V2G interaction control method, apparatus and device, and storage medium

Abstract

The invention provides a V2G interaction control method and device, equipment and a storage medium, and the method comprises the steps: receiving an adjustment instruction of a power grid, and broadcasting the adjustment instruction to a V2G charging equipment cluster; wherein the adjusting instruction comprises a total power adjusting target; a local decision maker corresponding to each V2G charging device in the cluster monitors local information and exchanges state information with the adjacent V2G charging device; and based on the adjustment instruction, the local information and the state information, the local decision-making device adopts a distributed decision-making algorithm to calculate the optimal charging / discharging power so as to meet the total power adjustment target of the power grid side, the charging expectation target of the user side and the battery loss reduction target of the equipment side. According to the invention, the decision-making right is issued to the local decision-making device of each V2G charging device, no longer depends on a single centralized controller, and can solve a series of problems of single-point bottleneck, response lag, control stiffness, poor expandability and the like of an existing centralized V2G control strategy.

Description

Technical Field

[0001] This application relates to the field of new energy power grid technology, and in particular to a V2G interactive control method, device, equipment and storage medium. Background Technology

[0002] With the rapid development of the new energy vehicle industry, large-scale grid connection of electric vehicles has become a reality. Vehicle-to-grid (V2G) technology, as a key means of absorbing renewable energy and improving grid flexibility, is receiving widespread attention. The core idea of ​​V2G is to treat a large number of electric vehicle charging stations as a distributed, controllable energy storage cluster, providing various ancillary services such as peak shaving and frequency regulation to the grid through centralized management of their charging and discharging behavior.

[0003] Currently, the mainstream V2G control strategy in the industry generally adopts a centralized architecture. In this architecture, the power grid dispatch center issues total power regulation commands to a centralized controller (such as a site controller or cloud platform). This centralized controller needs to collect real-time status information of all V2G charging devices within the cluster, perform unified optimization calculations based on global information, and then decompose and distribute the calculated charging and discharging commands to each terminal device for execution. However, the current centralized control strategy has the following drawbacks:

[0004] 1. System reliability has a single point of bottleneck: The centralized controller becomes the performance bottleneck and the single point of failure for the entire system. Once the controller fails or communication with the power grid is interrupted, the entire V2G cluster will fall into an uncontrolled state, unable to provide effective support to the power grid, and may even exacerbate power grid fluctuations.

[0005] 2. Slow Dynamic Response: Commands need to undergo multi-level transmission and processing through "cloud-site-pile-vehicle," resulting in long links and high latency. This makes it difficult for the system to meet the rapid response requirements of milliseconds to seconds for primary frequency regulation of the power grid, greatly limiting the ability of V2G resources to participate in high-value power grid services.

[0006] 3. Rigid control strategy: Centralized optimization usually adopts a uniform strategy, which makes it difficult to adaptively and finely take into account the personalized needs of each user, and it is difficult to achieve true optimization.

[0007] 4. Poor scalability and limited cluster size: As the size of the V2G cluster increases, the amount of data and computational complexity that the central controller needs to process grows exponentially, resulting in enormous computational and communication pressure.

[0008] Therefore, existing V2G control strategies based on centralized architecture face a combination of challenges in terms of reliability, response speed, user satisfaction, and scalability. Summary of the Invention

[0009] Based on this, this application proposes a V2G interactive control method, device, equipment, and storage medium, aiming to solve a series of comprehensive problems existing in the current centralized V2G control strategy, such as single-point bottleneck, response lag, control rigidity, and poor scalability.

[0010] Firstly, the technical solution provided in this application includes:

[0011] A V2G interactive control method, the method comprising:

[0012] The guidance module receives the adjustment command from the power grid and broadcasts the adjustment command to the V2G charging equipment cluster; wherein, the adjustment command includes a total power adjustment target;

[0013] Each V2G charging device in the cluster has its own local decision-maker that monitors local information in real time and exchanges status information with the local decision-makers of neighboring V2G charging devices through neighborhood communication.

[0014] Based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0015] Furthermore, the local information includes grid frequency, voltage, vehicle real-time SOC, user-set minimum expected SOC, and departure time.

[0016] Furthermore, the status information includes real-time charging / discharging power and the adjustable range of charging / discharging power.

[0017] Furthermore, the local decision-maker obtains the optimal charging / discharging power through a multi-objective optimization model, the objective function of which is:

[0018]

[0019] in, The charging / discharging power of the i-th V2G charging device, The standardized power reference value comes from the cooperative algorithm. , , These are power deviation weight, SOC adaptation weight, and equipment loss weight, respectively. This is an adaptation function for the vehicle's current SOC and departure time. Let be the equipment loss function. This represents the objective function value.

[0020] Furthermore, the distributed decision-making algorithm is a constrained consensus algorithm; the local decision-maker performs iterative calculations based on the exchanged real-time charging / discharging power, and in each iteration projects the calculation results onto its own adjustable charging / discharging power range dynamically determined by the local information, so as to generate the optimal charging / discharging power.

