Flexible control method, device, system and equipment for orderly charging of electric vehicle

Abstract

The invention provides a flexible control method, device, system and equipment for orderly charging of an electric vehicle, and is applied to the technical field of power utilization of a novel power system. The method comprises the steps of obtaining ordered charging participation willingness information of each user in a charging area of a target area, and generating an ordered charging control strategy in real time based on the ordered charging participation willingness information, an operation state of each private charging pile in the charging area in a current optimization period and an optimization control model; based on the ordered charging control strategy, calling an electricity utilization information acquisition system to send a corresponding control instruction to each private charging pile in the charging area through the target communication link so as to control the corresponding electric vehicle to be charged in order; the optimization control model is a convex optimization model with the purpose of minimizing the total load of the charging area. The problems that the user side of existing electric vehicle charging rigid control is not friendly, the enthusiasm of users to participate in regulation and control is low, and the power supply and demand regulation and control effect is poor are solved.

Description

Technical Field

[0001] This invention relates to the field of new power system electricity technology, specifically to a flexible control method, device, system, and equipment for orderly charging of electric vehicles. Background Technology

[0002] With the rapid development of my country's new energy vehicle industry, the power grid is facing a large-scale demand for electric vehicle charging. At the same time, the large-scale integration of intermittent renewable energy sources such as wind and solar power has exacerbated the imbalance between power grid supply and demand. Among these imbalances, those occurring during specific periods are particularly prominent. In this context, mobilizing all elements across the power grid, power sources, load, and energy storage to achieve coordinated supply and demand is of paramount importance.

[0003] Currently, the regulation of electric vehicles in residential areas involves forcibly cutting off power to private charging stations using prepaid electricity meters. This rigid control lacks consideration for the dynamic nature of grid demand and is inconvenient for residents charging their vehicles. It severely impacts users' willingness to choose green transportation and participate in regulation, thereby affecting the effectiveness of electricity supply and demand regulation. Summary of the Invention

[0004] To overcome the user-unfriendly nature of rigid control for electric vehicle charging and the resulting low user participation and poor power supply and demand regulation, this invention provides a flexible control method, device, system, and equipment for orderly charging of electric vehicles.

[0005] On one hand, the present invention provides a flexible control method for orderly charging of electric vehicles, comprising: Obtain information on the orderly charging participation intentions of each user in the charging station area of ​​the target area. Based on the orderly charging participation intentions of each user and the real-time reporting of the operating status of each private charging pile in the charging station area in the current optimization cycle and the optimization control model, generate an orderly charging control strategy for the current optimization cycle in real time. Based on the orderly charging control strategy of the current optimization cycle, the electricity information collection system is invoked to send corresponding control commands to each private charging pile in the charging area through the target communication link, so as to control the corresponding electric vehicles to charge in an orderly manner. The target communication link is an ordered charging control channel for each electric vehicle in the charging area, constructed based on the electricity information collection system; the optimization control model is a convex optimization model with the objective of minimizing the total load of the charging area; the operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area; the historical operating status of each private charging pile is obtained through the target communication link.

[0006] Optionally, the optimized control model includes the following construction steps: Based on the sensitivity of the sum of squares function to the peak-valley difference of the charging station area, an objective function is constructed with the goal of minimizing the sum of squares of the total load of the charging station area. Based on user charging needs, the charging power limits of each private charging station, the transformer capacity of the charging area, and the charging continuity of each private charging station, corresponding constraints are set for the objective function. Based on the objective function and the constraints, the optimized control model is obtained.

[0007] Optionally, the objective function is as follows: ; in, Let T be the objective function and T be the optimization period. The basic load of residents during time period t; Let N be the charging power of the i-th electric vehicle in the charging area at time t, and let N be the number of electric vehicles in the charging area.

[0008] Optionally, the user's intention to participate in orderly charging includes the user's desired charging period and the user's desired target charging capacity; the constraints include user charging demand constraints and charging continuity constraints. The user charging demand constraints are as follows: ; in, The grid connection time of the i-th electric vehicle within the charging station area; Let be the time when the i-th electric vehicle leaves the charging station area. , These are determined based on the user's expected charging time period corresponding to the i-th electric vehicle; The duration of each optimization period; This represents the total amount of electricity that the i-th electric vehicle in the charging area needs to be charged. It is determined based on the target battery capacity expected by the user corresponding to the i-th electric vehicle; Let be the battery capacity of the i-th electric vehicle within the charging station area; The charging continuity constraint is as follows: ; in, The cumulative number of charging status changes for the i-th electric vehicle within the charging station area at time t. This represents the charging status of the i-th electric vehicle within the charging station area at time t. The charging status of the i-th electric vehicle in the charging station area at time t-1; The value can be 0 or 1. This indicates that the i-th electric vehicle in the charging area is charging at time t. This indicates that the i-th electric vehicle in the charging station area is in a non-charging state at time t; This sets the upper limit on the number of times an electric vehicle's charging status can change within the optimization period. The value is a positive even number.

[0009] Optionally, based on the information on each user's willingness to participate in orderly charging and the operating status of each private charging pile in the charging area during the current optimization cycle, as well as the optimization control model, an orderly charging control strategy for the current optimization cycle is generated in real time, including: The optimized control model is transformed into a quadratic programming model in matrix form. Based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging area in the current optimization cycle, the corresponding solver is called to solve the quadratic programming model to obtain the optimal charging power of each electric vehicle in the current optimization cycle. Based on the optimal charging power of each electric vehicle in the current optimization cycle, an ordered charging control strategy for the current optimization cycle is generated in real time.

[0010] Optionally, obtaining the orderly charging participation intention information of each user within the charging station area of ​​the target region includes: Receive ordered charging messages from each user within the charging station area sent by the target application; By parsing the orderly charging messages of each user, information on the orderly charging participation intentions of each user within the charging station area can be obtained. Among them, the orderly charging message of each user is generated based on the orderly charging interface corresponding to the orderly charging function module of the target application where each user performs the target operation.

[0011] Optionally, the target communication link includes an ordered charging access unit corresponding to each private charging pile in the charging station area; the ordered charging access unit communicates with the corresponding private charging pile through a target communication protocol. The target communication protocol is a private communication protocol built between each private charging pile and its corresponding ordered charging unit.

[0012] Optionally, after invoking the electricity consumption information collection system to send corresponding control commands to each private charging pile in the charging area through the target communication link to control the corresponding electric vehicle to perform orderly charging based on the orderly charging control strategy of the current optimization cycle, the method further includes: Receive the control result feedback information transmitted back through the target communication link; Based on the error between the feedback information of the regulation result and the corresponding orderly charging control strategy, the orderly charging control strategy is updated.

