Method for scheduling power resources between a vehicle and a power grid and related device

Abstract

The application discloses a kind of scheduling methods of electric energy resource between vehicle and power grid and related equipment, the method includes: obtaining the vehicle state parameters of each vehicle participating in scheduling, and the target regulation parameters of power grid, and according to the vehicle state parameters of each vehicle, respectively determine the battery loss cost parameters and the scheduling willingness parameters of corresponding vehicle;Based on the target regulation parameters and the battery loss cost parameters, scheduling willingness parameters of each vehicle, with the minimum power fluctuation of power grid tie line and the maximum total net income of scheduling as optimization target, construct multi-objective optimization scheduling model in combination with preset constraint condition;Solve the multi-objective optimization scheduling model, obtain the bidirectional power regulation instruction of each vehicle in each scheduling period;The bidirectional power regulation instruction is output to control each vehicle to execute corresponding charging or discharging operation.The technical scheme provided by the application can enhance the rationality of electric energy resource scheduling between vehicle and power grid.

Description

Technical Field

[0001] This application belongs to the field of vehicle-grid interaction technology, and particularly relates to a method and related equipment for scheduling electrical resources between vehicles and the power grid. Background Technology

[0002] With the rapid development of the electric vehicle industry and the continuous advancement of vehicle-grid interaction technology, the large-scale participation of electric vehicles in grid power dispatch has become an important technological direction for improving grid operational resilience and promoting the consumption of new energy sources. By guiding and regulating the charging and discharging behavior of electric vehicles, peak shaving and valley filling of grid load can be achieved to a certain extent, thereby improving the operating efficiency of the distribution network.

[0003] However, existing methods for scheduling power resources between vehicles and the power grid mostly only achieve one-way optimization of charging periods or simple two-way interaction between individual vehicles and the power grid. They lack the ability to coordinate and optimize the scheduling of regional vehicle clusters and do not fully consider the impact of battery wear costs and user participation on the scheduling effectiveness. In practical applications, problems such as large deviations between the actual response capabilities of vehicles and scheduling targets, significant fluctuations in user participation, and insufficient rationality of the overall cluster regulation power are prone to occur. This makes it difficult to ensure the stable implementation of grid-side regulation demands, thus restricting the reliability and effectiveness of power resource scheduling.

[0004] Therefore, how to enhance the rationality of power resource scheduling between vehicles and the power grid has become an urgent technical problem to be solved. Summary of the Invention

[0005] The embodiments of this application provide a method, apparatus, computer program product, computer-readable storage medium, and electronic device for scheduling electrical resources between vehicles and the power grid, which can at least to some extent enhance the rationality of scheduling electrical resources between vehicles and the power grid.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] According to a first aspect of the embodiments of this application, a method for scheduling electrical resources between vehicles and the power grid is provided. The method includes: acquiring vehicle state parameters of each vehicle participating in the scheduling and target adjustment parameters of the power grid; determining battery loss cost parameters and scheduling intention parameters of the corresponding vehicles based on the vehicle state parameters of each vehicle; constructing a multi-objective optimization scheduling model based on the target adjustment parameters, battery loss cost parameters, and scheduling intention parameters of each vehicle, with the optimization objectives of minimizing power fluctuations in the power grid interconnection and maximizing the total net scheduling revenue, combined with preset constraints; solving the multi-objective optimization scheduling model to obtain bidirectional power adjustment commands for each vehicle in each scheduling period; and outputting the bidirectional power adjustment commands to control each vehicle to perform corresponding charging or discharging operations.

[0008] In some embodiments of this application, based on the aforementioned scheme, the vehicle status parameters include battery health status, real-time state of charge, preset departure time, and historical scheduling records. The step of determining the battery loss cost parameters and scheduling willingness parameters of each vehicle based on its vehicle status parameters includes: obtaining the battery degradation coefficient and single-schedule discharge amount of the corresponding vehicle; calculating the battery loss cost parameters of the corresponding vehicle according to the correspondence that loss cost is directly proportional to the square of the single-schedule discharge amount and inversely proportional to the battery health status; and quantifying the scheduling willingness parameters of the corresponding vehicle by combining the real-time state of charge, the preset departure time, the historical scheduling records, and the dynamic incentive electricity price.

[0009] In some embodiments of this application, based on the foregoing scheme, the target adjustment parameters include the tie-line power target value, peak shaving adjustment demand, and valley filling adjustment demand; the optimization objective of minimizing grid tie-line power fluctuation includes minimizing the sum of squared deviations between the actual tie-line power and the tie-line power target value in each scheduling period; the optimization objective of maximizing the total net scheduling revenue includes maximizing the difference between the sum of the electrical energy revenue and ancillary service revenue in each scheduling period and the sum of the incentive fees paid to each vehicle and the costs corresponding to the battery loss cost parameters.

[0010] In some embodiments of this application, based on the aforementioned scheme, the vehicle state parameters include a preset demand state of charge, and the preset constraints include: the state of charge of a single vehicle is within a preset safety range; at the preset departure time of the corresponding vehicle, its state of charge is not lower than the demand state of charge; the charging power of a single vehicle is within a preset charging power range; and the discharging power of a single vehicle is within a preset discharging power range.

[0011] In some embodiments of this application, based on the foregoing scheme, the step of solving the multi-objective optimization scheduling model to obtain the bidirectional power adjustment command for each vehicle in each scheduling period includes: using the alternating direction multiplier method to perform a distributed solution of the multi-objective optimization scheduling model to obtain the bidirectional power adjustment command for each vehicle.

