[0004] Electric Vehicles (EV) loads are characterized by mobility and randomness, and their number is huge. Their disorderly access may cause the charging peak to overlap with the peak load of the distribution system, causing the distribution system to be overloaded, and further increasing the peak-to-valley difference of the distribution system load, reducing the economic efficiency of the distribution system operation, and causing problems such as waste of equipment resources. On the other hand, the existing distribution network infrastructure cannot well meet the charging needs brought by electric vehicles, especially in cities and along highways. While EVs inherit the traditional transportation attributes, they also have the attributes of mobile energy storage. It has become a consensus that large-scale EVs are the largest distributed flexibility resources in the whole society. Optimizing the orderly charging behavior of EVs can effectively alleviate vehicle congestion in the traffic network and improve the stability of the power grid operation. Vehicle-to-Grid (V2G) is an innovative energy management method that realizes two-way energy exchange and information coordination between EVs and the power grid. The core of V2G technology is to utilize the energy storage function of electric vehicles, through intelligent charging and discharging systems, to provide electricity to the power grid when the power grid demand is peak, and absorb electricity from the power grid for charging when the power grid load is low. This two-way interaction not only improves the efficiency and reliability of the power grid, but also brings economic benefits to electric vehicle users. Therefore, large-scale development of EV is the largest distributed flexibility resource in the whole society. Research on EV and grid interaction (Vehicle-to-Grid, V2G) technology can promote the two-way flow of information and energy between EV and the grid, which is conducive to fully tapping the regulation potential of EV resources, giving play to the role of flexibility resources, alleviating the construction pressure of distribution networks, meeting users' charging needs, increasing the adjustable resources of the grid, and improving the new energy consumption rate.

[0005] However, regarding the research on the vehicle-grid interaction framework and orderly charging of electric vehicles, firstly, the research on the vehicle-grid interaction framework by domestic and foreign scholars has not fully covered the multiple subjects involved in the vehicle-grid interaction process (such as electric vehicle users, electric vehicle aggregators, power grid operators, charging stations, etc.) and the complex interaction relationships between these subjects. The process steps are relatively rough. This lack of research on multiple subjects and interaction relationships has led to limitations in the overall understanding and modeling of the vehicle-grid interaction system, making it difficult to fully reflect the dynamic characteristics and operating efficiency of the system; secondly, most of the relevant research on orderly charging of electric vehicles focuses on the direct centralized scheduling of electric vehicles, and the spatial scale of the resource characteristics of the EV group is either macroscopic, treating electric vehicles as a whole interacting with the power grid, or based on a single EV. As the research object, it focuses on user characteristics but is difficult to achieve global optimization, and does not consider the role of electric vehicle aggregators. This inappropriate scale selection makes it difficult to balance local optimization and global benefits when formulating strategies, affecting the efficiency and stability of the overall system; in addition, the current EV optimization control strategy mainly starts from the safe operation of the power grid or the economic interests of users, lacks consideration of the urgency of EV charging, and this single optimization control strategy is difficult to take into account the needs of user satisfaction, which may lead to user dissatisfaction and decreased participation, thereby affecting the sustainability and stability of the vehicle-grid interaction system; finally, most of the existing studies directly study the optimization scheduling process of electric vehicles, without combining user adjustability identification, and do not consider the role of Electric Vehicle Aggregator (EVA). As an intermediary between electric vehicles and the power grid, EVA has the important functions of coordinating, managing and optimizing EV charging behavior. The lack of EVA participation limits the flexibility and responsiveness of the vehicle-grid interaction system, making it difficult to effectively integrate and utilize EV resources. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides an orderly charging method and system for electric vehicles based on vehicle-grid interaction, thereby solving the technical problems in the prior art of vehicle-grid interaction research, such as unclear interaction relationships among multiple subjects, improper selection of spatial scale, single optimization strategy, and neglected role of electric vehicle aggregators.

[0007] The present invention is achieved through the following technical solutions: A method for orderly charging of electric vehicles based on vehicle-grid interaction comprises the following steps: Obtain electric vehicle user data, charging equipment data, and basic distribution network operation data; The electric vehicle user layer data, charging equipment layer data and basic distribution network operation data are input into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces an electric vehicle aggregator as an intermediary between the electric vehicle and the power grid.

[0008] Preferably, the electric vehicle user data includes user vehicle information, on-board battery charge status, expected departure time and willingness to participate in charging and discharging scheduling; the charging equipment data includes the operating status, electrical parameters, equipment fault status and charging interface status of the charging equipment; the distribution network operation basic data includes the distribution network time-of-use electricity price, the node average load power calculated from historical data, the upper and lower limits of active power output of traditional units / active power output of new energy units, the upper and lower limits of line capacity / node voltage safe operation, and distribution network topology parameters.

[0009] Preferably, the urgency of charging the electric vehicle is measured by four indicators: the remaining power of the electric vehicle battery, the charging time, the waiting time for charging, and the charging load demand; the constraints of the user satisfaction include the electric vehicle capacity constraint, the electric vehicle discharge capacity constraint, and the electric vehicle power constraint.

[0010] Preferably, the constraints of the pre-built electric vehicle multi-objective orderly charging model include active power constraints, reactive power constraints, distribution network input power constraints, voltage constraints, new energy access constraints and line transmission capacity constraints.