[0013] On the other hand, the present invention also provides a flexible control device for orderly charging of electric vehicles, applied to the cloud side, comprising: The strategy generation module is used to obtain the orderly charging participation intention information of each user in the charging station area of ​​the target area, and generate the orderly charging control strategy for the current optimization period in real time based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging station area in the current optimization period and the optimization control model reported in real time by the charging station area. The orderly charging control module is used to call the electricity information collection system to send corresponding control commands to each private charging pile in the charging area through the target communication link based on the orderly charging control strategy of the current optimization cycle, so as to control the corresponding electric vehicle to charge in an orderly manner. The target communication link is an ordered charging control channel for each electric vehicle in the charging area, constructed based on the electricity information collection system; the optimization control model is a convex optimization model with the objective of minimizing the total load of the charging area; the operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area; the historical operating status of each private charging pile is obtained through the target communication link.

[0014] Optionally, it also includes an optimized model building module, which includes: The objective function construction submodule is used to construct an objective function based on the sensitivity of the sum of squares function to the peak-valley difference of the charging station area, with the goal of minimizing the sum of squares of the total load of the charging station area; The constraint construction submodule is used to set corresponding constraints for the objective function based on user charging needs, charging power limits of each private charging pile, transformer capacity of the charging area, and charging continuity of each private charging pile. The model building submodule is used to obtain the optimized control model based on the objective function and the constraints.

[0015] Optionally, the objective function is as follows: ; in, Let T be the objective function and T be the optimization period. The basic load of residents during time period t; Let N be the charging power of the i-th electric vehicle in the charging area at time t, and let N be the number of electric vehicles in the charging area.

[0016] Optionally, the user's intention to participate in orderly charging includes the user's desired charging period and the user's desired target charging capacity; the constraints include user charging demand constraints and charging continuity constraints. The user charging demand constraints are as follows: ; in, The grid connection time of the i-th electric vehicle within the charging station area; Let be the time when the i-th electric vehicle leaves the charging station area. , These are determined based on the user's expected charging time period corresponding to the i-th electric vehicle; The duration of each optimization period; This represents the total amount of electricity that the i-th electric vehicle in the charging area needs to be charged. It is determined based on the target battery capacity expected by the user corresponding to the i-th electric vehicle; Let be the battery capacity of the i-th electric vehicle within the charging station area; The charging continuity constraint is as follows: ; in, The cumulative number of charging status changes for the i-th electric vehicle within the charging station area at time t. This represents the charging status of the i-th electric vehicle within the charging station area at time t. The value can be 0 or 1. This indicates that the i-th electric vehicle in the charging area is charging at time t. This indicates that the i-th electric vehicle in the charging station area is in a non-charging state at time t; This sets the upper limit on the number of times an electric vehicle's charging status can change within the optimization period. The value is a positive even number.

[0017] Optionally, the strategy generation module includes: The matrix transformation submodule is used to transform the optimization control model into a quadratic programming model in matrix form. The solution submodule is used to solve the quadratic programming model by calling the corresponding solver based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging area in the current optimization period, so as to obtain the optimal charging power of each electric vehicle in the current optimization period. The strategy generation submodule is used to generate an ordered charging control strategy for the current optimization period in real time based on the optimal charging power of each electric vehicle in the current optimization period.

[0018] Optionally, the strategy generation module includes: The receiving submodule is used to receive the ordered charging messages from each user in the charging station area sent by the target application; The parsing submodule is used to parse the orderly charging messages of each user to obtain the orderly charging participation intention information of each user in the charging area. Among them, the orderly charging message of each user is generated based on the orderly charging interface corresponding to the orderly charging function module of the target application where each user performs the target operation.

[0019] Optionally, the target communication link includes an ordered charging access unit corresponding to each private charging pile in the charging station area; the ordered charging access unit communicates with the corresponding private charging pile through a target communication protocol. The target communication protocol is a private communication protocol built between each private charging pile and its corresponding ordered charging unit.

[0020] Optionally, it also includes a feedback update module, which is used for: Receive the control result feedback information transmitted back through the target communication link; Based on the error between the feedback information of the regulation result and the corresponding orderly charging control strategy, the orderly charging control strategy is updated.

[0021] On the other hand, the present invention also provides a flexible control system for orderly charging of electric vehicles, including the flexible control device for orderly charging of electric vehicles described in the above embodiments.

[0022] Optionally, it also includes: a charging station control terminal and an orderly charging access unit located in the charging station area, as well as each private charging pile in the charging station area; the orderly charging access unit is electrically connected to the charging station control terminal and the corresponding private charging pile respectively. The control terminal of the transformer area is used to receive the orderly charging control command generated by the orderly charging control strategy based on the current optimization cycle issued by the electric vehicle orderly charging flexible control device on the cloud side, and forward the orderly charging control command to the corresponding orderly charging access unit. The orderly charging access unit is used to parse the received orderly charging control command and send the control command to the corresponding private charging pile based on the parsing result. The private charging station is used to respond to the control command and send a power adjustment command to the electric vehicle bound to the private charging station.

[0023] Optionally, the orderly charging access unit is installed on the side of the corresponding private charging pile or integrated into the smart meter corresponding to the private charging pile.

[0024] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method described in any of the foregoing is implemented.

[0025] On the other hand, the present invention also provides a readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the method described in any one of the above.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a flexible control method, device, and system for orderly charging of electric vehicles. Based on users' willingness to participate in orderly charging, the operating status of private charging piles within the charging area reported by the charging area during the current optimization cycle, and an optimized control model, an orderly charging control strategy for the current optimization cycle is generated in real time. By fully considering users' willingness to participate in orderly charging, a user-friendly control process is achieved, which can motivate electric vehicle users to participate in grid regulation and green travel, thereby improving the user-side regulation effect. Simultaneously, the optimized control model aims to minimize the total load of the charging area, using the charging area as a starting point to reduce grid operation fluctuations caused by large-scale electric vehicle grid access at the charging area level, thus ensuring the grid-side regulation effect.