[0012] In some embodiments of this application, based on the foregoing scheme, the method further includes: determining a basic incentive price corresponding to the real-time supply and demand status of the power grid; determining a state-of-charge compensation price based on the difference between the average state of charge of all currently participating vehicles and the real-time state of charge of the corresponding vehicle; determining a participation reward price based on the historical participation score of the corresponding vehicle, wherein the historical participation score is generated based on the historical dispatch records of the vehicle; and using the sum of the basic incentive price, the state-of-charge compensation price, and the participation reward price as the dynamic incentive price of the corresponding vehicle.

[0013] In some embodiments of this application, based on the foregoing scheme, the method further includes: after the scheduling cycle ends, statistically analyzing the actual charging and discharging data and scheduling contribution of each vehicle; calculating the incentive fee based on the actual charging and discharging data and the corresponding dynamic incentive electricity price, and updating the historical scheduling record of the corresponding vehicle.

[0014] In some embodiments of this application, based on the foregoing scheme, the method further includes: receiving dispatch service registration information and travel plan information submitted by the user; periodically collecting real-time vehicle status parameters of each vehicle and updating the vehicle status parameters.

[0015] In some embodiments of this application, based on the foregoing scheme, the method further includes: during the scheduling execution process, real-time monitoring of the state changes of each vehicle and the changes in the grid target adjustment requirements; when the state change exceeds a preset threshold, reconstructing and solving the multi-objective optimization scheduling model, and updating the bidirectional power adjustment commands of each vehicle.

[0016] According to a second aspect of the embodiments of this application, a scheduling device for electrical resources between vehicles and the power grid is provided. The device includes: an acquisition unit, configured to acquire vehicle status parameters of each vehicle participating in the scheduling, and target adjustment parameters of the power grid, and determine battery loss cost parameters and scheduling intention parameters of the corresponding vehicles based on the vehicle status parameters of each vehicle; a construction unit, configured to construct a multi-objective optimization scheduling model based on the target adjustment parameters, the battery loss cost parameters and scheduling intention parameters of each vehicle, with the optimization objectives of minimizing power fluctuations of the power grid interconnection and maximizing the total net scheduling revenue, combined with preset constraints; a solution unit, configured to solve the multi-objective optimization scheduling model to obtain bidirectional power adjustment commands for each vehicle in each scheduling period; and an output unit, configured to output the bidirectional power adjustment commands to control each vehicle to perform corresponding charging or discharging operations.

[0017] According to a third aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform an operation as described in any of the first aspects above.

[0018] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by a processor to perform the operation as described in any of the first aspects above.

[0019] According to a fifth aspect of the present application, an electronic device is provided, the electronic device including one or more processors and one or more memories, the one or more memories storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by the one or more processors to perform the operation as described in any of the first aspects above.

[0020] Based on the technical solution proposed in this application, by acquiring the vehicle status parameters of each vehicle participating in the scheduling and the target adjustment parameters of the power grid, and accordingly determining the battery loss cost parameters and scheduling willingness parameters of the corresponding vehicles, the adjustable potential and participation cost of each vehicle can be fully grasped during the scheduling planning stage. This avoids formulating scheduling schemes without considering the actual state of the vehicles, providing a reliable data foundation for the rational allocation of adjustment tasks. Furthermore, this application uses minimizing power fluctuations in the power grid interconnection and maximizing the total net scheduling revenue as dual optimization objectives. Combined with preset constraints, a multi-objective optimization scheduling model is constructed, which can simultaneously consider the stable operation requirements of the power grid and the economic feasibility of the scheduling process, avoiding scheduling imbalances caused by focusing on a single objective. This allows the scheduling scheme to achieve global optimality under multiple constraints. By solving the multi-objective optimization scheduling model, bidirectional power adjustment commands for each vehicle in each scheduling period are obtained, and the commands are output to control the vehicles to perform corresponding charging or discharging operations. This enables refined bidirectional power coordination control of large-scale vehicle clusters, allowing the charging and discharging behavior of each vehicle to accurately match the power grid demand, effectively enhancing the rationality of power resource scheduling between vehicles and the power grid. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A flowchart illustrating the method for scheduling electrical resources between vehicles and the power grid according to an embodiment of this application is shown; Figure 2 A block diagram of a power dispatching device between a vehicle and a power grid according to an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of an electronic device in an embodiment of this application is shown. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. It should also be noted that, for the sake of simplicity, certain components in the drawings that do not affect the interpretation of the technical solution of this application have been appropriately omitted.

[0025] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined. Therefore, the actual execution order may change depending on the actual situation.

[0026] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0027] This application proposes a scheduling scheme for electrical resources between vehicles and the power grid, which aims to enhance the rationality of electrical resource scheduling between vehicles and the power grid, thereby ensuring the stable implementation of power grid-side regulation needs.

[0028] Next, this application will elaborate on the proposed vehicle-to-grid (V2G) power resource scheduling scheme. This application is applied to the field of vehicle-to-grid (V2G) technology and is suitable for scenarios with large-scale electric vehicle parking, such as residential communities, public charging stations, and industrial parks. It can achieve bidirectional power coordination and regulation of electric vehicle clusters, improving the stability of grid operation and the scheduling process. (Refer to...) Figure 1The diagram illustrates a flowchart of a method for scheduling electrical resources between vehicles and the power grid according to an embodiment of this application. This method can be executed by a device with computing capabilities, such as a regional aggregation platform server deployed in the cloud, or by an edge control gateway deployed at a charging station; this application does not limit its execution in this regard. Figure 1 As shown, the method includes at least steps 110 to 140, which are described in detail below: In step 110, the vehicle status parameters of each vehicle participating in the scheduling and the target adjustment parameters of the power grid are obtained, and the battery loss cost parameters and scheduling intention parameters of the corresponding vehicles are determined according to the vehicle status parameters of each vehicle.