[0011] An orderly charging system for electric vehicles based on vehicle-grid interaction comprises: Data acquisition unit: The data acquisition unit is used to acquire electric vehicle user data, charging equipment data and basic distribution network operation data; Strategy determination unit: The strategy determination unit is used to input electric vehicle user layer data, charging equipment layer data and basic distribution network operation data into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces electric vehicle aggregators as intermediaries between electric vehicles and the power grid.

[0012] Preferably, the data acquisition unit includes a charging device layer for acquiring electric vehicle user data and a charging device layer for acquiring charging device data; the strategy determination unit includes a smart grid layer for running the pre-built electric vehicle multi-objective orderly charging model; a charging service network operation monitoring layer is also provided between the charging device layer and the smart grid layer; the charging service network operation monitoring layer is used to count and analyze the total number of EV users willing to participate in charging and discharging and the number of available charging devices in each time period; The EV user layer, charging equipment layer, charging service network operation monitoring layer and smart grid layer communicate in sequence.

[0013] Preferably, the charging service network operation monitoring layer includes a charging management system of a regional service network operation monitoring system and a charging management system of an operator service network operation monitoring system; The charging management system of the regional service network operation monitoring system communicates with the charging equipment layer through a concentrator or a protocol converter; the charging management system of the operator service network operation monitoring system communicates with the charging equipment layer through a station-level monitoring system.

[0014] A computer system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0015] A computer-readable storage medium stores a computer program, wherein the computer program / instructions are executed by a processor to implement the steps of the above method.

[0016] A computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention discloses an orderly charging method for electric vehicles based on vehicle-grid interaction. Firstly, the method covers the main participants in the vehicle-grid interaction process (electric vehicle users, electric vehicle aggregators, power grid operators, charging stations, etc.) by acquiring electric vehicle user data, charging equipment data and basic distribution network operation data. The participation and use of these data enables the model to more accurately reflect the complex interaction relationship between the various subjects, thereby improving the understanding and modeling capabilities of the overall dynamic characteristics and operation efficiency of the vehicle-grid interaction system; secondly, the multi-objective orderly charging model adopted by the present invention not only considers the impact of the overall behavior of the EV group on the power grid, but also refines the charging strategy of a single EV through the role of the electric vehicle aggregator (EVA), achieving a balance between local optimization and global benefits, which helps to improve the overall efficiency and Stability; at the same time, the present invention adds two key indicators, namely, the urgency of electric vehicle charging and user satisfaction, into the model, so that the optimization control strategy is no longer based solely on the safe operation of the power grid or the economic benefits of users, but rather considers the actual needs of users in a more humane way. This multi-dimensional optimization strategy can better balance the interests of all parties, improve user satisfaction, and thus enhance user participation and the sustainability of the vehicle-grid interactive system; furthermore, the present invention introduces EVA as an intermediary between electric vehicles and the power grid, giving full play to the advantages of EVA in coordinating, managing and optimizing EV charging behavior. EVA can flexibly adjust the charging plan of EVs, respond to power grid needs, and improve the responsiveness and flexibility of the system. At the same time, EVA can also integrate and utilize EV resources to achieve the optimal allocation of resources and further improve the overall efficiency of the system. In summary, the orderly charging method of electric vehicles based on vehicle-grid interaction in the present invention effectively solves the problems existing in existing research by comprehensively considering multiple subjects and complex interactive relationships, taking into account the optimization of macro and micro scales, introducing the urgency of electric vehicle charging and user satisfaction factors, giving full play to the important role of EVA, and improving the flexibility and responsiveness of the system, and provides strong support for the development and application of vehicle-grid interactive systems.

[0018] Furthermore, another inventive point of the present invention is that the electric vehicle user data includes user vehicle information, vehicle battery charge state, expected departure time and willingness to participate in charging and discharging scheduling; the charging equipment data includes the operating status of the charging equipment, electrical parameters, equipment failure status and charging interface status; the basic data of the distribution network operation includes real-time load data, electricity price information and grid stability indicators. First, by comprehensively considering the electric vehicle user data, charging equipment data and basic data of the distribution network operation, a more scientific and reasonable charging strategy can be formulated to meet the charging needs of electric vehicle users and ensure the safe and stable operation of the power grid. According to factors such as grid load conditions, electricity price information and grid stability indicators, the charging time and charging power of electric vehicles can be flexibly adjusted to achieve optimal allocation and efficient utilization of resources. By considering the willingness of electric vehicle users to participate in charging and discharging scheduling and the urgency of charging, a charging strategy that better meets user needs can be formulated to improve user satisfaction and participation.

[0019] Furthermore, another inventive point of the present invention is that the urgency of charging the electric vehicle is measured by four indicators: the remaining power of the electric vehicle battery, the charging time, the waiting time for charging, and the charging load demand; the constraints of the user satisfaction include the electric vehicle capacity constraint, the electric vehicle discharge capacity constraint, and the electric vehicle power constraint. First, by comprehensively considering the four indicators of the remaining power of the electric vehicle battery, the charging time, the waiting time for charging, and the charging load demand, the system can more accurately evaluate the charging urgency of each electric vehicle, which helps to give priority to electric vehicles with low power, long waiting time, or urgent charging needs during the scheduling process, thereby improving the overall charging efficiency and service quality and realizing refined regulation; in addition, during the peak load period of the power grid, by identifying electric vehicles with low charging urgency, they can be guided to delay charging or choose other charging stations, thereby reducing the pressure on the power grid and balancing the contradiction between supply and demand. At the same time, for electric vehicles with high urgency, charging can be arranged first to ensure their normal operation to balance the contradiction between supply and demand; the setting of charging urgency enables electric vehicle users to obtain more personalized charging services according to their actual situation and needs. For users who are in urgent need of charging, the system can respond quickly and give priority to them, thereby improving user satisfaction and loyalty. In terms of setting constraints, the setting of conditions such as electric vehicle capacity constraints, discharge capacity constraints and power constraints is aimed at ensuring that electric vehicles will not damage the battery or cause safety accidents due to overcharging, over-discharging or power mismatch during the charging process, which helps to improve users' trust and satisfaction with charging services.