[0027] The optimized control model constructed in this invention is a convex optimization model with the objective of minimizing the total load of the charging station area. It can quickly solve the global optimal solution to the optimization problem, thereby enabling real-time control strategy generation and control processes at the minute level. This adapts to the highly variable user demand in target areas (such as residential areas), allowing the control strategy to quickly match changes in user demand and achieving flexible control on the user side, thus increasing user participation and demonstrating strong feasibility. Furthermore, the target communication link used for orderly charging control in this invention is constructed by reusing existing electricity information collection systems, which avoids new hardware investment and modifications to existing power control systems, facilitating widespread implementation. Attached Figure Description

[0028] Figure 1 This is one of the flowcharts of a flexible control method for orderly charging of electric vehicles according to the present invention; Figure 2 This is a schematic diagram of a target communication link for orderly charging according to the present invention; Figure 3 This is a second schematic diagram of the process of a flexible control method for orderly charging of electric vehicles according to the present invention; Figure 4This is a system architecture diagram of an electric vehicle orderly charging flexible control system according to the present invention; Figure 5 A schematic diagram of a vehicle charging power control strategy curve, as an example of the present invention. Figure 6 This is a schematic diagram illustrating the transformer load conditions before and after using the present invention, as an example of the present invention. Figure 7 This is a structural block diagram of a power equipment according to the present invention. Detailed Implementation

[0029] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Example 1 The present invention provides a flexible control method for orderly charging of electric vehicles, the schematic diagram of which is shown below. Figure 1 As shown, the method includes: Step S110: Obtain the orderly charging participation intention information of each user in the charging station area of ​​the target area; based on the orderly charging participation intention information of each user and the real-time reported operating status of each private charging pile in the charging station area in the current optimization cycle and the optimization control model, generate the orderly charging control strategy for the current optimization cycle in real time. Step S120: Based on the orderly charging control strategy of the current optimization cycle, the electricity information collection system is invoked to send corresponding control commands to each private charging pile in the charging area through the target communication link, so as to control the corresponding electric vehicle to charge in an orderly manner.

[0031] In this example implementation, the target area can be a residential area or a commercial cluster area. Users within the target area have private charging piles, and each private charging pile is linked to at least one of the user's electric vehicles. The private charging piles within the target area are powered by the distribution transformer of the corresponding charging station area. The user's willingness to participate in orderly charging can include whether the user is willing to participate in orderly charging control, the user's charging needs, and vehicle usage needs. For example, charging needs and vehicle usage needs can include the user's usage time period or the user's acceptable charging time period, as well as the user's desired target SOC (State of Charge). The execution entity of this method can be the cloud side, which can perform orderly charging control of the corresponding electric vehicles through information interaction between the cloud side, the charging station area, and the corresponding private charging pile, realizing orderly charging control through "cloud network-station area-vehicle-charging pile" collaboration. The current optimization period can be a future period, such as the next 24 hours. A 24-hour charging plan can be formulated for each electric vehicle in the area, and the control strategy can be refined to multiple time periods, such as 24 time periods. The target communication link is an orderly charging control channel for each electric vehicle within the charging station area, constructed based on an electricity consumption information collection system; the electricity consumption information collection system can be any existing electricity consumption collection system within the power system. The optimized control model is a convex optimization model with the objective of minimizing the total load of the charging area. The operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area. For example, the operating status of each private charging pile in the future time period can be predicted by various neural network models. The historical operating status of each private charging pile is obtained through the target communication link, that is, the historical operating status of private charging piles is collected through the target communication link. The "cloud network-charging area-vehicle-pile" orderly charging flexible control system designed in this invention takes the residential area as the entry point, makes full use of the existing communication links in the area, and opens up a two-way communication link between the user (vehicle), the orderly charging pile, the intelligent terminal on the charging area side, and the main station platform. This invention performs orderly charging control through the collaboration of "cloud network-charging area-vehicle-pile". Users can submit their willingness to participate in orderly charging through interactive applications. The cloud-side control main station obtains the status of each charging pile in real time, and dynamically generates an orderly charging control strategy by combining user selection, equipment parameters, and charging area load data. The orderly charging control strategy controls the three-level transmission link of the main station-area concentrator or converged terminal-ordered charging pile access unit. Finally, the charging pile receives the instruction and outputs a real-time current limit to the on-board charger through PWM (Pulse Width Modulation) signal. The on-board charger then accurately adjusts the charging power accordingly. Through closed-loop control, it achieves bidirectional adaptation between the area load optimization and user charging demand, effectively avoiding the shortcomings of rigid control, manual power restoration, and manual authentication required by traditional fee control technology, thus improving the user experience.

[0032] In some implementations, the optimized control model includes the following construction steps: Based on the sensitivity of the sum of squares function to the peak-valley difference of the charging station area, an objective function is constructed with the goal of minimizing the sum of squares of the total load of the charging station area. Based on user charging needs, the charging power limits of each private charging station, the transformer capacity of the charging area, and the charging continuity of each private charging station, corresponding constraints are set for the objective function. Based on the objective function and the constraints, the optimized control model is obtained.

[0033] In this example implementation, considering the high sensitivity of the sum of squares function to the peak-valley difference in the charging area load, the sum of squares of the load is selected as the objective function, which can reduce the peak-valley difference in the charging area load. Simultaneously, the core of the objective function is the total load of the charging area, which reflects the load fluctuation at each moment. By minimizing the sum of squares of the total load of the charging area as the objective, the load curve is smoothed. The combination of the total load and the sum of squares function amplifies the penalty term for peak loads, forcing the optimization model to distribute charging periods and avoid power concentration. Smoothing the charging area load curve reduces the risk of transformer overload, reduces equipment losses and grid expansion costs, and improves power supply reliability. Furthermore, the sum of squares function naturally constitutes a convex quadratic programming problem, which can be quickly solved using mature commercial solvers (such as Gurobi and CPLEX), ensuring the real-time nature of the optimization process. To ensure that the optimization control strategy is solvable and practically meaningful, constraints need to be set on control variables such as user charging demand, charging power limits of each private charging pile, transformer capacity of the charging area, and charging continuity. Therefore, the constraints may include user charging demand constraints, charging power constraints of each private charging pile, transformer capacity constraints of the charging area, and charging continuity constraints of each private charging pile.

[0034] For example, this example uses minimizing the sum of squares of the total load in the distribution area as the objective function, aiming to make the total load curve of the power grid as smooth as possible. The specific objective function is: (1) in, Let T be the objective function and T be the optimization period. The basic load of residents during time period t; Let N be the charging power of the i-th electric vehicle in the charging area at time t, and N be the number of electric vehicles in the charging area. For example, T represents the next 24 hours (96 prediction points in total, with a 15-minute interval between adjacent prediction points). The orderly charging control strategy model proposed in this example can use a rolling optimization method, with the adjustment time being from the current time to the next 24 hours.

[0035] For example, the constraints are as follows: (1) User charging demand constraints Throughout the optimization process, it is necessary to ensure that electric vehicle users can meet their charging needs and reach the expected state of charge while the vehicle is parked, so as to support users' daily travel plans, but not exceeding the vehicle's battery capacity.