[0029] In practical applications, the vehicle status parameters are the basic data for evaluating vehicle adjustability, and the target adjustment parameters are the grid-side objectives that need to be achieved during the scheduling process. These two types of parameters together constitute the input basis for scheduling optimization. The grid's target adjustment parameters can be uniformly issued by the superior grid dispatch center, or they can be generated based on the historical operating patterns and real-time load status of the regional distribution network, used to guide the direction and scale of power resource adjustment within the region. The process of obtaining the two types of parameters can be executed simultaneously or sequentially according to a preset order; this application does not limit this.

[0030] In this application, vehicle status parameters of each vehicle within the jurisdiction can be collected by an edge control gateway deployed at the charging station.

[0031] In this application, the edge control gateway can be deployed in locations such as community power distribution rooms and charging station control rooms. It possesses local data acquisition, command forwarding, and preliminary calculation capabilities, and can establish connections with the onboard intelligent terminals of each vehicle via wired or wireless communication. The onboard intelligent terminals can interact with the vehicle's Battery Management System (BMS) and charger control system to obtain real-time battery operation data and charging status. The data acquisition cycle of the edge control gateway can be set according to scheduling accuracy requirements, such as 1 minute, 5 minutes, or 15 minutes, ensuring data real-time performance while controlling communication and computational overhead.

[0032] In this application, the vehicle status parameters include battery health status, real-time state of charge (SOH), preset departure time, and historical scheduling records. The battery health status (SOH) characterizes the current degree of battery capacity degradation, typically expressed as a percentage. A brand new battery has a 100% SOH, and as the number of usage cycles increases, the available battery capacity decreases, and the SOH value decreases accordingly. The real-time state of charge (SOC) characterizes the current remaining battery charge, also expressed as a percentage, and is a core indicator for judging the vehicle's discharge capability. The preset departure time is set by the user in advance, representing the time when the user will next use the vehicle, and is an inviolable time constraint during the scheduling process. The historical scheduling records store data such as the number of times the vehicle has participated in V2G scheduling, its fulfillment status, and cumulative charge / discharge volume, used to assess the user's participation habits and reliability.

[0033] In such Figure 1 In step 110, the step of determining the battery loss cost parameter and scheduling intention parameter of the corresponding vehicle based on the vehicle status parameters of each vehicle can be performed according to the following steps 111 to 113: Step 111: Obtain the battery degradation coefficient and single-discharge amount for the corresponding vehicle.

[0034] In this application, the battery degradation coefficient is a fixed parameter related to the battery chemistry system. Different types of power batteries have different degradation characteristics; for example, lithium iron phosphate batteries and ternary lithium batteries have different degradation rates, and their corresponding degradation coefficients are also different. This coefficient can be obtained through test data provided by battery manufacturers, or it can be fitted and calibrated based on historical vehicle operating data. The single dispatch discharge volume refers to the total electrical energy delivered by the vehicle to the grid within a single dispatch cycle, measured in kilowatt-hours. This value can be estimated based on dispatch requirements and vehicle status, or it can be calculated using historical dispatch data.

[0035] Step 112: Based on the correspondence that the loss cost is directly proportional to the square of the single dispatch discharge amount and inversely proportional to the battery health status, calculate the battery loss cost parameters for the corresponding vehicle.

[0036] Specifically, the battery loss cost parameter can be calculated using the following formula:

[0037] in, For the first The battery wear cost incurred by the vehicles participating in this dispatch is expressed in yuan. For the first Battery degradation coefficient for each vehicle; This refers to the discharge amount during a single scheduling process; For the first The battery health status of the vehicle.

[0038] In this application, the calculation method reflects the non-linear characteristics of battery degradation, meaning that the greater the discharge capacity, the faster the battery loss increases, and deep discharge has a far greater impact on battery life than shallow discharge. For example, when the vehicle battery health is 90% and the battery degradation coefficient is 0.02 yuan per square kWh, a single discharge of 10 kWh corresponds to a battery loss cost of approximately 2.22 yuan; if the single discharge capacity increases to 20 kWh, the loss cost will increase to approximately 8.89 yuan, far exceeding the linear growth rate. This quantification method can accurately reflect the battery life loss at different discharge depths, avoiding excessive battery damage to users due to deep discharge and alleviating users' core concerns about participating in V2G dispatch.

[0039] Step 113: Combine the real-time charge status of the corresponding vehicle, the preset departure time, the historical scheduling records, and the dynamic incentive electricity price to quantify the scheduling intention parameters of the corresponding vehicle.

[0040] Specifically, the scheduling intention parameter can be calculated using the following formula:

[0041] in, For the first Taiwanese vehicles The scheduling intention parameter at any given time; For the first Taiwanese vehicles The state of charge at any given moment, expressed as a percentage (%), is the most critical factor. Users with sufficient charge (e.g., 80%) are more likely to participate in discharging. The system must ensure that the battery is charged to the user's required level before the user sets the next departure time. For the first Taiwanese vehicles Incentive electricity prices at any time For the first User engagement profiles for vehicles in Taiwan.

[0042] In this application, the scheduling willingness parameter is used to characterize the user's willingness to participate in V2G scheduling in the current state. Its value can be set between 0 and 1, with higher values ​​indicating stronger willingness to participate. Specifically, the higher the vehicle's real-time state of charge, the stronger the user's sense of battery security, and the higher their willingness to participate in discharging; the further away from the preset departure time, the more time the vehicle has to replenish its battery, and the higher their willingness to participate in scheduling; the higher the dynamic incentive price, the greater the economic benefit the user receives, and the higher their willingness to participate; the richer the historical scheduling records and the better the fulfillment of obligations, the higher the user's acceptance of the scheduling mode, and the relatively higher their willingness to participate. The scheduling willingness parameter can be calculated through a preset functional relationship or predicted based on historical data using a machine learning model; this application does not limit this.