[0020] Furthermore, the constraints of the pre-built electric vehicle multi-objective orderly charging model include active power constraints, reactive power constraints, distribution network input power constraints, voltage constraints, new energy access constraints, and line transmission capacity constraints, wherein the active power constraints ensure that the active power carrying capacity of the power grid or charging facilities will not be exceeded during the charging process of the electric vehicle, which helps to prevent equipment damage or power grid failure caused by power overload, and ensure the safety and stability of the charging process. Although electric vehicle charging mainly involves active power, the constraints of reactive power are crucial to maintaining the voltage stability and power factor of the power grid. Reasonable reactive power management can reduce reactive power flow in the power grid and improve the power transmission capacity and efficiency of the power grid. The input power constraint of the distribution network limits the power input from the upper power grid to the distribution network, ensuring that the distribution network does not exceed its carrying capacity when receiving a large number of electric vehicle charging loads. This helps to prevent the distribution network from being overloaded and ensure the safe operation of the power grid. The voltage constraint ensures that the voltage of each node of the power grid remains within a reasonable range during the charging process of the electric vehicle. Excessive or low voltage may cause damage to the power grid equipment and the electric vehicle battery, so the voltage constraint is an important means to ensure the safety of the charging process. With the widespread application of renewable energy, the constraints on the access of new energy sources have become particularly important. This constraint condition helps to optimize the generation and consumption of renewable energy, ensure that the charging process of electric vehicles can make full use of renewable energy, reduce dependence on traditional energy, and promote the transformation and sustainable development of the energy structure. The line transmission capacity constraint limits the transmission capacity of each line in the power grid to prevent power grid failures caused by line overload. When electric vehicles are connected to the power grid for charging on a large scale, reasonable line transmission capacity constraints can ensure the stable operation of the power grid and avoid chain reactions caused by local overload. In summary, the setting of these constraints together constitutes an important basis for the multi-objective orderly charging model of electric vehicles. They not only ensure the safety and stability of the charging process, but also promote the efficient operation and sustainable development of the power grid. By comprehensively considering these constraints, a more scientific and reasonable charging strategy can be formulated to achieve harmonious coexistence between electric vehicles and the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 It is a flow chart of an orderly charging method for electric vehicles based on vehicle-grid interaction in the present invention; Figure 2This is a schematic diagram of a process for establishing a multi-objective orderly charging model for electric vehicles in Example 2 of the present invention; Figure 3 A schematic diagram of energy flow and information flow of an orderly charging method for electric vehicles based on vehicle-grid interaction established in the present invention.

[0023] Figure 4 It is a structural schematic diagram of an orderly charging system for electric vehicles based on vehicle-grid interaction in the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0027] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0028] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0029] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings: Example 1 like Figure 1 As shown, the present invention discloses an orderly charging method for electric vehicles based on vehicle-grid interaction, comprising the following steps: S1: Obtain electric vehicle user data, charging equipment data, and basic distribution network operation data; S2: Input the electric vehicle user layer data, charging equipment layer data and basic distribution network operation data into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces an electric vehicle aggregator as an intermediary between the electric vehicle and the power grid.

[0031] The method aims to achieve efficient, orderly and intelligent management of the electric vehicle charging process through vehicle-to-grid (V2G) technology. It first collects key data, including user usage habits, charging equipment capabilities and the real-time status of the power grid, and then uses this data to make decisions in a multi-objective optimization model to ultimately determine the charging time and charging power for each electric vehicle.

[0032] When this method is applied, first of all, it includes the collection of electric vehicle user data, charging equipment data and basic data of distribution network operation, among which electric vehicle user data includes user charging needs (such as required power, expected charging time, etc.), vehicle type, battery status, etc. Charging equipment data includes the geographical location of the charging station, the number of available charging piles, charging speed, equipment status, etc. The basic data of distribution network operation includes the time-of-use electricity price of the distribution network, the average load power of the node calculated from historical data, the upper and lower limits of the active power output of the traditional unit / the active power output of the new energy unit, the upper and lower limits of the line capacity / node voltage safe operation, and the topological parameters of the distribution network. Then, the above data are input into the pre-built multi-objective orderly charging model of electric vehicles. This model is a complex optimization system that can handle multiple conflicting objectives, such as minimizing the operating cost of the distribution network, smoothing load fluctuations, considering the charging urgency of electric vehicles, and improving user satisfaction.

[0033] This method uses multi-objective optimization, including distribution network operation cost, load fluctuation, EV charging urgency and user satisfaction. Considering the distribution network operation cost, the peak-to-valley difference of the power grid can be reduced by optimizing the charging time and power, thus reducing the operation cost of the power grid; considering the load fluctuation, the load of the power grid can be balanced, the power grid fluctuation caused by EV charging can be reduced, and the stability and reliability of the power grid can be improved; considering the urgency of EV charging, the needs of EVs with low power and in urgent need of charging can be given priority; considering factors such as user charging habits and expected charging time, the personalized needs of users can be met as much as possible, effectively improving user satisfaction. As an intermediary between EVs and the power grid, the Electric Vehicle Aggregator (EVA) plays a vital role. It is responsible for collecting the charging needs of EVs, interacting with the power grid, and scheduling the charging process of EVs according to the decision results of the multi-objective orderly charging model. Through the coordination of EVA, the scale, order and intelligent management of EV charging can be realized. In short, this orderly charging method for EVs based on vehicle-grid interaction realizes efficient, orderly and intelligent management of the charging process of EVs by comprehensively considering multiple factors and introducing EV aggregators as intermediaries, which is of great significance for promoting the popularization of EVs and the intelligent development of power grids.