[0036] (2) In the formula: The grid connection time of the i-th electric vehicle within the charging station area; Let be the time when the i-th electric vehicle leaves the charging station area. , These are determined based on the user's expected charging time period corresponding to the i-th electric vehicle; The duration of each optimization period (length of a single optimization period), i.e., the interval between adjacent prediction points, such as... It lasts for 15 minutes; The total amount of electricity (in kWh) that the i-th electric vehicle in the charging station area needs to be charged. It is determined based on the target battery capacity expected by the user corresponding to the i-th electric vehicle; Let be the battery capacity of the i-th electric vehicle within the charging station area.

[0037] (2) Charging power constraint To ensure the feasibility of solving the orderly charging control strategy model, it is necessary to ensure that vehicles are charged only during grid-connected periods and that the power does not exceed the upper and lower limits of the charging pile.

[0038] (3) In the formula: The maximum allowed charging power for the i-th electric vehicle.

[0039] (3) Transformer capacity constraints During the implementation of the orderly charging optimization and control strategy, it is necessary to ensure that the total load of the transformer in the distribution area (basic load + charging load) does not exceed the maximum allowable load of the transformer.

[0040] (4) In the formula: This represents the maximum allowable load (kW) of the transformer.

[0041] (4) Charging continuity To avoid some early-production vehicles failing to wake up, and to ensure a better continuous charging experience for users, charging continuity is constrained.

[0042] (5) in, The cumulative number of charging status changes for the i-th electric vehicle within the charging station area at time t. This represents the charging status of the i-th electric vehicle within the charging station area at time t. The value can be 0 or 1. This indicates that the i-th electric vehicle in the charging area is charging at time t. This indicates that the i-th electric vehicle in the charging station area is in a non-charging state at time t; This sets the upper limit on the number of times an electric vehicle's charging status can change within the optimization period. The value is a positive even number; It can be freely set when When the value is 2, the electric vehicle can only change its charging state (on / off state) when starting and ending charging. For example, for electric vehicles that do not have a vehicle-to-charging station wake-up function, =2.

[0043] Equations (1) to (5) above form an optimized control model. The orderly charging control strategy disclosed in this example combines the evaluation results of the transformer load status of the charging area and the user's charging demand. The objective function is to minimize the total load fluctuation of the charging area, and the constraints are user charging demand, charging power, transformer capacity, and charging continuity. A mixed integer linear programming model is constructed. This model is a convex quadratic programming problem, the objective function is a convex function, and the constraints are linear. It can be solved by a commercial solver that supports large-scale problems. A 24-hour charging plan can be formulated for each electric vehicle. The strategy can be refined to T time periods, such as 24 time periods (15-minute level with 96 points of precision). By considering the sensitivity of the objective function to the peak-valley difference of the charging area load, the peak-valley difference of the charging area load corresponding to the final optimization result can be reduced, ensuring the optimization effect. By adding user charging demand constraints, flexible power regulation of electric vehicles can be achieved.

[0044] In some implementations, S110 generates an orderly charging control strategy for the current optimization period in real time based on the orderly charging participation intention information of each user, the operating status of each private charging pile in the charging area during the current optimization period, and the optimization control model, including: The optimized control model is transformed into a quadratic programming model in matrix form. Based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging area in the current optimization cycle, the corresponding solver is called to solve the quadratic programming model to obtain the optimal charging power of each electric vehicle in the current optimization cycle. Based on the optimal charging power of each electric vehicle in the current optimization cycle, an ordered charging control strategy for the current optimization cycle is generated in real time.

[0045] In this example implementation, an ordered charging control strategy for the current optimization cycle is generated in real time by solving the optimization control model. The solution logic of the optimization control model follows a closed-loop process of "data-driven - model building - optimization solution - verification iteration". First, based on the historical operating status of each private charging pile, a load forecasting model (such as various neural network models) is used to make real-time predictions to obtain the residential basic load for future periods and the operating status of each private charging pile in the current optimization cycle, as well as vehicle charging demand parameters (such as... , , The orderly charging participation information of each user reported by the transformer substation can be obtained. The global parameters of the orderly charging control strategy are shown in Table 1. Table 1 is a summary table of global parameters of the orderly charging control strategy, which includes extended parameters such as vehicle, time, power, and capacity used in the control strategy algorithm formula. The objective function and linear constraints (charging demand, power limit, transformer capacity, user demand) are transformed into a quadratic programming model in matrix form. Then, solvers such as CPLEX and Gurobi are called to calculate the optimal charging power of each private charging pile, thereby generating an orderly charging control strategy for each private charging pile in the transformer substation. It can also be verified through posterior verification to ensure that the power demand meets the user demand and that the transformer load is not exceeded. In subsequent model extensions, robust optimization can be introduced to handle prediction bias, rolling time-domain optimization can be used to dynamically update the charging strategy, or time-of-use pricing can be combined to embed economic weights in the objective function to achieve multi-objective coordinated optimization of safety, smoothness, and economy.

[0046] Table 1

[0047] This invention proposes an innovative ordered charging control strategy that constructs a fast solution framework based on convex quadratic programming. It transforms the charging power allocation problem into a convex optimization model with the objective of minimizing the sum of squares of the total load. A rigorously designed convex objective function ensures the uniqueness of the global optimum, and constraints significantly reduce the solution complexity. Furthermore, it integrates commercial solvers to improve the strategy's solution speed, addressing the challenge of balancing real-time response and global optimum in ordered electric vehicle charging. This provides technical support for highly resilient grid regulation in large-scale electric vehicle grid-connected scenarios in residential areas.

[0048] In some implementations, S110's acquisition of the orderly charging participation intention information of each user within the charging station area of ​​the target region includes: Receive ordered charging messages from each user within the charging station area sent by the target application; By parsing the orderly charging messages of each user, information on the orderly charging participation intentions of each user within the charging station area can be obtained. Among them, the orderly charging message of each user is generated based on the orderly charging interface corresponding to the orderly charging function module of the target application where each user performs the target operation.

[0049] In this example implementation, the target application can be any application developed by the power grid for users. A pre-ordered charging function module can be developed on the target application. Users can enter or select their willingness to participate in pre-ordered charging, time period, target SOC value, etc., through the pre-ordered charging interface of this module. In response to the user's target operation, a pre-ordered charging message is generated and sent to the pre-ordered charging control master station. The pre-ordered charging control master station parses the message to obtain the pre-ordered charging participation intention information of each user.

[0050] In one example implementation, the target communication link includes an ordered charging access unit corresponding to each private charging pile within the charging station area; the ordered charging access unit communicates with the corresponding private charging pile via a target communication protocol. The target communication protocol is a private communication protocol built between each private charging pile and its corresponding ordered charging unit.