[0043] By quantifying battery wear costs and scheduling willingness through the above steps, adjustable vehicles can be accurately screened before scheduling optimization. Vehicles with high willingness and low wear can be prioritized for scheduling, avoiding user resistance caused by forced scheduling and battery damage caused by over-discharge. This process can effectively improve the actual execution rate of scheduling instructions, reduce the number of users who withdraw from scheduling midway, and thus provide stable and reliable adjustable resources for subsequent cluster collaborative scheduling, enhancing the stability of the overall scheduling process.

[0044] Continue to refer to Figure 1 In step 120, based on the target adjustment parameters and the battery loss cost parameters and scheduling intention parameters of each vehicle, a multi-objective optimization scheduling model is constructed with the optimization objectives of minimizing power fluctuations of the grid interconnection line and maximizing the total net scheduling revenue, combined with preset constraints.

[0045] The multi-objective optimization scheduling model is the core decision-making unit of the entire scheduling method. It simultaneously considers the operational needs of the power grid and the economic needs of the operators, and safeguards the core interests of the users through constraints, ultimately achieving a balance of interests among the three parties. During the model construction process, battery loss cost parameters and scheduling willingness parameters are used as weighting factors in the optimization calculation, guiding the model to prioritize vehicles with low loss and strong willingness to undertake the adjustment tasks, thereby improving the feasibility of the solution.

[0046] In this application, the target regulation parameters include tie-line power target values, peak shaving regulation requirements, and valley filling regulation requirements. The tie-line power target value is the ideal incoming power value of the regional power grid set by the power grid dispatching layer. The peak shaving regulation requirements are the requirements for reducing regional load during peak electricity consumption periods, and the valley filling regulation requirements are the requirements for absorbing surplus power during off-peak electricity consumption periods. These two types of regulation requirements can be converted into tie-line power target values ​​for corresponding time periods and integrated into a unified smoothing target.

[0047] In this application, the optimization objective for minimizing power fluctuations in the power grid tie lines includes minimizing the sum of squared deviations between the actual power of the tie lines and the target power value of the tie lines during each scheduling period. The specific expression for the optimization objective of minimizing power fluctuations in the power grid tie lines can be:

[0048] in, The objective function value for power grid smoothing; This refers to the total number of time slots within the scheduling cycle. For example, if each scheduling time slot is 15 minutes, a 24-hour scheduling cycle would correspond to 96 time slots. For the first The actual power of the regional power grid interconnection line during a given time period, in kilowatts; This represents the target power value for the tie line, in kilowatts. This target aims to achieve stable operation of the regional power grid by minimizing the sum of squares of the deviations between the actual power and the target value, thereby reducing the impact of load fluctuations on distribution network equipment and mitigating the risk of transformer overload.

[0049] In this application, the optimization objective of maximizing the total net revenue from scheduling includes maximizing the difference between the sum of electrical energy revenue and ancillary service revenue in each scheduling period and the sum of the incentive fees paid to each vehicle and the costs corresponding to the battery loss cost parameters. The specific expression for the optimization objective of maximizing the total net revenue from scheduling can be:

[0050] in, The net income objective function value; For the first The electricity revenue obtained through the peak-valley electricity price difference during each time period is expressed in yuan. For the first Revenue earned during each time period by providing ancillary services such as frequency modulation and backup is expressed in yuan. The total number of vehicles participating in the dispatch; For the first Payment to the first period The incentive cost for each vehicle is expressed in yuan. For the first The first time period The battery depreciation cost per vehicle is expressed in yuan. This objective ensures that the total revenue of the dispatching operator can cover the total expenditure, achieving the economical and sustainable operation of the dispatching model, while also ensuring that the incentives received by users can cover their own battery depreciation costs.

[0051] In this application, the vehicle state parameters include a preset demand state of charge, and the preset constraints include the following: First, the state of charge of a single vehicle is within a preset safe range, namely: To prevent overcharging or over-discharging of the battery, the BMS system sets safety boundaries.

[0052] Secondly, at the preset departure time of the corresponding vehicle, its state of charge is not lower than the required state of charge, that is: This ensures that the battery level is not lower than the minimum required when the user sets off. .

[0053] Third, the charging power of a single vehicle is within the preset charging power range, that is: .

[0054] Fourth, the discharge power of a single vehicle is within the preset discharge power range, that is: .

[0055] In this application, the preset required state of charge (SOC) can be set by the user, representing the minimum remaining battery power required for the user's departure. The preset constraints are hard rules that scheduling optimization must meet. The safe SOC range can be set from 20% to 95% to avoid overcharging or over-discharging the battery and protect its lifespan. The travel demand constraints ensure that the user's normal vehicle use is not affected and are a core prerequisite for user participation in scheduling. The charging and discharging power constraints match the hardware capabilities of the onboard charger and charging station, ensuring that the instructions are executable and do not exceed the equipment's rated operating range.

[0056] In this application, by constructing a dual-objective optimization model and setting multiple rigid constraints, the grid regulation requirements, operational economic requirements, and user rights requirements can all be incorporated into a unified optimization framework. This ensures that the output dispatching scheme simultaneously meets the requirements of multiple parties, avoiding a situation where a single objective is prioritized at the expense of the interests of other stakeholders. This multi-objective collaborative optimization approach can maximize the smoothing of grid power while ensuring user travel needs and battery safety, effectively improving the feasibility and implementation of the dispatching scheme, and thus enhancing the overall stability of power resource dispatching between vehicles and the grid.

