Electric vehicle charging and discharging scheduling method and system based on cloud side-end cooperation

Abstract

The invention belongs to the technical field of new energy and energy conservation, and relates to an electric vehicle charging and discharging scheduling method and system based on cloud side-end collaboration, and the method comprises the steps: obtaining the information of an electric vehicle, the load of a power grid and the electricity price, carrying out the center layer optimization according to the information, and generating a daily load curve optimization scheme taking the demand of the power grid as priority; according to a daily load curve optimization scheme with power grid demand priority, edge layer optimization of a user side is carried out, an edge layer model optimization result with minimization of the charging and discharging cost of a user as a target is generated, the edge layer model optimization result is fed back to a center layer, and a final electric vehicle charging and discharging optimization scheduling strategy is determined through iterative optimization. Different from centralized control that all computing tasks are completed by a central cloud, cloud edge coordination is realized by additionally arranging an edge server on a network edge side close to a data terminal and utilizing the coordination advantages of cloud computing and edge computing, so that unified scheduling is realized, and the real-time requirement is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy and energy conservation, and relates to an electric vehicle charging and discharging scheduling method and system based on cloud-edge-end collaboration. Background Art

[0002] As a movable distributed energy storage, an electric vehicle (EV) can provide large-scale flexible resources for the power grid, improve the consumption capacity of new energy, and greatly enhance the stability of the operation of the power system. As a controllable load and distributed energy storage of the power system, when parked, electric energy can be transmitted to the power grid through the discharge of its storage battery, realizing two-way energy interaction with the power grid. On the other hand, through orderly charging behavior, line overload can be avoided, the peak-valley difference of the power grid can be reduced, and the economy of the power system can be improved.

[0003] However, in the prior art, for the charging and discharging of electric vehicles, the distribution network dispatching center directly dispatches EVs. With the continuous increase in the number of EVs, the driving and charging behaviors of EVs in the city have more complex information and energy interactions with the power grid, resulting in communication congestion and an increase in the computing pressure of the dispatching center, that is, it is difficult to achieve control when a large number of EVs are connected. In addition, the dispatching architecture is relatively simple. Summary of the Invention

[0004] The purpose of the present invention is to provide an electric vehicle charging and discharging scheduling method and system based on cloud-edge-end collaboration, aiming to solve the problems of communication congestion and computing pressure in the prior art. By introducing edge computing and combining the collaborative advantages of cloud computing and edge computing, the present invention realizes unified scheduling and meets the real-time requirements. To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present application discloses an electric vehicle charging and discharging scheduling method based on cloud-edge-end collaboration, including: Obtaining electric vehicle information, grid load, and electricity price; Performing central layer optimization according to the electric vehicle information, grid load, and electricity price to generate a daily load curve optimization plan with grid demand prioritized; Performing edge layer optimization on the user side according to the daily load curve optimization plan with grid demand prioritized to generate an edge layer model optimization result with the goal of minimizing the charging and discharging cost of users; Feeding back the edge layer model optimization result to the central layer, and determining the final electric vehicle charging and discharging optimization scheduling strategy through iterative optimization.

[0005] Preferably, the central layer optimization is specifically calculated by the following formula:

[0006]

[0007] Wherein, is the objective function of the central layer; represents the load power at time t; represents the average load power at time t; is the number of EVs; Positive and negative values represent the discharging and charging powers at time t.

[0008] Preferably, the constraints for the central layer optimization to generate a daily load curve optimization scheme with grid demand prioritized include: During the charging and discharging process of electric vehicles the limit of the maximum and minimum power needs to be satisfied:

[0009] Set the line transmission power constraint:

[0010] Wherein: is the maximum charging power of the i-th electric vehicle; is the maximum discharging power of the i-th electric vehicle; is the maximum transmission capacity of the line; represents the load power at time t; Positive and negative values represent the discharging and charging powers at time t.

[0011] Preferably, the optimization of the edge layer on the user side is specifically implemented by the following formula:

[0012]

[0013]

[0014] Wherein: is the objective function of the edge layer; , respectively represent the 0-1 parameters for charging and discharging; is the unit control time period length; , are the electricity prices for charging and discharging respectively; , represent the charging and discharging efficiencies respectively; represents the cost loss of the electric vehicle due to the discharging behavior; represents the cyclic charging and discharging power; , represent the linear relationship coefficients between the battery life and the number of cycles respectively; represents the SOC at time period t-1; represents the SOC at time period t.