[0051] In this example implementation, based on the communication link of the multiplexing system, an ordered charging access unit needs to be configured for each private charging pile to connect each private charging pile to the communication link. The control strategy communication process based on the ordered charging access unit is as follows: Figure 2 As shown, Figure 2 This is a communication link diagram for the control strategy of the "cloud network-transformer area-vehicle-charging pile" orderly charging system. The main station platform (i.e., the control master station) communicates remotely with both the user acquisition system and the target application. The concentrator communicates with the user acquisition system via 4G or 5G, using the DL / T698.45 communication protocol for data exchange. The concentrator communicates downlink with the orderly charging access unit and smart meters via dual-mode communication (high-speed carrier + high-speed low-power wireless), also using the DL / T698.45 communication protocol. The orderly charging access unit communicates uplink with the concentrator via dual-mode communication (high-speed carrier + high-speed low-power wireless) and downlink with private charging piles via an RS-485 interface. The communication protocol between each private charging pile and its corresponding orderly charging unit can be configured based on the private communication protocol of each private charging pile; for example, the Modbus serial communication protocol can be used for data exchange with private charging piles. This example breaks down the communication barriers of private charging piles, thereby enabling effective control of them.

[0052] In one example implementation, after invoking the electricity consumption information collection system to send corresponding control commands to each private charging pile in the charging area through the target communication link to control the corresponding electric vehicle to perform orderly charging, based on the orderly charging control strategy of the current optimization cycle, the method further includes: Receive the control result feedback information transmitted back through the target communication link; Based on the error between the feedback information of the regulation result and the corresponding orderly charging control strategy, the orderly charging control strategy is updated.

[0053] In this example implementation, each private charging pile can feed back the actual control results (actual operating status) to the cloud-side control master station through the target communication link. The cloud-side control master station can update / adjust the control strategy according to the error between the actual control results and the orderly charging control strategy, so that the control strategy has better practical guidance significance.

[0054] For example, the "cloud network-transformer area-vehicle-charging station" orderly charging intelligent control process of the present invention is as follows: Figure 3 As shown, after the user selects whether to participate in orderly charging via the interactive APP, the main station platform (control master station) obtains the vehicle-charging pile-distribution area load prediction results in real time, including the operating status of the charging piles and the distribution area load. Combined with the user's dynamic orderly charging needs, it generates an orderly charging control strategy (charging plan) for electric vehicles under the distribution area. This strategy is then distributed from the main station platform to the orderly charging access unit via the distribution area concentrator. The orderly charging access unit sends control commands to the charging piles according to the strategy / charging plan. The charging piles send charging current limits to the on-board charger via PWM, and the on-board charger adjusts the charging power to achieve optimal control of the electric vehicle's charging power and time period. On the other hand, the orderly charging piles feed back their actual operating status to the distribution area concentrator or fusion terminal. The distribution area concentrator or fusion terminal then feeds back the received charging pile operating status and distribution area load information to the main station platform for measurement and adjustment. In this example, the main station platform obtains charging needs through user interaction, acquires charging pile load and distribution area load information through user sampling, and performs charging load prediction based on historical data. The main station platform, through comprehensive processing of the above information and in combination with the load control requirements of the distribution area, formulates a flexible adjustment strategy for the orderly charging of private charging piles in this distribution area. The strategy is distributed to the orderly charging pile access unit through the distribution area concentrator or fusion terminal to complete the flexible adjustment of the power of private charging piles and feed the control event results back to the main station platform.

[0055] Simulation Experiment 1: An orderly charging control strategy is generated for private charging piles in a residential area. The initial simulation parameters are as follows: T = 96, meaning a 15-minute interval is used; N = 50, meaning the residential area has 50 electric vehicles. L_max = 500, which means the upper limit of transformer capacity is 500kVA; L_base = np.random.rand(T) * 200, which simulates the basic load of 96 residential points; t_arrive = np.random.randint(0, T - 8), which simulates the arrival time; t_depart = t_arrive + 8, which means simulating 8 time periods * 15min = (2 hours); P_max_i = 7, meaning the maximum charging power of the charging pile is 7kW; E_i = np.random.uniform(10, 14), which means the random simulation requires 10~14kWh of electricity. Verify transformer capacity: print("All transformer capacity constraints are met") Charging power sampling display: print("\n=Charging power distribution of the first 5 vehicles (kW)="); Based on the above parameter settings, the charging power strategy curves of the first 5 vehicles are obtained using the method of this invention, as shown below. Figure 5 As shown, from Figure 5 It can be seen that the charging periods of the five vehicles are relatively dispersed, which can avoid the power aggregation effect. Integrating the charging strategy over time for each vehicle, the actual total charging amounts for vehicles 1 to 5 are 11.9 kWh, 13.2 kWh, 12.6 kWh, 10.9 kWh, and 12.7 kWh respectively. This shows that the actual total charging amount for each vehicle is not significantly different from its target charging demand of 11.1 kWh, which can meet the needs of each user. Furthermore, the transformer capacity verification results show that the control process fully satisfies the transformer capacity constraint; the objective function of the execution process exhibits good convergence.

[0056] Simulation Experiment 2: For a pilot project of orderly charging in residential areas based on an electricity consumption information collection system in a certain region, the load monitoring of transformer substations was carried out using the orderly charging control strategy of this invention and without using the orderly charging control strategy. The results are as follows: Figure 6 As shown in the figure, the dashed line represents the baseline load of the transformer area before the use of this invention. From Figure 6It can be seen that the peak period for residential electricity consumption is from 7 pm to 9 pm. By applying the orderly charging control strategy of this invention, some of the charging load is guided to the off-peak period from 11 pm to 6 am the next day, transferring 6.88 kW of charging load and reducing the peak load of the transformer area at night by 7.6%, which verifies the feasibility and superiority of the orderly charging control technology based on the power consumption system.

[0057] The orderly charging control strategy for residential areas proposed in this invention is based on a closed-loop framework of "data-driven - model building - optimization solution - verification iteration". It constructs a mixed-integer quadratic programming model with minimizing the sum of squares of the total load in the transformer substation as its core. Through multi-dimensional collaborative optimization, it satisfies the dual requirements of grid security and user needs. The orderly charging strategy is based on the 24-hour forecast of residential basic load and vehicle charging demand data, combined with constraints such as user charging demand, power limitations, transformer capacity, and charging continuity, to generate a 15-minute-level refined charging plan, ensuring the real-time performance of the strategy. Simulation examples demonstrate that the proposed strategy algorithm has good convergence and provides guidance for practical engineering applications.