[0057] Continue to refer to Figure 1 In step 130, the multi-objective optimization scheduling model is solved to obtain the bidirectional power adjustment command for each vehicle in each scheduling period.

[0058] In this application, the bidirectional power adjustment command includes a power setting value for each vehicle in each scheduling period. Positive values ​​represent the charging power of the vehicle drawing power from the grid, while negative values ​​represent the discharging power of the vehicle sending power to the grid. The command precision can match the granularity of the scheduling period, achieving fine-grained power adjustment. The solution process can select an appropriate algorithm based on the vehicle scale and computing power conditions. In small-scale scenarios, a centralized solution can be used, while in large-scale vehicle cluster scenarios, a distributed solution method is more suitable.

[0059] In such Figure 1 In step 130, solving the multi-objective optimization scheduling model to obtain the bidirectional power adjustment commands for each vehicle in each scheduling period can be performed according to step 131 as follows: Step 131: The multi-objective optimization scheduling model is solved in a distributed manner using the alternating direction multiplier method to obtain the bidirectional power adjustment command for each vehicle.

[0060] In this application, the Alternating Direction Method of Multipliers (ADMM) is an algorithm suitable for large-scale distributed optimization. Its core idea is to decompose the global optimization problem into multiple independent sub-problems, which are solved separately by each local node, and then the global optimum is achieved through iterative coordination. In the scenario of this application, the vehicle cluster under the jurisdiction of each edge control gateway can be considered as a sub-problem unit. Each gateway completes the optimization calculation for its assigned vehicles locally, and then uploads the calculation results to the regional aggregation platform for coordinated iteration, ultimately converging to obtain the globally optimal power allocation scheme.

[0061] The distributed solution approach employed in this application offers several advantages. Firstly, detailed user travel plans and battery privacy data do not need to be uploaded to the central platform; only a small number of coordinating variables need to be interacted with, effectively protecting user data privacy. Secondly, when the number of vehicles involved in scheduling is large, such as thousands, the distributed algorithm can significantly improve the solution speed through parallel computing, controlling computational latency to the minute level, meeting the response requirements of real-time scheduling, and avoiding the computational bottlenecks and latency issues associated with centralized computing.

[0062] This application employs a distributed optimization algorithm to solve the scheduling model, which can efficiently complete power allocation calculations for large-scale vehicle clusters while protecting user privacy, ensuring the real-time performance and global optimality of scheduling instructions. The efficient solution capability enables the scheduling system to cope with the computational pressure brought about by the increase in the number of vehicles, maintaining stable output efficiency and avoiding untimely instruction issuance due to computational delays, thereby improving the response speed and operational stability of power resource scheduling.

[0063] Continue to refer to Figure 1 In step 140, the bidirectional power adjustment command is output to control each vehicle to perform the corresponding charging or discharging operation.

[0064] In this application, the bidirectional power regulation commands obtained from the solution can be sent to the execution layer devices, which then control the vehicle to complete the corresponding charging and discharging actions, transforming the optimization results into actual power regulation effects. The command output method can be selected according to the system architecture; it can be uniformly issued by the cloud platform or locally forwarded and executed by the edge control gateway.

[0065] In this application, the edge control gateway can issue corresponding bidirectional power adjustment commands to each vehicle. After receiving the cluster power command issued by the regional aggregation platform, the edge control gateway can break down the command into specific power commands for individual vehicles and then send them to the corresponding vehicle's onboard intelligent terminal via the communication network. After receiving the command, the onboard intelligent terminal interacts with the vehicle's battery management system and charger control system to adjust the charger's operating mode and output power, precisely executing charging or discharging operations. For example, when the command requires the vehicle to discharge at a power of 3 kilowatts, the onboard terminal will control the charger to operate in inverter mode, converting the DC power in the battery into AC power that conforms to the grid standard and sending it to the grid, realizing energy transfer from the vehicle to the grid.

[0066] In this application, by executing command issuance and power control through an edge control gateway, the optimization results can be accurately implemented, ensuring that the actual charging and discharging power of each vehicle is consistent with the dispatch command. Precise execution control enables the total regulation power of the cluster to accurately match the target demand of the power grid, reducing the discount on dispatch effect caused by execution deviation, thereby improving the execution accuracy and actual regulation effect of power resource dispatch, and ensuring the stable achievement of dispatch objectives.

[0067] In this application, steps 151 to 154 may also be performed: Step 151: Determine the basic incentive electricity price corresponding to the real-time supply and demand status of the power grid.

[0068] Step 152: Determine the state-of-charge compensation price based on the difference between the average state of charge of all currently participating vehicles and the real-time state of charge of the corresponding vehicle.

[0069] Step 153: Determine the participation reward electricity price based on the historical participation score of the corresponding vehicle, wherein the historical participation score is generated based on the vehicle's historical dispatch records.

[0070] Step 154: The sum of the basic incentive electricity price, the state-of-charge compensation electricity price, and the participation reward electricity price is used as the dynamic incentive electricity price for the corresponding vehicle.

[0071] In this application, the dynamic incentive electricity price is the core mechanism for regulating user participation and achieving fair distribution of benefits. Its value dynamically changes with the grid status, vehicle status, and user behavior, rather than being a fixed, uniform price. The specific dynamic incentive electricity price can be calculated using the following formula:

[0072] in, for Pay to the first The incentive electricity price for each vehicle is expressed in yuan per kilowatt-hour; for The base incentive electricity price at any given time, in yuan per kilowatt-hour; This represents the average state of charge of all vehicles currently participating in the dispatch process. For the first Taiwanese vehicles Real-time state of charge at any given moment; For the state-of-charge compensation term, the weighting coefficient is used. For the first Historical participation rating of vehicles in Taiwan; The weighting coefficients for participation rating items.