[0034] Among them, the electric vehicle user data in the method may include user vehicle information, on-board battery charge status, expected departure time and willingness to participate in charging and discharging scheduling; the charging equipment data may include the operating status of the charging equipment, electrical parameters, equipment fault status and charging interface status; the basic data of distribution network operation includes real-time load data, electricity price information and grid stability indicators.

[0035] Among them, in the electric vehicle user data, the user vehicle information includes basic information such as vehicle model and battery capacity. This information helps to understand the charging needs and charging capacity of each electric vehicle and provides a basis for formulating personalized charging strategies. The state of charge (SOC) of the on-board battery reflects the remaining power of the electric vehicle battery in real time and is an important indicator for assessing the urgency of charging. Vehicles with low SOC will be given priority for charging to ensure that they can depart on time or meet other urgent needs. The estimated departure time provided by the user helps to predict the time window of charging demand, so as to reasonably arrange the charging plan and avoid charging when the user is about to leave, resulting in insufficient charging or waste of charging resources. The willingness to participate in charging and discharging scheduling reflects the degree of acceptance of charging flexibility. For users who are willing to participate in charging and discharging scheduling, certain incentives (such as electricity price discounts, point rewards, etc.) can be given to encourage them to cooperate with the scheduling needs of the power grid and achieve more efficient energy utilization.

[0036] In the charging equipment data, the operating status of the charging equipment includes information such as whether the equipment is online and available, which is the basis for ensuring the smooth progress of the charging process. For equipment that is faulty or under maintenance, the system will automatically exclude it as a charging option to avoid inconvenience to users. Electrical parameters such as charging power and charging efficiency directly affect the charging speed and charging cost. By optimizing these parameters, the grid load and user costs can be reduced while ensuring charging efficiency. Real-time monitoring of charging equipment failures helps to promptly discover and deal with potential problems and ensure the safety and reliability of the charging process. The charging interface status includes information such as interface type and whether it is idle, which is crucial for guiding users to choose the appropriate charging pile for charging.

[0037] Among the basic data of distribution network operation, the time-of-use electricity price of the distribution network refers to the division of 24 hours a day into several time periods according to the system operation status, and the electricity fee is charged for each time period according to the average marginal cost of system operation. This electricity price mechanism aims to guide users to optimize electricity consumption through economic means, reduce electricity consumption during peak hours, and increase electricity consumption during valley hours, so as to ensure safe and stable power supply. The specific time division and electricity price standards of time-of-use electricity prices will vary according to the actual conditions of different regions and power grids. The average load power of the node calculated from historical data reflects the average power demand of the node over a period of time. This data is of great significance for the planning and operation of the power system, and can help predict future load demand and formulate reasonable power dispatching strategies. The upper and lower limits of active power output of traditional units (such as coal-fired and gas-fired generators) and new energy units (such as wind power and photovoltaic generators) are determined by multiple factors. For traditional units, the upper limit of their active power is mainly affected by factors such as the output of the prime mover, the mechanical strength of the generator, and the stator current limit; the lower limit may be restricted by factors such as unit stability and operating efficiency. The active power output of new energy units is affected by the randomness, volatility and intermittency of natural resources (such as wind speed and light intensity), and its output upper and lower limits will also change accordingly. Line capacity / node voltage safe operation upper and lower limits, line capacity refers to the maximum power that the line can transmit under safe operating conditions. It is affected by many factors such as line material, cross-section, length and ambient temperature. The upper and lower limits of node voltage safe operation refer to the maximum and minimum values ​​of node voltage fluctuations within the allowable range. These values ​​are determined according to the safe operation standards of the power grid and the technical requirements of the equipment. Exceeding these upper and lower limits may have an adverse effect on the stability of the power grid and the safety of the equipment. The distribution network topology parameters describe the connection relationship and structural characteristics between the various components (such as lines, transformers, switches, etc.) in the distribution network. These parameters play an important role in fault analysis, state estimation, and optimized operation of the distribution network. By obtaining and analyzing the distribution network topology parameters, real-time monitoring and effective management of the distribution network can be achieved. These data together constitute the basic information database for the operation of the distribution network, providing important support for the planning, operation and management of the power system.

[0038] In summary, these data play a vital role in the orderly charging method of electric vehicles. By comprehensively considering these data, a more scientific, reasonable and efficient charging strategy can be formulated to achieve the harmonious coexistence of electric vehicles and the power grid, which is of course not limited to the above-mentioned optimized indicators.

[0039] In addition, the urgency of electric vehicle charging is measured by four indicators: the remaining power of the electric vehicle battery, the charging time, the waiting time for charging, and the charging load demand; the constraints of user satisfaction include electric vehicle capacity constraints, electric vehicle discharge capacity constraints, and electric vehicle power constraints.