[0058] Currently, my country's new energy vehicle industry has developed rapidly, and it has been the country with the largest number of new energy vehicles in the world for many consecutive years. At the same time, the integration of intermittent renewable energy sources such as wind and solar power during the construction of the new power system has exacerbated the imbalance between power grid supply and demand. From the perspective of energy security, it is urgent to fully mobilize all elements of the power generation, grid, load, and storage system, especially to fully tap the potential of customer load-side resources to achieve supply and demand coordination. Currently, my country has tens of millions of new energy vehicles and nearly ten million private charging facilities. With the rapid popularization of electric vehicles, residents' charging demand is constantly increasing. During evening peak hours, the charging load in residential areas highly overlaps with daily electricity consumption, leading to severe overload in some residential areas and highlighting the time-specific supply and demand imbalance, making it difficult for new energy vehicle users to apply for charging pile installation. How to achieve the rational allocation of charging resources and fully meet residents' charging needs is a key issue facing the flexible adjustment capabilities of the new power system. Currently, some vehicle-to-grid (V2G) pilot projects have been launched nationwide, but most focus on public charging stations for electric vehicles, primarily using DC charging piles. Research on orderly charging control for private AC charging piles in residential areas is limited, lacking clear technological pathways and sustainable business models, especially regarding the architecture and strategies for orderly charging control systems. When facing large-scale electric vehicle charging demands, there is a lack of adaptability to control technologies for grid-secured scenarios, necessitating further exploration of effective collaborative control system architectures and strategies.

[0059] Given the current lack of systematic charging control technology for electric vehicles in residential areas, a possible approach is to use prepaid electricity meters to control the power supply to private charging stations. This method involves issuing a power-off command to the prepaid electricity meter of a resident's private charging station based on the grid's demand for regional regulation. The relay in the meter disconnects, thus controlling the charging in an orderly manner. However, this electric vehicle prepaid control scheme is rigid and lacks consideration for the dynamic nature of user charging needs and grid demand. It fails to meet the refined needs of users and cannot flexibly adjust the power output of private charging stations based on charging demand and grid load. Potential negative impacts include: 1) Some older prepaid electricity meters require manual on-site power restoration, increasing complexity and labor costs. 2) After power restoration, the private charging station requires re-authentication of the user's identity if the resident wants to restart charging, indicating low intelligence. 3) Currently, approximately 25% of electric vehicles lack vehicle-charging station wake-up functionality, requiring users to re-plug the charging gun to wake the vehicle after power restoration. 4) It cannot flexibly adjust the charging start time and charging power, which is not user-friendly for charging.

[0060] Considering the above, this invention proposes a flexible control system and control strategy suitable for orderly charging in residential areas. Taking the charging station area as the entry point, the communication method of the overall system and equipment is designed, and an orderly charging control system with "cloud network-station area-vehicle-charging pile" as the main architecture is constructed. A two-way communication link is established between the user (vehicle), the orderly charging pile, the smart meter / orderly charging pile access unit, the station terminal, and the main station platform. Furthermore, a multi-scenario, user-unobtrusive intelligent orderly charging control strategy is proposed to achieve optimal regulation of electric vehicle charging power and time. The intelligent orderly charging method can adjust the charging load power and time, optimize power control, realize peak shaving and valley filling of the station area load, ensure green and low-carbon travel for residents, and support the safe and stable operation of the power grid. In other words, it achieves optimal regulation of electric vehicle charging power and time, taking into account both residents' charging needs and the safe and stable operation of the distribution station area. It is a good technical means to serve the safe and economical operation of local power supply and demand balance.

[0061] Currently, new energy vehicles, as mobile energy storage resources, have enormous potential value for vehicle-to-grid interaction in new power systems. However, the application of orderly charging technology for electric vehicles in residential areas is still limited. In the future, as the charging demand in residential areas gradually increases, the supply-demand contradiction will become more prominent during peak evening hours due to the high overlap between charging load and household electricity consumption. Furthermore, new energy vehicle users will face difficulties in applying for and connecting charging piles. This invention will be widely applied to orderly charging of electric vehicles in residential areas, effectively reducing peak evening load rates and improving the ability to apply for and connect charging piles.

[0062] Example 2 Based on the same inventive concept, this invention also discloses a flexible control device for orderly charging of electric vehicles, applied to the cloud side, comprising: The strategy generation module is used to obtain the orderly charging participation intention information of each user in the charging station area of ​​the target area, and generate the orderly charging control strategy for the current optimization period in real time based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging station area in the current optimization period and the optimization control model reported in real time by the charging station area. The orderly charging control module is used to call the electricity information collection system to send corresponding control commands to each private charging pile in the charging area through the target communication link based on the orderly charging control strategy of the current optimization cycle, so as to control the corresponding electric vehicle to charge in an orderly manner. The target communication link is an ordered charging control channel for each electric vehicle in the charging area, constructed based on the electricity information collection system; the optimization control model is a convex optimization model with the objective of minimizing the total load of the charging area; the operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area; the historical operating status of each private charging pile is obtained through the target communication link.

[0063] In this example implementation, the electric vehicle orderly charging flexible control device can be deployed with the cloud-based control master station. For example, the device can be deployed at... Figure 2 or Figure 3 The main platform is used for orderly charging control.

[0064] In one possible implementation, an optimization model building module is also included, which includes: The objective function construction submodule is used to construct an objective function based on the sensitivity of the sum of squares function to the peak-valley difference of the charging station area, with the goal of minimizing the sum of squares of the total load of the charging station area; The constraint construction submodule is used to set corresponding constraints for the objective function based on user charging needs, charging power limits of each private charging pile, transformer capacity of the charging area, and charging continuity of each private charging pile. The model building submodule is used to obtain the optimized control model based on the objective function and the constraints.

[0065] In one possible implementation, the objective function is as follows: ; in, Let T be the objective function and T be the optimization period. The basic load of residents during time period t; Let N be the charging power of the i-th electric vehicle in the charging area at time t, and let N be the number of electric vehicles in the charging area.

[0066] In one possible implementation, the user's intention to participate in orderly charging includes the user's desired charging period and the user's desired target charge level; the constraints include user charging demand constraints and charging continuity constraints. The user charging demand constraints are as follows: ; in, The grid connection time of the i-th electric vehicle within the charging station area; Let be the time when the i-th electric vehicle leaves the charging station area. , These are determined based on the user's expected charging time period corresponding to the i-th electric vehicle; The duration of each optimization period; This represents the total amount of electricity that the i-th electric vehicle in the charging area needs to be charged. It is determined based on the target battery capacity expected by the user corresponding to the i-th electric vehicle; Let be the battery capacity of the i-th electric vehicle within the charging station area; The charging continuity constraint is as follows: ; in, The cumulative number of charging status changes for the i-th electric vehicle within the charging station area at time t. This represents the charging status of the i-th electric vehicle within the charging station area at time t. The value can be 0 or 1. This indicates that the i-th electric vehicle in the charging area is charging at time t. This indicates that the i-th electric vehicle in the charging station area is in a non-charging state at time t; This sets the upper limit on the number of times an electric vehicle's charging status can change within the optimization period. The value is a positive even number.