[0073] In this application, the basic incentive electricity price is directly related to the supply and demand tension of the power grid. During peak grid load periods and periods of urgent regulation demand, the basic incentive electricity price will be increased accordingly to attract more users to participate in dispatch; during periods of smooth supply and demand, the basic incentive electricity price will remain at a lower level. The state of charge compensation item reflects additional compensation for users discharging with low charge. When the real-time state of charge of a vehicle is below the average level, users will sacrifice their own power reserves when discharging, facing a higher risk of temporary power shortages, thus requiring higher electricity price compensation. The participation reward item is used to incentivize users to participate in dispatch in a long-term and stable manner. Users with higher historical participation frequency and better performance records will have higher participation scores and can obtain higher electricity price rewards. For example, if the base incentive price is 1 yuan per kilowatt-hour for a certain period, the average state of charge (SOC) of all vehicles is 70%, the real-time SOC of a certain vehicle is 50%, the SOC compensation weighting coefficient is 0.01 yuan per kilowatt-hour per percentage point, the vehicle's historical participation score is 80 points, and the participation reward weighting coefficient is 0.005 yuan per kilowatt-hour per point, then the corresponding dynamic incentive price for that vehicle is 1 yuan per kilowatt-hour plus 0.2 yuan per kilowatt-hour SOC compensation plus 0.4 yuan per kilowatt-hour participation reward, totaling 1.6 yuan per kilowatt-hour.

[0074] In this application, a dynamic incentive pricing mechanism based on multi-dimensional contribution can achieve the principle of fairness in distributing benefits according to contribution, allowing users who sacrifice more and contribute more to obtain higher economic returns. At the same time, long-term rewards can improve user retention and activity. A reasonable pricing mechanism can continuously expand the scale of vehicles participating in dispatch, ensure the sufficiency of dispatchable resources, and avoid the limitation of dispatching capacity due to insufficient dispatchable vehicles, thereby further enhancing the stability and sustainability of power resource dispatch.

[0075] In this application, steps 161 to 162 may also be performed: Step 161: After the scheduling cycle ends, collect the actual charging and discharging data and scheduling contribution of each vehicle.

[0076] Step 162: Calculate the incentive fee based on the actual charging and discharging data and the corresponding dynamic incentive electricity price, and update the historical dispatch record of the corresponding vehicle.

[0077] In this application, the scheduling cycle can be set according to the operating scenario. For example, it can be based on a calendar day or a single peak shaving or valley filling process. After the scheduling cycle ends, the system will collect data such as the actual charging and discharging capacity, response time, power adjustment accuracy, and performance of each vehicle to comprehensively evaluate the vehicle's contribution to the scheduling. The actual charging and discharging data is based on the actual values ​​collected by the on-board terminal and the electricity meter to ensure the accuracy and fairness of the settlement.

[0078] In this application, the incentive fee due to the user can be calculated based on the actual discharge volume and the corresponding dynamic incentive electricity price, and the fee can be automatically distributed to the user's linked account. At the same time, the system will update the participation status of this dispatch to the vehicle's historical dispatch record, including participation time, charging and discharging volume, and fulfillment status, and simultaneously update the user's historical participation score as the basis for subsequent dispatch willingness assessment and incentive pricing.

[0079] In this application, accurate settlement and continuous updates of historical records ensure timely realization of users' economic benefits, enhancing their trust in the dispatching service. Simultaneously, the continuously accumulating historical data optimizes the accuracy of dispatching willingness assessment and incentive pricing, creating a positive feedback loop that gradually improves user participation and the adaptability of dispatching schemes, thereby continuously improving the stability and operational effectiveness of power resource dispatching.

[0080] In this application, steps 171 to 172 may also be performed: Step 171: Receive the dispatch service registration information and travel plan information submitted by the user.

[0081] Step 172: Periodically collect real-time vehicle status parameters of each vehicle and update the vehicle status parameters.

[0082] In this application, users can register for the dispatch service through a mobile application, submitting information such as vehicle model, battery parameters, and linked account. They can also set daily travel plans, such as departure and return times on weekdays and required battery power, and can modify their plans at any time based on temporary travel needs. After receiving this information, the system updates the preset departure time and required state of charge for the corresponding vehicle, ensuring that the dispatch plan matches the user's latest travel arrangements.

[0083] During the scheduling process, the system periodically collects real-time status parameters of the vehicles through the edge control gateway, such as the state of charge, charging status, and location information every 5 minutes, to keep abreast of changes in the vehicle's status. When the vehicle's status changes, such as a decrease in battery power or the vehicle leaving the charging location, the system will synchronously update the input parameters of the scheduling model to ensure that the scheduling calculations are based on the latest status data.

[0084] In this application, by registering users and reporting their travel plans, the system can proactively determine vehicle availability and power demand, providing accurate preliminary information for dispatch planning. Periodic status updates ensure the timeliness of dispatch data and reduce deviations in dispatch plans caused by status changes. This mechanism effectively improves the accuracy of basic dispatch data, reduces the probability of dispatch plans not matching actual conditions, and thus enhances the reliability and stability of power resource dispatch.

[0085] In this application, steps 181 to 182 may also be performed: Step 181: During the scheduling process, monitor the status changes of each vehicle and the changes in the power grid target adjustment requirements in real time.

[0086] Step 182: When the state change exceeds the preset threshold, reconstruct and solve the multi-objective optimization scheduling model, and update the bidirectional power adjustment command for each vehicle.