[0015] Preferably, the constraints for generating the optimization result of the edge layer model with the goal of minimizing the charging and discharging cost of the user include: User travel demand constraint:

[0016] Available time constraint:

[0017] Transformer capacity constraint:

[0018] In the formula, is the state of charge; is set by the user or selects the system default minimum value; is the time period during which the vehicle can participate in orderly charging and discharging and provide available time for scheduling; , represent the start and end times of EV charging and discharging; represents the transformer capacity limit value for this time period; represents the load power at time period t; Positive and negative values represent the discharging and charging power at time period t.

[0019] In the second aspect, the present application discloses an electric vehicle charging and discharging scheduling system based on cloud-edge-end collaboration, including: A vehicle networking platform for obtaining electric vehicle information, grid load, and electricity price; A cloud center layer module for performing central layer optimization according to electric vehicle information, grid load, and electricity price, and generating a daily load curve optimization plan with grid demand prioritized; An edge layer module for performing edge layer optimization on the user side according to the daily load curve optimization plan with grid demand prioritized, and generating an optimization result of the edge layer model with the goal of minimizing the charging and discharging cost of the user; An iterative optimization module for feeding back the optimization result of the edge layer model to the central layer, and determining the final electric vehicle charging and discharging optimization scheduling strategy through iterative optimization.

[0020] Preferably, the cloud center layer module is expressed by the following formula:

[0021]

[0022] During the charging and discharging process of the electric vehicle It is necessary to meet the limit of the power maximum value:

[0023] Set the line transmission power constraint:

[0024] In the formula, is the objective function of the central layer; represents the load power at time t; represents the average load power at time t; is the number of EVs; Positive and negative values represent the discharging and charging powers at time t; is the maximum charging power of the i-th electric vehicle; is the maximum discharging power of the i-th electric vehicle; is the maximum transmission capacity of the line.

[0025] Preferably, the edge layer module is expressed by the following formula:

[0026]

[0027]

[0028] User travel demand constraint:

[0029] Available time constraint:

[0030] Transformer capacity constraint:

[0031] In the formula: is the objective function of the edge layer; , respectively represent the 0-1 parameters of charging and discharging; is the unit control time period length; , are the electricity prices of charging and discharging respectively; , represent the charging and discharging efficiencies respectively; represents the cost lost by the electric vehicle due to the discharging behavior; represents the cyclic charging and discharging power; , represent the linear relationship coefficients between the battery life and the number of cycles respectively; represents the SOC at time t-1; Indicates the SOC at time t; is the state of charge; It is set by the user or selects the system default minimum value; For the vehicle to be able to participate in orderly charging and discharging, and provide available time periods for scheduling; 、 Indicates the start and end charging and discharging times of the EV; Indicates the transformer capacity limit value for this time period.

[0032] In a third aspect, the present application discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for scheduling the charging and discharging of electric vehicles based on cloud-edge-terminal collaboration are implemented.

[0033] In a fourth aspect, the present application discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method for scheduling the charging and discharging of electric vehicles based on cloud-edge-terminal collaboration are implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects: 1) The present application adopts the method of cloud-edge collaboration for optimized scheduling. Different from the centralized control where all computing tasks are completed by the central cloud, cloud-edge collaboration is achieved by adding an edge server on the network edge side close to the data terminal, taking advantage of the collaborative advantages of cloud computing and edge computing, which not only realizes unified scheduling but also meets the real-time requirements; fully utilizes the technical characteristics of edge computing with low latency and strong access to achieve extensive interconnection and interoperability among all links of the source, load, and storage, thereby establishing an organic whole of terminal perception and processing, edge node local analysis and optimization, cloud platform overall massive information deep learning, and comprehensive intelligent decision-making.