[0067] In one possible implementation, the strategy generation module includes: The matrix transformation submodule is used to transform the optimization control model into a quadratic programming model in matrix form. The solution submodule is used to solve the quadratic programming model by calling the corresponding solver based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging area in the current optimization period, so as to obtain the optimal charging power of each electric vehicle in the current optimization period. The strategy generation submodule is used to generate an ordered charging control strategy for the current optimization period in real time based on the optimal charging power of each electric vehicle in the current optimization period.

[0068] In one possible implementation, the strategy generation module includes: The receiving submodule is used to receive the ordered charging messages from each user in the charging station area sent by the target application; The parsing submodule is used to parse the orderly charging messages of each user to obtain the orderly charging participation intention information of each user in the charging area. Among them, the orderly charging message of each user is generated based on the orderly charging interface corresponding to the orderly charging function module of the target application where each user performs the target operation.

[0069] In one possible implementation, the target communication link includes an ordered charging access unit corresponding to each private charging pile in the charging station area; the ordered charging access unit communicates with the corresponding private charging pile via a target communication protocol. The target communication protocol is a private communication protocol built between each private charging pile and its corresponding ordered charging unit.

[0070] In one possible implementation, a feedback update module is also included, which is used to: Receive the control result feedback information transmitted back through the target communication link; Based on the error between the feedback information of the regulation result and the corresponding orderly charging control strategy, the orderly charging control strategy is updated.

[0071] The orderly charging control system designed in this invention takes the charging station area as the starting point and innovatively establishes a replicable and easily promoted orderly charging technology solution for electric vehicles in residential areas. It fully reuses the existing communication links in the station area to realize two-way communication between users (vehicles), orderly charging piles, station-side intelligent terminals, and the main station platform. It can effectively guide electric vehicle users in residential areas to actively participate in orderly charging and minimize the construction or renovation costs of orderly charging in residential areas.

[0072] Example 3 Based on the same inventive concept, this invention also discloses a flexible control system for orderly charging of electric vehicles, including the flexible control device for orderly charging of electric vehicles as described in Embodiment 2. For example, the flexible control system for orderly charging of electric vehicles can be... Figure 2 and Figure 3 The main platform in China.

[0073] In one example implementation, it further includes: a charging station control terminal and an orderly charging access unit located in the charging station area, as well as each private charging pile in the charging station area; the orderly charging access unit is electrically connected to the charging station control terminal and the corresponding private charging pile respectively. The control terminal of the transformer area is used to receive the orderly charging control command generated by the orderly charging control strategy based on the current optimization cycle issued by the electric vehicle orderly charging flexible control device on the cloud side, and forward the orderly charging control command to the corresponding orderly charging access unit. The orderly charging access unit is used to parse the received orderly charging control command and send the control command to the corresponding private charging pile based on the parsing result. The private charging station is used to respond to the control command and send a power adjustment command to the electric vehicle bound to the private charging station.

[0074] In this example implementation, the orderly charging control system is a "cloud-network-station-vehicle-charging pile" collaborative system, and the specific system architecture is as follows: Figure 4 As shown, the system includes: a "cloud network" layer comprising the main station platform and user interaction APP; a "distribution area" layer comprising the distribution area control terminal, smart meters, and orderly charging access units within the distribution area's substation; and a "vehicle-charging pile" layer comprising private charging piles and electric vehicles in the target area (e.g., residential areas). The main station platform is responsible for monitoring the distribution area load and charging pile operation information; it also formulates and issues charging pile control commands to the user acquisition system. The user acquisition system collects metering information from the user's charging pile meter via dual-mode communication (high-speed power line carrier HPLC + high-speed low-power wireless HRF); simultaneously, it transmits control commands to the charging piles through the distribution area concentrator and orderly charging access units. The distribution area control terminal (e.g., the distribution area concentrator) forwards orderly charging messages via dual-mode communication. The orderly charging access unit is responsible for parsing the orderly charging commands issued by the concentrator; communicating with the charging piles via RS485 / CAN; and forwarding start, stop, and power adjustment control commands to the charging piles according to the orderly charging commands. The user interaction app is responsible for collecting information on whether users participate in the orderly charging system, including participation status, participation time, and other vehicle owner usage needs. It also pushes charging process monitoring information and charging bills to users. The orderly charging pile is capable of communicating with the orderly charging access unit via RS485 / CAN and other communication methods; it is responsible for receiving and responding to commands such as starting charging, stopping charging, and adjusting charging power; it is also responsible for sending PWM power adjustment commands to the vehicles. The power supply lines of the user acquisition system distribute AC power from the power grid to multiple low-voltage branch boxes (e.g., from low-voltage branch box #1 to low-voltage branch box #N) through the substation, control terminal, and outgoing line cabinet, and then supply the charging piles via the lines within the smart meters.

[0075] For example, the orderly charging access unit is installed on the side of the corresponding private charging pile or integrated into the smart meter corresponding to the private charging pile. Figure 4 The meter boxes #1 to #N on the left are for installing the orderly charging access unit next to the corresponding private charging pile. Figure 4 The meter boxes #1 to #N on the right side integrate the orderly charging access unit into the smart meter. In other words, the smart meter + orderly charging access unit on the left side is replaced with an IoT meter containing the orderly charging access unit, achieving the same control function as the deployment method on the left side. However, it is necessary to replace the existing smart meters of users with IoT meters.

[0076] This invention discloses a "cloud-network-substation-vehicle-charging pile" orderly charging control system. Based on the platform functions and communication channels of an orderly charging management system, a user acquisition system, a substation concentrator, and an orderly charging pile access unit, this system establishes a replicable and easily scalable orderly charging system solution for electric vehicles in residential areas with minimal cost investment. It effectively supports seamless and orderly charging for electric vehicle users in residential areas through technological means. This invention's "cloud-network-substation-vehicle-charging pile" orderly charging control system targets charging substations, independently develops an orderly charging system, and establishes a two-way communication link between the user (vehicle), the orderly charging pile, the smart meter / orderly charging pile access unit, the substation terminal, and the main station platform.

[0077] A smart and orderly charging control process applicable to multiple scenarios.

[0078] Example 4 like Figure 7 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0079] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the electric vehicle orderly charging flexible control method in the above embodiments.