[0040] Among them, for the measurement of the urgency of electric vehicle charging, first of all, the lower the remaining power, the higher the charging urgency. This is because electric vehicles with low power need to be charged as soon as possible to ensure that they can continue to drive or meet other needs. Secondly, the user's expected charging time is also an important factor in evaluating the charging urgency. If the user has strict requirements on the charging time (such as the need for fast charging to rush the time), the charging urgency will increase accordingly; in addition, the longer the electric vehicle waits for charging at the charging station, the higher its charging urgency, because the long wait not only wastes the user's time, but also may affect the charging experience of other users; finally, the real-time load of the power grid and the charging load capacity of the charging station will also affect the evaluation of the charging urgency. During high-load periods, the power grid and the charging station may not be able to meet the charging needs of all electric vehicles at the same time, so the charging priority needs to be adjusted according to the load conditions.

[0041] As for the constraints of user satisfaction, firstly, the battery capacity of electric vehicles limits the acceptable charging amount. When formulating charging strategies, it is necessary to ensure that the charging amount does not exceed the battery capacity of electric vehicles to avoid battery damage or safety hazards caused by overcharging. Secondly, although the charging process mainly focuses on the charging amount, the discharge capacity constraint is also part of user satisfaction, which involves the power and capacity limitations of electric vehicles during the discharge process (such as V2G scenarios) to ensure that electric vehicles can provide stable power output when needed. Furthermore, the charging power of electric vehicles is limited by charging equipment and power grids. When formulating charging strategies, it is necessary to ensure that the charging power of each electric vehicle does not exceed the carrying capacity of its charging equipment and power grid to avoid impact on the power grid or damage to the charging equipment. These constraints together constitute the guarantee framework of user satisfaction, ensuring that electric vehicles can meet the charging needs of users during the charging process and ensure the safety and reliability of the charging process.

[0042] In the orderly charging method of electric vehicles, the constraints of charging urgency and user satisfaction are comprehensively considered to develop the optimal charging strategy. By real-time monitoring of the remaining battery power, charging time, waiting time for charging, and the load and electricity price information of the power grid, the system can dynamically adjust the charging priority and charging power to balance the power grid load, reduce user costs and improve user satisfaction. At the same time, by complying with the capacity, discharge capacity and power constraints of electric vehicles, the safety and reliability of the charging process can be ensured.

[0043] The constraints of the pre-built electric vehicle multi-objective orderly charging model include active power constraints, reactive power constraints, distribution network input power constraints, voltage constraints, new energy access constraints and line transmission capacity constraints.

[0044] Among them, the active power constraint limits the active power flow at each node or each line in the power grid. During the charging process of electric vehicles, it is necessary to ensure that the active power absorbed by the charging station from the power grid does not exceed its rated power, and the total active power flow in the power grid does not exceed its safety limit to prevent overload and voltage drop.

[0045] Reactive power is essential to maintain the voltage stability of the power grid. Reactive power constraints require that reactive power exchange in the power grid is within a reasonable range to ensure that the voltage level remains within the permissible range. In the electric vehicle charging scenario, although the charging equipment mainly consumes active power, its impact on reactive power also needs to be considered, especially when using power electronic equipment.

[0046] The power input constraint of the distribution network limits the total amount of power input from the upper power grid to the distribution network. When electric vehicles are connected to the distribution network for charging on a large scale, it is necessary to ensure that the input power of the distribution network does not exceed its design capacity to avoid excessive pressure on the upper power grid or affecting the normal power consumption of other users.

[0047] Voltage constraints require that the voltage of each node in the power grid remain within the allowable range. During the charging process of electric vehicles, the increase in charging load may cause the voltage of some nodes in the power grid to drop. Therefore, it is necessary to ensure that the voltage of each node always meets the requirements of safe and stable operation through reasonable charging scheduling and power grid control strategies.

[0048] With the large-scale access of new energy (such as wind power and photovoltaic power) to the power grid, the uncertainty of its output has brought new challenges to the stable operation of the power grid. Considering the access constraints of new energy in the electric vehicle charging model means that it is necessary to predict the output of new energy and incorporate it into the charging scheduling strategy to ensure that the power grid can still maintain stable operation and meet the charging needs of electric vehicles when the output of new energy fluctuates.

[0049] The line transmission capacity constraint limits the transmission power of each line in the power grid. During the charging process of electric vehicles, it is necessary to ensure that the power transmission through each line does not exceed its design capacity to prevent line overload and damage. This requires the charging scheduling strategy to reasonably distribute the charging load and avoid excessive pressure on key lines in the power grid.

[0050] Example 2 like Figure 2 As shown, the present invention provides a construction process of the above-mentioned electric vehicle multi-objective orderly charging model, comprising the following steps: Step 1: Construct the overall framework of vehicle-grid interaction The overall framework of vehicle-grid interaction is divided into four levels: EV user level, charging equipment level, charging service network operation monitoring level, and smart grid level.

[0051] The EV user layer is composed of various EVs such as taxis, online-hailing cars, buses, and private cars. The charging equipment layer is composed of charging piles and charging stations. The charging service network operation monitoring layer is composed of the regional service network operation monitoring system charging management system and the operator service network operation monitoring system charging management system. The regional service network operation monitoring system charging management system interacts with the charging equipment layer for capability and control information through a communication concentrator or protocol converter. The smart grid layer interacts with the charging service network operation monitoring layer for information such as regulation capability and control strategy, and interacts with power through electric vehicle aggregators.

[0052] Step 2: Adjustable electric vehicle user intelligent identification based on fuzzy C means (FCM) adjustable membership confirmation and support vector machine (SVM).