[0087] In this application, various unforeseen circumstances may arise during the dispatching process, such as some vehicles leaving the dispatching process early due to temporary user travel, some vehicles experiencing charging failures and being unable to continue participating in regulation, or significant changes in the grid's regulation demands due to unforeseen circumstances. These changes will alter the execution conditions of the original dispatching plan, and if the original instructions continue to be executed, the predetermined regulation objectives may not be achieved.

[0088] In this application, the system monitors changes in vehicle status and grid demand in real time and presets corresponding trigger thresholds. For example, when the proportion of abnormally withdrawn vehicles exceeds 10%, or the change in grid regulation demand exceeds 20%, a rescheduling process is triggered. During rescheduling, the system reconstructs and solves the multi-objective optimization scheduling model based on the latest vehicle status and regulation demand, generates updated bidirectional power regulation commands, and issues them for execution. The preset thresholds can be flexibly adjusted according to actual operational needs, ensuring the flexibility of scheduling response while avoiding frequent rescheduling that could affect system stability.

[0089] In this application, through real-time monitoring and dynamic rescheduling mechanisms, the system can promptly respond to various sudden anomalies during the scheduling process, dynamically adjust the power allocation scheme, fill the power adjustment gap caused by the withdrawal of abnormal vehicles, and ensure that the total regulation power of the cluster always meets the target demand of the power grid. This dynamic adjustment capability can effectively reduce the impact of local anomalies on the overall scheduling effect, improve the anti-interference capability of the scheduling system, and thus further enhance the stability of power resource scheduling between vehicles and the power grid.

[0090] The following describes an embodiment of the apparatus described in this application, which can be used to execute the method for scheduling electrical resources between vehicles and the power grid as described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method for scheduling electrical resources between vehicles and the power grid described above in this application.

[0091] See Figure 2 The diagram shows a block diagram of a device for scheduling electrical resources between a vehicle and the power grid in an embodiment of this application.

[0092] like Figure 2 As shown, the scheduling device 200 for power resources between a vehicle and the power grid according to an embodiment of this application includes: an acquisition unit 201, a construction unit 202, a solution unit 203, and an output unit 204.

[0093] The system includes: an acquisition unit 201, which acquires the vehicle status parameters of each vehicle participating in the scheduling, as well as the target adjustment parameters of the power grid, and determines the battery loss cost parameters and scheduling intention parameters of the corresponding vehicles based on the vehicle status parameters; a construction unit 202, which constructs a multi-objective optimization scheduling model based on the target adjustment parameters, the battery loss cost parameters and scheduling intention parameters of each vehicle, with the optimization objectives of minimizing the power fluctuation of the power grid tie line and maximizing the total net scheduling revenue, combined with preset constraints; a solution unit 203, which solves the multi-objective optimization scheduling model to obtain the bidirectional power adjustment commands of each vehicle in each scheduling period; and an output unit 204, which outputs the bidirectional power adjustment commands to control each vehicle to perform the corresponding charging or discharging operation.

[0094] In some embodiments of this application, based on the aforementioned scheme, the vehicle status parameters include battery health status, real-time state of charge, preset departure time, and historical scheduling records. The acquisition unit 201 is configured to: acquire the battery degradation coefficient and single scheduling discharge amount of the corresponding vehicle; calculate the battery loss cost parameter of the corresponding vehicle according to the correspondence that the loss cost is proportional to the square of the single scheduling discharge amount and inversely proportional to the battery health status; and quantify the scheduling intention parameter of the corresponding vehicle by combining the real-time state of charge, the preset departure time, the historical scheduling records, and the dynamic incentive electricity price.

[0095] In some embodiments of this application, based on the foregoing scheme, the target adjustment parameters include the tie-line power target value, peak shaving adjustment demand, and valley filling adjustment demand; the optimization objective of minimizing grid tie-line power fluctuation includes minimizing the sum of squared deviations between the actual tie-line power and the tie-line power target value in each scheduling period; the optimization objective of maximizing the total net scheduling revenue includes maximizing the difference between the sum of the electrical energy revenue and ancillary service revenue in each scheduling period and the sum of the incentive fees paid to each vehicle and the costs corresponding to the battery loss cost parameters.

[0096] In some embodiments of this application, based on the aforementioned scheme, the vehicle state parameters include a preset demand state of charge, and the preset constraints include: the state of charge of a single vehicle is within a preset safety range; at the preset departure time of the corresponding vehicle, its state of charge is not lower than the demand state of charge; the charging power of a single vehicle is within a preset charging power range; and the discharging power of a single vehicle is within a preset discharging power range.

[0097] In some embodiments of this application, based on the foregoing scheme, the solving unit 203 is configured to: use the alternating direction multiplier method to perform distributed solving of the multi-objective optimization scheduling model to obtain bidirectional power adjustment commands for each vehicle.

[0098] In some embodiments of this application, based on the foregoing scheme, the device further includes: a determining unit, configured to determine a basic incentive price corresponding to the real-time supply and demand status of the power grid; determine a state-of-charge compensation price based on the difference between the average state of charge of all currently participating vehicles and the real-time state of charge of the corresponding vehicle; determine a participation reward price based on the historical participation score of the corresponding vehicle, wherein the historical participation score is generated based on the historical dispatch records of the vehicle; and use the sum of the basic incentive price, the state-of-charge compensation price, and the participation reward price as the dynamic incentive price of the corresponding vehicle.

[0099] In some embodiments of this application, based on the foregoing scheme, the device further includes: a first update unit, used to, after the end of the scheduling cycle, collect the actual charging and discharging data and scheduling contribution of each vehicle; calculate the incentive fee based on the actual charging and discharging data and the corresponding dynamic incentive electricity price; and update the historical scheduling record of the corresponding vehicle.