[0035] 2) The scheduling method proposed in this paper can achieve load fluctuation suppression, peak shaving and valley filling, etc., to ensure that the system meets multi-objective constraints. Adopting the form of vehicle-grid interaction can not only reduce the charging and discharging costs for users, reduce vehicle cost consumption, and improve users' travel satisfaction, but also improve the operation safety of the distribution network to a greater extent, make the grid load more in line with technical specifications, thereby realizing a win-win pattern of friendly interaction and mutual benefit between EVs and the grid load. Description of the Drawings

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. 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 those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0037] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the technical architecture diagram of the embodiments of the present invention. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0041] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is 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, rather than indicating or implying 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 of the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0042] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0043] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if the terms "set", "installed", "connected", "connected" are used, they 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 directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0044] The present invention will be further described in detail below with reference to the accompanying drawings: See Figure 1 , this application discloses an electric vehicle charging and discharging scheduling method based on cloud-edge-end collaboration, including: S1: Obtain electric vehicle information, grid load, and electricity price; S2: Perform central layer optimization according to the electric vehicle information, grid load, and electricity price to generate a daily load curve optimization plan with grid demand prioritized; S3: According to the daily load curve optimization plan with grid demand prioritized, perform edge layer optimization on the user side to generate an edge layer model optimization result with the goal of minimizing the charging and discharging cost of users; S4: Feed back the edge layer model optimization result to the central layer, and through iterative optimization, determine the final electric vehicle charging and discharging optimization scheduling strategy.

[0045] This application adopts the method of cloud-edge collaboration for optimization scheduling. Different from the centralized control where all computing tasks are completed by the central cloud, cloud-edge collaboration is achieved by adding edge servers on the network edge side close to the data terminal, and taking advantage of the collaboration of cloud computing (central layer optimization) and edge computing (edge layer optimization). It not only realizes unified scheduling but also meets the real-time requirements; fully utilizes the technical characteristics of edge computing with low latency and strong access to achieve extensive interconnection and intercommunication among all links of source, load, and storage, thus establishing an organic whole of terminal perception and processing, edge node local analysis and optimization, cloud platform overall deep learning of massive information, and comprehensive intelligent decision-making. In some embodiments, the central layer optimization is specifically calculated by the following formula:

[0046]

[0047] In the formula, is the objective function of the central layer; represents the load power at time t; represents the average load power at time t; is the number of EVs; Positive and negative values represent the power of discharging and charging during the time period t.

[0048] Further preferably, the constraints for optimizing the central layer to generate an optimized daily load curve scheme with grid demand prioritized include: During the charging and discharging process of electric vehicles The limit of the maximum and minimum power needs to be satisfied:

[0049] Set the line transmission power constraint:

[0050] In the formula: is the maximum charging power of the i-th electric vehicle; is the maximum discharging power of the i-th electric vehicle; is the maximum transmission capacity of the line; represents the load power at time period t; Positive and negative values represent the power of discharging and charging during the time period t.

[0051] In some embodiments, the optimization of the edge layer on the user side is specifically implemented by the following formula:

[0052]

[0053]

[0054] In the formula: is the objective function of the edge layer; , respectively represent the 0-1 parameters of charging and discharging; is the unit control time period length; , are the electricity prices of charging and discharging respectively; , respectively represent the charging and discharging efficiencies; represents the cost loss of the electric vehicle due to the discharging behavior; represents the cyclic charging and discharging power; , respectively represent the linear relationship coefficients between the battery life and the number of cycles; represents the SOC at time period t-1; represents the SOC at time period t.

[0055] Further preferably, the constraints for generating the optimization result of the edge layer model with the goal of minimizing the charging and discharging cost of the user include: User travel demand constraint:

[0056] Available time constraint:

[0057] Transformer capacity constraint:

[0058] In the formula, is the state of charge; is set by the user or selects the system default minimum value; is the time period during which the vehicle can participate in orderly charging and discharging and provide available time periods for scheduling; 、 represent the start and end times of EV charging and discharging; represents the transformer capacity limit value for this time period; represents the load power at time t; Positive and negative values represent the power of discharging and charging at time t.

[0059] In some embodiments, see Figure 2 , the process of the scheduling architecture of the electric vehicle charging and discharging scheduling method based on cloud-edge-end collaboration is as follows: 1) Preparation stage: This stage covers comprehensive information collection and screening work. First, the vehicle networking platform aggregates the available data of all EVs, and through a series of screening processes, identifies the vehicles that meet the scheduling conditions. The screened EV information is then submitted to the distribution cloud platform to lay the foundation for subsequent optimal scheduling work. At the same time, the vehicle networking platform also needs to collect key information such as grid load and electricity price to ensure that scheduling decisions can be made with sufficient data support.