[0053] The EV user layer obtains information about each user's vehicle, the state of charge of the on-board battery, the expected departure time, and the willingness to participate in the charging and discharging scheduling, and uploads it to the charging equipment layer. The charging equipment layer combines the received EV user layer information with its own charging equipment status information and uploads it to the charging service network operation monitoring layer. The electric vehicle aggregator performs adjustable electric vehicle intelligent identification based on the information about all users and equipment in the jurisdiction provided by the charging service network operation monitoring layer.

[0054] The main factors affecting the responsiveness of electric vehicles include charging power, state of charge (SOC), charging time (the shortest time to reach the ideal state of charge) and access time (the time from grid connection to grid disconnection). User behavior preferences are reflected through characteristic parameters such as vehicle type, battery capacity, average daily power demand, average daily grid connection time, and user points / coupon usage. Based on this, the electric vehicle charging adjustability feature tuple is set as follows: (1) In the formula, is the vehicle model characteristic parameter, is the characteristic parameter of battery capacity, is the characteristic parameter of power demand, Provides user access duration characteristic parameters. is the user incentive characteristic parameter, which is a 0-1 variable. The corresponding calculation method is as follows: (2) In the formula, is the average number of responses of the user for the corresponding feature category of the car-netizen interaction under the i-th feature parameter, and G is the total number of car-netizen interactions organized by the electric vehicle aggregator.

[0055] FCM is used to perform cluster analysis on the samples of typical electric vehicle charging adjustability feature tuples, and the cluster centers of those willing to participate in the response and those not willing to participate in the response are obtained. The objective function in the clustering process is: (3) The constraints are: (4) In the formula, W is the total number of samples, is the sample number identifier, is the number of categories, is a sample with 5-dimensional features, is the first with 5-dimensional features The center of the cluster, For sample Belong to Class membership, single sample The sum of the membership degree of each cluster is 1; is the membership factor; Represents the Euclidean distance from the load sample to the cluster center.

[0056] Membership The calculation method is: (5) Cluster Center The calculation method is: (6) In the formula, k is the category identifier, and its actual meaning is the same as Consistent.

[0057] when When , the iteration terminates. The number of iterations, is the error threshold.

[0058] The cluster centers are: (7) (8) Based on this, the participation response membership and non-participation response membership of electric vehicles are: (9) (10) In the formula, and Represent cluster centers and No. elements.

[0059] Then, the mapping relationship can be constructed by taking the charging adjustability feature tuple of the sample electric vehicle user as input and the actual adjustable attribute of the sample electric vehicle as output. The SVM is trained with typical data, and the trained FCM-SVM model is used to make real-time adjustability judgment on the access situation of electric vehicles.

[0060] Step 3: Establish an EV charging urgency index and prioritize different EV charging options The urgency of electric vehicle charging is measured by four indicators: the remaining power of the electric vehicle battery (State of Charge, SOC), charging time, waiting time for charging, and charging load demand. The lower the remaining power of the electric vehicle battery, the longer the charging time, the shorter the waiting time for charging, the greater the charging load demand, and the higher the urgency of electric vehicle charging. The calculation methods of charging time, waiting time for charging, and charging load demand are: (11) (12) (13) In the formula, is the charging time when electric vehicle i meets the minimum charging requirement, Is the charging waiting time, is the charging power of electric vehicle i during period t. It is the state of charge of electric vehicle i when it has the lowest charging requirement. is the initial state of charge of electric vehicle i, is the capacity of electric vehicle i, is the charging efficiency of the electric vehicle, is the maximum charging power of electric vehicle i. is the starting charging time of electric vehicle i, is the driving-away charging time of electric vehicle i. , , The practical significance of formula (12) can be further expressed as: (14) In the formula, T t The current runtime period.

[0061] Step 4: Establish a multi-objective orderly charging optimization model for electric vehicles Taking into account the distribution network regulation and the charging satisfaction requirements of electric vehicle users, a multi-objective orderly charging model for electric vehicles is established, and its objective function is: (15) In the formula, is the distribution network operating cost, For load fluctuation, Charging satisfaction for electric vehicle users. , , are the weights of each objective function respectively.

[0062] Different orderly charging plans for electric vehicles are formulated based on the urgency of electric vehicle charging demand. Plan 1: When the distribution system can accommodate all electric vehicles, priority is given to electric vehicle charging satisfaction, and electric vehicles are charged in an orderly manner according to the coupling of time periods; Plan 2: When the distribution network cannot fully accommodate all electric vehicles, according to the charging demand urgency index, the charging load of electric vehicles with high urgency is charged first, and then the distribution network regulation demand, voltage constraints and user satisfaction are comprehensively considered to obtain the corresponding global optimization strategy.

[0063] The operating cost of the distribution network is Including distribution network loss cost and operation cost, It is expressed as the minimum load variance, and the specific calculation method is as follows: (16) (17) In the formula, is the current distribution network electricity price, which is determined according to the time-of-use electricity price, N is the number of nodes, is the mutual conductance between node m and node n, is the node voltage of node m at time t; is the node voltage of node n at time t, is the phase angle difference between node m and node n in time interval t, is the number of electric vehicles, is the charging power of electric vehicle i in period t, given by formula (13); is the load demand of node n, T is the time period, is the average power of node n during period T, calculated based on historical data.