[0100] In some embodiments of this application, based on the foregoing scheme, the device further includes: a second update unit, configured to receive dispatch service registration information and travel plan information submitted by the user; periodically collect real-time vehicle status parameters of each vehicle and update the vehicle status parameters.

[0101] In some embodiments of this application, based on the aforementioned scheme, the construction unit 202 is configured to: monitor the state changes of each vehicle and the changes in the grid target adjustment requirements in real time during the scheduling execution process; when the state change exceeds a preset threshold, reconstruct and solve the multi-objective optimization scheduling model, and update the bidirectional power adjustment instructions of each vehicle.

[0102] Based on the same inventive concept, embodiments of this application provide a computer program product, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor, so as to cause a computer device having the processor to perform operations performed by the method for scheduling electrical resources between vehicles and the power grid as described above.

[0103] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations performed by the method for scheduling electrical resources between vehicles and the power grid as described above.

[0104] Based on the same inventive concept, this application also provides an electronic device, see reference. Figure 3 The diagram shows a schematic of the structure of an electronic device in an embodiment of this application. The electronic device includes one or more memories 304, one or more processors 302, and at least one computer program (computer program instruction) stored in the memory 304 and executable on the processor 302. When the processor 302 executes the computer program, it implements the method for scheduling electrical resources between the vehicle and the power grid as described above.

[0105] Among them, Figure 3In this document, a bus architecture (represented by bus 300) is used. Bus 300 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0106] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions can be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units can be integrated into a single processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0109] When the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0110] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for scheduling electrical resources between vehicles and the power grid, characterized in that, The method includes: Obtain the vehicle status parameters of each vehicle participating in the scheduling, as well as the target adjustment parameters of the power grid, and determine the battery loss cost parameters and scheduling intention parameters of the corresponding vehicles based on the vehicle status parameters of each vehicle. Based on the target adjustment parameters, battery loss cost parameters and scheduling intention parameters of each vehicle, a multi-objective optimization scheduling model is constructed with the optimization objectives of minimizing power fluctuations in the power grid interconnection and maximizing total net scheduling revenue, combined with preset constraints. Solve the multi-objective optimization scheduling model to obtain the bidirectional power adjustment command for each vehicle in each scheduling period; The bidirectional power adjustment command is output to control each vehicle to perform the corresponding charging or discharging operation.

2. The method according to claim 1, characterized in that, The vehicle status parameters include battery health status, real-time state of charge, preset departure time, and historical scheduling records. The process of determining the battery loss cost parameters and scheduling intention parameters for each vehicle based on its status parameters includes: Obtain the battery degradation coefficient and single-discharge amount for the corresponding vehicle; Based on the correspondence that the loss cost is directly proportional to the square of the single dispatch discharge and inversely proportional to the battery health status, the battery loss cost parameters for the corresponding vehicle are calculated. By combining the real-time charge status of the corresponding vehicle, the preset departure time, the historical scheduling records, and the dynamic incentive electricity price, the scheduling intention parameters of the corresponding vehicle are quantified.

3. The method according to claim 1, characterized in that, The target adjustment parameters include the tie-line power target value, peak shaving adjustment demand, and valley filling adjustment demand; the optimization objective of minimizing the power fluctuation of the grid tie-line includes minimizing the sum of squares of the deviations between the actual power of the tie-line and the target power value of the tie-line in each scheduling period; the optimization objective of maximizing the total net scheduling revenue includes maximizing the difference between the sum of the electricity revenue and ancillary service revenue in each scheduling period and the sum of the incentive fees paid to each vehicle and the costs corresponding to the battery loss cost parameters.

4. The method according to claim 1, characterized in that, The vehicle status parameters include a preset required state of charge, and the preset constraints include: the state of charge of a single vehicle is within a preset safe range; at the preset departure time of the corresponding vehicle, its state of charge is not lower than the required state of charge; the charging power of a single vehicle is within a preset charging power range; and the discharging power of a single vehicle is within a preset discharging power range.

5. The method according to claim 1, characterized in that, Solving the multi-objective optimization scheduling model yields bidirectional power adjustment commands for each vehicle during each scheduling period, including: The multi-objective optimization scheduling model is solved in a distributed manner using the alternating direction multiplier method to obtain the bidirectional power adjustment commands for each vehicle.

6. The method according to claim 1, characterized in that, The method further includes: Determine the basic incentive electricity price corresponding to the real-time supply and demand status of the power grid; The state-of-charge (SOC) compensation price is determined based on the difference between the average SOC of all vehicles currently participating in the dispatch and the real-time SOC of the corresponding vehicle. The participation incentive electricity price is determined based on the historical participation score of the corresponding vehicle, and the historical participation score is generated based on the vehicle's historical dispatch records; The sum of the basic incentive electricity price, the state-of-charge compensation electricity price, and the participation reward electricity price is used as the dynamic incentive electricity price for the corresponding vehicle.

7. The method according to claim 6, characterized in that, The method further includes: After the scheduling cycle ends, the actual charging and discharging data of each vehicle and its contribution to the scheduling are collected. The incentive fee is calculated based on the actual charging and discharging data and the corresponding dynamic incentive electricity price, and the historical dispatch record of the corresponding vehicle is updated.

8. The method according to claim 1, characterized in that, The method further includes: Receive user-submitted dispatch service registration information and travel plan information; The system periodically collects and updates the real-time vehicle status parameters of each vehicle.

9. The method according to claim 1, characterized in that, The method further includes: During the scheduling process, the status changes of each vehicle and the changes in the power grid target adjustment requirements are monitored in real time. When the state change exceeds a preset threshold, the multi-objective optimization scheduling model is reconstructed and solved, and the bidirectional power adjustment command for each vehicle is updated.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as described in any one of claims 1 to 9.

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