[0060] 2) Central layer optimization: This stage mainly focuses on meeting the requirements of the grid side. By adjusting the charging and discharging power of EVs at different time periods within a day, considering constraint conditions such as transformer capacity limit and EV charging and discharging power, the distribution and main station perform the optimization calculation of the central layer model. The purpose of this stage is to generate an optimized daily load curve plan with grid demand priority. 3) Edge layer optimization: Based on the optimization results of the first stage as a precondition constraint, focusing on the requirements of the user side, the edge layer optimization calculation is carried out. This stage takes minimizing the charging and discharging costs of vehicle owners as the optimization goal, and at the same time considers factors such as EV usage requirements and battery health status, and conducts detailed optimization calculations to obtain the optimal solution of the edge layer model.

[0061] 4) Iterative optimization process: The optimization results of the charging and discharging power of the edge model will be fed back to the central layer, triggering a new round of iterative calculations. Through continuous iteration between the upper and lower layer models, the loop will end until the predetermined termination conditions are met, thereby determining the final EV charging and discharging optimization scheduling strategy. 5) Execution and feedback: The cloud master station conveys the optimized scheduling results to the intelligent fusion terminal, and the fusion terminal collaborates with the EVs participating in the scheduling to execute the optimized scheduling plan. This step ensures the effective implementation of the optimized scheduling strategy in practical applications, realizes the maximization of the grid operation efficiency and the economic benefits of EV users, and promotes a win-win situation between the grid and EV users.

[0062] The design concept of this two-layer optimization model is to maximize the charging demand and economic benefits of EV users on the basis of ensuring the stable and efficient operation of the grid. The central layer of the model is for grid-side optimization, and the main goal is to mitigate the adverse impact of EV charging and discharging activities on the daily load curve of the grid through effective peak shaving and valley filling strategies, further optimize the energy structure and operation mode of the grid, and ensure the efficiency and economy of grid operation. The edge layer model targets EV users, not only aiming to minimize the charging cost, but also taking into account the battery health status and the actual travel needs of users. Through refined charging and discharging scheduling strategies, it seeks to maximize the benefits of both sides while meeting the needs of individual users.

[0063] In some embodiments, the grid-side optimization scheduling strategy of the central layer is as follows: The design of the model takes into account the actual needs and constraints of the grid, and also takes into account the feasibility and flexibility of EV charging and discharging operations. To achieve the optimal management of the grid load, the objective function constructed in this paper aims to minimize the mean square error of the daily grid load.

[0064] (1) (2) In the formula: is the objective function of the central layer; , respectively represent t the load power and the average load power in the time period; The positive and negative values represent the discharging and charging powers in the t time period.

[0065] Constraint conditions: (1) During the charging and discharging process of electric vehicles needs to meet the limit of the maximum and minimum power.

[0066] (3) Wherein: and are respectively the maximum charging and discharging powers of the i th electric vehicle.

[0067] (2) When EVs are centrally connected to the grid for charging, it may cause line overload, so line transmission power constraints are set.

[0068] (4) Wherein: is the maximum transmission capacity of the line.

[0069] In some embodiments, the edge layer user - side optimal scheduling strategy is as follows: The objective function aims to minimize the overall charging and discharging cost of users. This not only covers the direct cost of purchasing electricity from the grid, but also includes the possible income from selling electricity to the grid through V2G technology. The objective function is shown in Equation (5), which accurately depicts the total cost of users' charging and discharging decisions based on time - of - use electricity prices throughout the day, and at the same time takes into account the long - term impact of EV battery loss on the total cost of users in the charging and discharging strategy.

[0070] (5) (6) (7) Wherein: is the objective function of the edge layer; and respectively represent the 0 - 1 parameters of charging and discharging; is the unit control time period length; and are respectively the electricity prices of charging and discharging; and respectively represent the charging and discharging efficiencies; represents the cost of loss due to the discharging behavior of the electric vehicle; represents the cyclic charging and discharging electricity; and respectively represent the linear relationship coefficients between battery life and the number of cycles; and respectively represent the SOC at time t and time t - 1.

[0071] Constraint conditions: (1) User travel demand constraint: (8) Wherein: is the state of charge; is set by the user or selects the system default minimum value.