[0064] The constraints are: (1) Active power constraints (18) In the formula, is the active power output of the traditional unit at the nth node at time t, is the output of new energy on the nth node at time t, is the load active power of the nth node at time t, The total charging demand for electric vehicles, is the active power loss of the transmission line, and its calculation formula is: (19) In the formula, is the phase angle of the mth node, is the phase angle of the nth node; is the mutual electrical susceptance between the mth node and the nth node (2) Reactive power constraint (20) In the formula, is the reactive power output of the nth node at time t, is the reactive power of the load at the nth node at time t, is the loss of reactive power transmitted between different nodes, and its calculation formula is: (twenty one) (3) Output constraints of traditional units The output constraints of traditional units at different nodes are expressed as: (twenty two) In the formula, , They are the lower and upper limits of the input power of the traditional unit at the distribution network node n at time t.

[0065] (4) Voltage constraint Voltages at different nodes The constraints are expressed as: (twenty three) In the formula, , are the lower and upper limits of the voltage at the nth node at time t respectively.

[0066] (5) Constraints on access to new energy Node output with distributed power supply The constraints are expressed as: (twenty four) In the formula, They are respectively the lower limit and upper limit of the new energy output of the nth node at time t.

[0067] (6) Line transmission capacity constraints The power transmission between different nodes is limited by the line transmission capacity, which can be expressed as: (25) In the formula, is the transmission power limit of the kth line, is the transmission power limit of the kth line when the transmission direction is opposite.

[0068] To maximize the satisfaction of electric vehicle users, the user side aims to minimize the average deviation between the actual state of charge and the expected state of charge when driving away. The calculation method is: (26) (27) In the formula, is the average satisfaction with electric vehicles, is the number of connected electric vehicles, is the charging satisfaction of electric vehicle i, is the state of charge of electric vehicle i when it drives away, is the expected state of charge of electric vehicle i.

[0069] To ensure that each electric vehicle can operate normally, the constraints that need to be met are as follows: (1) Electric vehicle capacity constraints The charge state of an electric vehicle connected to a charging pile needs to meet the following constraints when it leaves the charging pile: (28) In the formula, SOC i is the charge state of electric vehicle i when it leaves the charging station, are the lower and upper limits of the final state of charge of electric vehicle i, respectively.

[0070] (2) Discharge capacity constraints of electric vehicles During the grid-connected phase, electric vehicles can return their own charge to the grid to provide energy support, but they cannot return it unconditionally and must meet the following constraints: (29) In the formula, is the charge state of electric vehicle i at time t, which cannot be 0 at any time.

[0071] (3) Electric vehicle power constraints During the grid-connected charging phase of electric vehicles, the charging power Need to satisfy the constraints: (30) In the formula, The upper limit of the charging power of electric vehicle i.

[0072] Example 3 like Figure 3As shown, it is a schematic diagram of energy flow and information flow of an orderly charging method for electric vehicles based on vehicle-grid interaction established in the present invention. Specifically, the charging station is a natural aggregator. By managing the charging and discharging behavior of EVs in the station, EVs can directly participate in the grid regulation as flexible loads; a certain number of decentralized charging piles are also uniformly managed by the aggregator to participate in the grid aggregation regulation. EV users feedback the willingness of each user to participate in the charging and discharging scheduling, the expected departure time, and the information such as the vehicle and battery of their EVs that the charging equipment is allowed to read and upload. The charging equipment layer combines the received EV user layer information with its own charging equipment status information and uploads it to the charging service network operation monitoring layer. Then, the charging service network operation monitoring layer counts and analyzes all user and equipment related information in the jurisdiction, generally including the total number of users willing to participate in charging and discharging in each period and the available number of charging and swapping equipment, etc., and uploads it to the electric vehicle aggregator. The electric vehicle aggregator performs adjustable user intelligent identification, and divides the user charging priority according to the electric vehicle charging urgency index, and further generates the charging and discharging load curve or interactive capacity interval, and uploads the total load capacity, total charging and discharging power, carrying capacity interval and other information to the smart grid layer.

[0073] The smart grid layer formulates the charging strategy for electric vehicles based on the grid operation status and regulation requirements, as well as the received charging and discharging load curve or capacity range, and sends it to the electric vehicle aggregator, and conducts status monitoring and analysis to facilitate the adjustment of the subsequent charging and swapping plan curve. The electric vehicle aggregator formulates the charging and discharging strategy of each charging management system according to the plan issued by the smart grid layer, and makes subsequent charging and discharging strategy corrections based on the measurement status information uploaded by the charging equipment layer.

[0074] Example 4 like Figure 4 As shown, the present invention discloses an orderly charging system for electric vehicles based on vehicle-grid interaction, comprising: Data acquisition unit: The data acquisition unit is used to acquire electric vehicle user data, charging equipment data and basic distribution network operation data; Strategy determination unit: The strategy determination unit is used to input electric vehicle user layer data, charging equipment layer data and basic distribution network operation data into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces electric vehicle aggregators as intermediaries between electric vehicles and the power grid.

[0075] Among them, the data acquisition unit includes a charging device layer for acquiring electric vehicle user data and a charging device layer for acquiring charging device data; the strategy determination unit includes a smart grid layer for running the pre-built electric vehicle multi-objective orderly charging model; a charging service network operation monitoring layer is also provided between the charging device layer and the smart grid layer; the charging service network operation monitoring layer is used to count and analyze the total number of EV users willing to participate in charging and discharging and the available number of charging devices in each time period; the EV user layer, the charging device layer, the charging service network operation monitoring layer and the smart grid layer communicate in sequence The charging service network operation monitoring layer includes the regional service network operation monitoring system charging management system and the operator service network operation monitoring system charging management system; the regional service network operation monitoring system charging management system communicates with the charging equipment layer through a concentrator or a protocol converter; the operator service network operation monitoring system charging management system communicates with the charging equipment layer through a station-level monitoring system.