[0080] Example 5 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). An electronic device readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both the built-in storage medium within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the electric vehicle orderly charging flexible control method described in the above embodiments.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A flexible control method for orderly charging of electric vehicles, applied to the cloud side, characterized in that, include: Obtain information on the orderly charging participation intentions of each user in the charging station area of ​​the target area. Based on the orderly charging participation intentions of each user and the real-time reporting of the operating status of each private charging pile in the charging station area in the current optimization cycle and the optimization control model, generate an orderly charging control strategy for the current optimization cycle in real time. Based on the orderly charging control strategy of the current optimization cycle, the electricity information collection system is invoked to send corresponding control commands to each private charging pile in the charging area through the target communication link, so as to control the corresponding electric vehicles to charge in an orderly manner. The target communication link is an ordered charging control channel for each electric vehicle in the charging area, constructed based on the electricity information collection system; the optimization control model is a convex optimization model with the objective of minimizing the total load of the charging area; the operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area; the historical operating status of each private charging pile is obtained through the target communication link.

2. The method according to claim 1, characterized in that, The optimized control model includes the following construction steps: Based on the sensitivity of the sum of squares function to the peak-valley difference of the charging station area, an objective function is constructed with the goal of minimizing the sum of squares of the total load of the charging station area. Based on user charging needs, the charging power limits of each private charging station, the transformer capacity of the charging area, and the charging continuity of each private charging station, corresponding constraints are set for the objective function. Based on the objective function and the constraints, the optimized control model is obtained.

3. The method according to claim 2, characterized in that, The objective function is as follows: ； in, Let T be the objective function and T be the optimization period. The basic load of residents during time period t; Let N be the charging power of the i-th electric vehicle in the charging area at time t, and let N be the number of electric vehicles in the charging area.

4. The method according to claim 3, characterized in that, The user's willingness to participate in orderly charging includes the user's desired charging time period and the user's desired target power level; the constraints include user charging demand constraints and charging continuity constraints. The user charging demand constraints are as follows: ； in, The grid connection time of the i-th electric vehicle within the charging station area; Let be the time when the i-th electric vehicle leaves the charging station area. , These are determined based on the user's expected charging time period corresponding to the i-th electric vehicle; The duration of each optimization period; This represents the total amount of electricity that the i-th electric vehicle in the charging area needs to be charged. It is determined based on the target battery capacity expected by the user corresponding to the i-th electric vehicle; Let be the battery capacity of the i-th electric vehicle within the charging station area; The charging continuity constraint is as follows: ； in, The cumulative number of charging status changes for the i-th electric vehicle within the charging station area at time t. This represents the charging status of the i-th electric vehicle within the charging station area at time t. The charging status of the i-th electric vehicle in the charging station area at time t-1; The value can be 0 or 1. This indicates that the i-th electric vehicle in the charging area is charging at time t. This indicates that the i-th electric vehicle in the charging station area is in a non-charging state at time t; This sets the upper limit on the number of times an electric vehicle's charging status can change within the optimization period. The value is a positive even number.

5. The method according to claim 4, characterized in that, Based on the information on each user's willingness to participate in orderly charging and the operating status of each private charging pile in the charging area during the current optimization cycle, as well as the optimization control model, an orderly charging control strategy for the current optimization cycle is generated in real time, including: The optimized control model is transformed into a quadratic programming model in matrix form. Based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging area in the current optimization cycle, the corresponding solver is called to solve the quadratic programming model to obtain the optimal charging power of each electric vehicle in the current optimization cycle. Based on the optimal charging power of each electric vehicle in the current optimization cycle, an ordered charging control strategy for the current optimization cycle is generated in real time.

6. The method according to claim 1, characterized in that, The acquisition of information on the orderly charging participation intentions of each user within the charging station area of ​​the target region includes: Receive ordered charging messages from each user within the charging station area sent by the target application; By parsing the orderly charging messages of each user, information on the orderly charging participation intentions of each user within the charging station area can be obtained. Among them, the orderly charging message of each user is generated based on the orderly charging interface corresponding to the orderly charging function module of the target application where each user performs the target operation.

7. The method according to any one of claims 1-6, characterized in that, The target communication link includes an ordered charging access unit corresponding to each private charging pile in the charging station area; the ordered charging access unit communicates with the corresponding private charging pile through a target communication protocol. The target communication protocol is a private communication protocol built between each private charging pile and its corresponding ordered charging unit.

8. The method according to claim 1, characterized in that, After implementing the orderly charging control strategy based on the current optimization cycle, which calls the electricity consumption information collection system to send corresponding control commands to each private charging pile in the charging area via the target communication link to control the corresponding electric vehicles to charge in an orderly manner, the strategy also includes: Receive the control result feedback information transmitted back through the target communication link; Based on the error between the feedback information of the regulation result and the corresponding orderly charging control strategy, the orderly charging control strategy is updated.

9. A flexible control device for orderly charging of electric vehicles, applied to the cloud side, characterized in that, include: The strategy generation module is used to obtain the orderly charging participation intention information of each user in the charging station area of ​​the target area, and generate the orderly charging control strategy for the current optimization period in real time based on the orderly charging participation intention information of each user and the operating status of each private charging pile in the charging station area in the current optimization period and the optimization control model reported in real time by the charging station area. The orderly charging control module is used to call the electricity information collection system to send corresponding control commands to each private charging pile in the charging area through the target communication link based on the orderly charging control strategy of the current optimization cycle, so as to control the corresponding electric vehicle to charge in an orderly manner. The target communication link is an ordered charging control channel for each electric vehicle in the charging area, constructed based on the electricity information collection system; the optimization control model is a convex optimization model with the objective of minimizing the total load of the charging area; the operating status of each private charging pile in the current optimization cycle is obtained by real-time prediction based on the historical operating status of each private charging pile in the charging area; the historical operating status of each private charging pile is obtained through the target communication link.

10. A flexible control system for orderly charging of electric vehicles, characterized in that, Includes the electric vehicle orderly charging flexible control device as described in claim 9.

11. The system according to claim 10, characterized in that, Also includes: The charging station area includes a control terminal and an orderly charging access unit located in the charging station area, as well as each private charging pile within the charging station area; the orderly charging access unit is electrically connected to the control terminal and the corresponding private charging pile respectively. The control terminal of the transformer area is used to receive the orderly charging control command generated by the orderly charging control strategy based on the current optimization cycle issued by the electric vehicle orderly charging flexible control device on the cloud side, and forward the orderly charging control command to the corresponding orderly charging access unit. The orderly charging access unit is used to parse the received orderly charging control command and send the control command to the corresponding private charging pile based on the parsing result. The private charging station is used to respond to the control command and send a power adjustment command to the electric vehicle bound to the private charging station.

12. The system according to claim 11, characterized in that, The orderly charging access unit is installed on the side of the corresponding private charging pile or integrated into the smart meter corresponding to the private charging pile.

13. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method as described in any one of claims 1 to 8 is implemented.

14. A readable storage medium, characterized in that, It contains an executable program, which, when executed, implements the method as described in any one of claims 1 to 8.

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