[0072] (2) Available time constraint: Given that EVs are parked most of the time, V2G technology thus has great application potential. However, even if a day is divided into 96 time periods, it is impossible for EVs to be connected to the grid for energy exchange at all times. Therefore, clarifying the time range during which EVs can be used for scheduling is a key step in implementing an effective V2G strategy. This requires accurately defining the schedulable time of EVs to ensure the feasibility of energy exchange. The acceptable schedulable time constraint range of EVs is shown in Equation (9).

[0073] (9) In the formula: is the time period during which the vehicle can participate in orderly charging and discharging and provide available time for scheduling; , represent the start and end times of EV charging and discharging.

[0074] (3) Transformer capacity constraint During the implementation of V2G scheduling, it is necessary to ensure that the capacity of the transformers in the power grid will not be overloaded. This constraint condition is to ensure the stability and security of the power grid and avoid damage to power facilities or power grid failures caused by overload.

[0075] (10) In the formula: represents the transformer capacity limit value for this time period.

[0076] Embodiment Considering the actual usage situation of domestic EVs and their diverse influencing factors, this paper selects a residential area in Beijing as a specific case and conducts simulation verification on the MATLAB platform. This is aimed at ensuring the effectiveness and implementation feasibility of the proposed EV charging and discharging scheduling strategy in real application scenarios. The parameter configuration of the simulation is as follows: (1) Simulation environment configuration: In this environment, the intelligent charging device is set as the key device to control the charging and discharging status of EVs. The power supply is provided by four transformers with a power factor of 0.85 and a capacity of 1600 kVA. The operating efficiency of these transformers is as high as 95%, ensuring the high efficiency of power supply. (2) EV setting: The simulation considers 500 EVs. The battery capacity of each vehicle is set to 56.75 kW·h, and the average power consumption per 100 kilometers is 20 kW·h, simulating the energy consumption under actual driving conditions. The charging and discharging power is set to 7 kW, and the charging and discharging process efficiency is 90%, reflecting the actual losses during the energy conversion process. (3) Charging mode: The model assumes that the EV is charged once a day to ensure that the remaining battery capacity after each charge or discharge can meet the daily travel needs of users. At the same time, considering the battery replacement cost, the impact of battery replacement on economy during long-term operation is evaluated. (4) Electricity price setting: The simulation model sets the electricity price according to the time-of-use electricity price policy for EV charging and discharging in residential areas of Beijing. The detailed peak-valley electricity price information is shown in Table 1.

[0077] Table 1 Time-of-use electricity price for EV charging and discharging in residential areas of Beijing

[0078] Based on the above scheduling architecture and simulation parameters, the following optimization results are obtained after MATLAB simulation calculation: 1. Optimization effect of grid-side load Comparison of daily load curves: Load reduction during peak hours: After optimization, the total load during peak hours (8:00 - 12:00, 17:00 - 21:00) decreased by 18.5%. This is mainly due to the fact that the EV discharges about 3.2 MW during this period, relieving the grid pressure.

[0079] Charging increase during valley hours: The EV charges concentratedly during valley hours (0:00 - 8:00), and the charging power reaches 4.1 MW, accounting for 72% of the total daily charging amount, making full use of low-price electricity.

[0080] Transformer load balancing: The peak load rates of the four transformers decreased from 92% before optimization to 78%, and the capacity limit was not triggered.

[0081] 2. Economic analysis on the user side Daily average cost of a single EV: Charging cost: The charging cost during valley hours is 18.14 yuan (50.4 kWh × 0.36 yuan / kWh).

[0082] Discharge income: The discharge income during peak hours is 25.2 kWh × 0.64 yuan / kWh = 16.13 yuan.

[0083] Net cost: Through the price difference between charging and discharging, the user's net cost is reduced to 2.01 yuan (when not optimized, the charging cost alone is 50.4 kWh × 0.36 = 18.14 yuan), saving 88.9%.

[0084] Total income of the whole community: The total daily discharge income of 500 EVs reaches 8,065 yuan, the charging cost is 9,070 yuan, and the net cost is 1,005 yuan, saving 96% compared with the non-scheduled scenario (only the charging cost is 25,200 yuan).

[0085] 3. Time distribution of EV charging and discharging Charging period: Valley period (0:00 - 8:00): 80% of EVs complete charging, with an average charging duration of 6.5 hours, and the battery is charged to 90% SOC.

[0086] Discharging period: Peak period (8:00 - 12:00): 50% of EVs discharge, with an average discharge power of 6.3 kW and a duration of 2 hours.