[0076] The present invention proposes an orderly charging optimization method for electric vehicles based on vehicle-grid interaction. The method clarifies four levels of vehicle-grid interaction: EV user layer, charging equipment layer, charging service network operation monitoring layer and smart grid layer, constructs a fuzzy C-means-support vector machine model for intelligent identification of EV adjustability, establishes an electric vehicle charging urgency index, divides the charging priorities of different electric vehicles, and constructs a multi-objective orderly charging optimization model for electric vehicles from the perspectives of distribution network operation cost, load fluctuation and user satisfaction. This is conducive to helping power grids and operators make full use of the interactive capabilities of large-scale EVs, meet the charging needs of electric vehicles, improve the operating economy of the distribution system, and smooth out system load fluctuations.

[0077] This invention takes electric vehicle aggregators as the research object, and proposes an overall framework and control process for vehicle-grid interaction; the FCM-SVM model is used to identify the adjustability of electric vehicles, from which the adjustable electric vehicle load is extracted for real-time flexible scheduling; taking into account the requirements of optimal distribution network operation, load fluctuation suppression and user satisfaction, a multi-objective orderly charging optimization model for electric vehicles is constructed. This invention proposes an overall framework and control process for vehicle-grid interaction, realizes information and energy interaction between EVs and power grids, accurately evaluates the actual dispatchability of the EV group through the FCM-SVM model, and prioritizes the charging of adjustable electric vehicles according to the urgency of charging. A charging optimization model is constructed from the perspective of system operation and user satisfaction, which helps to smooth the load curve of the power grid, reduce the fluctuation of the power grid load, enhance the stability and reliability of the power grid, and improve the utilization rate of charging facilities and the flexibility and convenience of users.

[0078] In addition, a schematic diagram of a terminal device is provided in an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0079] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0080] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0081] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0082] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0083] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An orderly charging method for electric vehicles based on vehicle-grid interaction, characterized in that: The following steps are involved: Obtain electric vehicle user data, charging equipment data, and basic distribution network operation data; The electric vehicle user layer data, charging equipment layer data and basic distribution network operation data are input into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces an electric vehicle aggregator as an intermediary between the electric vehicle and the power grid.

2. The method for orderly charging of electric vehicles based on vehicle-grid interaction according to claim 1 is characterized in that: The electric vehicle user data includes user vehicle information, on-board battery charge status, expected departure time and willingness to participate in charging and discharging scheduling; the charging equipment data includes the operating status, electrical parameters, equipment fault status and charging interface status of the charging equipment; the basic distribution network operation data includes the distribution network time-of-use electricity price, the node average load power calculated from historical data, the upper and lower limits of the active power output of traditional units / new energy units, the line capacity / node voltage safe operation upper and lower limits, and the distribution network topology parameters.

3. The method for orderly charging of electric vehicles based on vehicle-grid interaction according to claim 1 is characterized in that: The urgency of electric vehicle charging is measured by four indicators: the remaining power of the electric vehicle battery, the charging time, the waiting time for charging, and the charging load demand; the constraints of user satisfaction include electric vehicle capacity constraints, electric vehicle discharge capacity constraints, and electric vehicle power constraints.

4. The method for orderly charging of electric vehicles based on vehicle-grid interaction according to claim 1 is characterized in that: The constraints of the pre-built electric vehicle multi-objective orderly charging model include active power constraints, reactive power constraints, distribution network input power constraints, voltage constraints, new energy access constraints and line transmission capacity constraints.

5. An orderly charging system for electric vehicles based on vehicle-grid interaction, characterized in that: include: Data acquisition unit: The data acquisition unit is used to acquire electric vehicle user data, charging equipment data and basic distribution network operation data; Strategy determination unit: The strategy determination unit is used to input electric vehicle user layer data, charging equipment layer data and basic distribution network operation data into a pre-built electric vehicle multi-objective orderly charging model to determine the charging strategy of the electric vehicle; the pre-built electric vehicle multi-objective orderly charging model takes into account the distribution network operation cost, load fluctuation, electric vehicle charging urgency and user satisfaction, and introduces electric vehicle aggregators as intermediaries between electric vehicles and the power grid.

6. The electric vehicle orderly charging system based on vehicle-grid interaction according to claim 5 is characterized in that: The data acquisition unit includes a charging device layer for acquiring electric vehicle user data and a charging device layer for acquiring charging device data; the strategy determination unit includes a smart grid layer for running the pre-built electric vehicle multi-objective orderly charging model; a charging service network operation monitoring layer is also provided between the charging device layer and the smart grid layer; the charging service network operation monitoring layer is used to count and analyze the total number of EV users willing to participate in charging and discharging and the number of available charging devices in each time period; The EV user layer, charging equipment layer, charging service network operation monitoring layer and smart grid layer communicate in sequence.

7. The electric vehicle orderly charging system based on vehicle-grid interaction according to claim 6 is characterized in that: The charging service network operation monitoring layer includes a regional service network operation monitoring system charging management system and an operator service network operation monitoring system charging management system; The charging management system of the regional service network operation monitoring system communicates with the charging equipment layer through a concentrator or a protocol converter; the charging management system of the operator service network operation monitoring system communicates with the charging equipment layer through a station-level monitoring system.

8. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method as claimed in any one of claims 1 to 4 are implemented.

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