[0087] Peak period (17:00 - 21:00): 70% of EVs discharge, with an average discharge power of 5.8 kW and a duration of 3 hours.

[0088] Battery health protection: The number of charge - discharge cycles is limited to 1 time per day, and the SOC is maintained in the range of 20% - 90% to extend the battery life.

[0089] This optimized scheduling scheme successfully balances the load demand on the grid side and the economy on the user side, significantly reduces the peak - valley difference of the grid, makes the transformer operation safer; greatly reduces the user's charging cost, and approaches zero - cost electricity through peak - valley arbitrage; optimizes battery health management and extends the service life; this scheme verifies the feasibility and efficiency of the hierarchical iterative scheduling strategy in the residential area scenario.

[0090] This application discloses an electric vehicle charging and discharging scheduling system based on cloud - edge - terminal collaboration, including: The vehicle - to - everything (V2X) platform is used to obtain electric vehicle information, grid load, and electricity price; The cloud - center - layer module is used to perform center - layer optimization based on electric vehicle information, grid load, electricity price and other information, and generate an optimized daily load curve scheme with grid demand as the priority; The edge - layer module is used to perform edge - layer optimization on the user side according to the optimized daily load curve scheme with grid demand as the priority, and generate an optimized result of the edge - layer model with the goal of minimizing the user's charging and discharging cost; The iterative optimization module is used to feedback the optimized result of the edge - layer model to the center - layer, and determine the final optimized scheduling strategy for electric vehicle charging and discharging through iterative optimization.

[0091] In some embodiments, the cloud - center - layer module is expressed by the following formula:

[0092]

[0093] During the charging and discharging process of electric vehicles It is necessary to meet the limit of the maximum and minimum power:

[0094] Set the line transmission power constraint:

[0095] In the formula, is the objective function of the central layer; represents the load power at time period t; represents the average load power at time period t; is the number of EVs; Positive and negative values represent the discharging and charging powers at time period t; is the maximum charging power of the i-th electric vehicle; is the maximum discharging power of the i-th electric vehicle; is the maximum transmission capacity of the line.

[0096] In some embodiments, the edge layer module is expressed by the following formula:

[0097]

[0098]

[0099] User travel demand constraint:

[0100] Available time constraint:

[0101] Transformer capacity constraint:

[0102] In the formula: is the objective function of the edge layer; , respectively represent the 0-1 parameters of charging and discharging; is the unit control time period length; , are the electricity prices of charging and discharging respectively; , respectively represent the charging and discharging efficiencies; represents the cost loss of the electric vehicle due to discharging behavior; represents the cyclic charge and discharge power; , are the linear relationship coefficients between battery life and number of cycles respectively; represents the SOC at time period t-1; represents the SOC at time period t; is the state of charge; is set by the user or selects the system default minimum value; For the vehicle to be able to participate in orderly charging and discharging and provide available time periods for scheduling; and represent the start and end times of EV charging and discharging; represents the transformer capacity limit for this time period.

[0103] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for scheduling electric vehicle charging and discharging based on cloud-edge-end collaboration described in any one of the above are implemented.

[0104] The present invention also discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for scheduling electric vehicle charging and discharging based on cloud-edge-end collaboration described in any one of the above are implemented.

[0105] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take 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.) containing computer-usable program code.

[0106] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0107] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for the functions specified in one block or a plurality of blocks.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for scheduling electric vehicle charging and discharging based on cloud-edge-end collaboration, characterized in that: include: Obtain electric vehicle information, grid load and electricity price; Perform central layer optimization based on electric vehicle information, grid load and electricity price, and generate a daily load curve optimization plan that prioritizes grid demand; According to the daily load curve optimization scheme that prioritizes grid demand, the edge layer on the user side is optimized to generate edge layer model optimization results that aim to minimize the user's charging and discharging costs; The optimization results of the edge layer model are fed back to the central layer, and the final electric vehicle charging and discharging optimization scheduling strategy is determined through iterative optimization.

2. According to the method of claim 1, the charging and discharging scheduling method of electric vehicles based on cloud-edge-end collaboration is characterized in that: The central layer optimization is specifically calculated by the following formula: In the formula, is the objective function of the central layer; represents the load power during period t; represents the average load power during period t; is the number of EVs; The positive and negative values ​​represent the discharging and charging power in the period t.

3. According to claim 2, a method for scheduling electric vehicle charging and discharging based on cloud-edge-end collaboration is characterized in that: Central layer optimization, generating daily load curve optimization scheme with grid demand priority, includes the following constraints: Electric vehicle charging and discharging The maximum power limit must be met: Set the line transmission power constraint: Where: The maximum charging power for the i-th electric vehicle; is the maximum discharge power of the i-th electric vehicle; is the maximum transmission capacity of the line; represents the load power during period t; The positive and negative values ​​represent the discharging and charging power in the period t.

4. According to the method of claim 1, the charging and discharging scheduling method of electric vehicles based on cloud-edge-end collaboration is characterized in that: The edge layer optimization on the user side is specifically implemented by the following formula: Where: is the objective function of the edge layer; , Represents the 0-1 parameters of charge and discharge respectively; Control the time period length for the unit; , are the electricity prices for charging and discharging respectively; , Respectively represent the efficiency of charge and discharge; Represents the cost of electric vehicle losses due to discharge behavior; Indicates the cycle charge and discharge capacity; , Respectively represent the linear relationship coefficients between battery life and cycle number; represents the SOC at time t-1; Represents the SOC during period t.

5. According to the method of claim 4, the charging and discharging scheduling method of electric vehicles based on cloud-edge-end collaboration is characterized in that: The constraints for generating edge layer model optimization results with the goal of minimizing the user's charging and discharging costs include: User travel demand constraints: Available time constraints: Transformer capacity constraints: In the formula, is the state of charge; The system default minimum value is set or selected by the user; To enable vehicles to participate in orderly charging and discharging, and provide available time slots for scheduling; , Indicates the start and end time of EV charging and discharging; Indicates the transformer capacity limit for this period; represents the load power during period t; The positive and negative values ​​represent the discharging and charging power in the period t.

6. An electric vehicle charging and discharging scheduling system based on cloud-edge-end collaboration, characterized in that: include: Internet of Vehicles platform, used to obtain electric vehicle information, grid load and electricity price; The cloud-based central layer module is used to optimize the central layer based on electric vehicle information, grid load and electricity price, and generate a daily load curve optimization plan that prioritizes grid demand; The edge layer module is used to optimize the edge layer on the user side according to the daily load curve optimization scheme that prioritizes grid demand, and generate edge layer model optimization results that aim to minimize the user's charging and discharging costs; The iterative optimization module is used to feed back the optimization results of the edge layer model to the central layer, and determine the final electric vehicle charging and discharging optimization scheduling strategy through iterative optimization.

7. The electric vehicle charging and discharging scheduling system based on cloud-edge-end collaboration according to claim 6 is characterized in that: The cloud center layer module is expressed as follows: Electric vehicle charging and discharging The maximum power limit must be met: Set the line transmission power constraint: In the formula, is the objective function of the central layer; represents the load power during period t; represents the average load power during period t; is the number of EVs; Positive and negative values ​​represent the discharging and charging power in period t; The maximum charging power for the i-th electric vehicle; is the maximum discharge power of the i-th electric vehicle; is the maximum transmission capacity of the line.

8. The electric vehicle charging and discharging scheduling system based on cloud-edge-end collaboration according to claim 6 is characterized in that: The edge layer module is expressed as follows: User travel demand constraints: Available time constraints: Transformer capacity constraints: Where: is the objective function of the edge layer; , Represents the 0-1 parameters of charge and discharge respectively; Control the time period length for the unit; , are the electricity prices for charging and discharging respectively; , Respectively represent the efficiency of charge and discharge; Represents the cost of electric vehicle losses due to discharge behavior; Indicates the cycle charge and discharge capacity; , Respectively represent the linear relationship coefficients between battery life and cycle number; represents the SOC at time t-1; represents the SOC at time t; is the state of charge; The system default minimum value is set or selected by the user; To enable vehicles to participate in orderly charging and discharging, and provide available time slots for scheduling; , Indicates the start and end time of EV charging and discharging; Indicates the transformer capacity limit for this period.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the electric vehicle charging and discharging scheduling method based on cloud-edge-end collaboration as described in any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the electric vehicle charging and discharging scheduling method based on cloud-edge-end collaboration as described in any one of claims 1 to 5.

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