[0021] Furthermore, the iterative process of the constrained consensus algorithm is defined by the following mathematical formula:

[0022]

[0023] in, The optimal charge / discharge power calculated after this iteration is... For projection operators, The adjustable range of its own charging / discharging power. Gather for the neighbors, These are the weighting coefficients. Real-time charging / discharging power of neighboring devices at the previous moment;

[0024] in, Based on the vehicle's real-time SOC and departure time dynamic changes.

[0025] Furthermore, the power grid regulation instructions are received by the station controller or cloud platform, and the regulation instructions are broadcast to the V2G charging equipment cluster.

[0026] Secondly, the technical solution provided in this application includes:

[0027] A V2G interactive control method, wherein the method is executed by a local decision-maker corresponding to each V2G charging device in a V2G charging device cluster, comprising:

[0028] Receive regulation instructions from the power grid, wherein the regulation instructions include a total power regulation target;

[0029] It monitors local information in real time and exchanges status information with neighboring V2G charging devices through neighborhood communication;

[0030] Based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0031] Thirdly, the technical solution provided in this application includes:

[0032] A V2G interactive control device, the system comprising:

[0033] The guidance module, which is a station controller or cloud platform, is used to receive the grid's regulation instructions and broadcast the regulation instructions to the V2G charging equipment cluster; the regulation instructions include the grid's total power regulation target;

[0034] A V2G charging device cluster, wherein each V2G charging device in the cluster is configured with a local decision-maker, the local decision-maker including a sensing module, a communication module and a decision-making module;

[0035] The sensing module is used to monitor local information in real time;

[0036] The communication module is used to exchange status information with adjacent V2G charging devices through neighborhood communication.

[0037] The decision module is used to autonomously calculate the optimal charging / discharging power based on the adjustment command, the local information, and the status information using a distributed decision algorithm, so as to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0038] Fourthly, the technical solution provided in this application includes:

[0039] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the V2G interactive control method described above.

[0040] Fifthly, the technical solution provided in this application includes:

[0041] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the V2G interactive control method described above.

[0042] The technical solution provided in this application has at least the following advantages over the prior art:

[0043] 1. Improved system reliability and robustness: Decision-making power is decentralized to the local decision-maker of each V2G charging device, eliminating reliance on a single centralized controller. This decentralized architecture avoids single points of failure; when a local device or communication malfunctions, the rest of the cluster can continue to work collaboratively, thus improving the overall reliability of the system.

[0044] 2. Improved dynamic response speed to meet the rapid adjustment needs of the power grid: By using a local decision-maker to perform parallel calculations based on local and neighborhood information, the processing of instructions at the central node and the multi-level distribution are eliminated, shortening the control loop. This enables the cluster to achieve millisecond to second-level response speeds, providing a technical foundation for participating in rapid services such as primary frequency regulation of the power grid.

[0045] 3. Achieved refined protection of users' personalized needs: The user's charging expectations are taken as one of the core optimization objectives and embedded in the local decision-making logic. This enables each V2G charging device to autonomously weigh its options based on real-time local information when responding to grid commands, ensuring that users' personalized charging needs are met and improving the user experience problems caused by the rigidity of centralized control strategies.

[0046] 4. Enhanced system scalability: The distributed architecture distributes computing and communication loads across the nodes in the cluster. As the cluster grows, each device only needs to collaborate with a limited number of neighbors, and its local computing and communication burden does not increase significantly, enabling better support for the access and application of large-scale V2G charging devices in the future. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an exemplary embodiment of the V2G interactive control method of this application;

[0048] Figure 2 This is a structural diagram of an exemplary embodiment of the V2G interactive control device of this application. Detailed Implementation

[0049] This specific embodiment is merely an explanation of this application and is not intended to limit it. Those skilled in the art, after reading this specification, can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application. To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in 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, not all, of the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0050] The term "comprising" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0051] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0052] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0053] Figure 1 This application illustrates a V2G interactive control method as shown in an exemplary embodiment, the method comprising:

[0054] Step S100: Receive the grid regulation command and broadcast the regulation command to the V2G charging equipment cluster; wherein, the regulation command includes a total power regulation target.

[0055] Specifically, the power grid dispatch center sends regulation instructions, including the total power regulation target, to the guidance module via a dedicated power communication network. This guidance module can be a substation controller or a cloud platform. The guidance module can then broadcast the regulation instructions to the V2G charging equipment clusters within its management range via existing communication networks (such as 4G / 5G, Ethernet, or industrial wireless networks). The guidance module's function is simplified to simply receiving and broadcasting instructions, without performing any complex power allocation calculations or optimization decisions.

[0056] In step S200, each V2G charging device in the cluster has its own local decision-maker that monitors local information in real time and exchanges status information with the local decision-makers of neighboring V2G charging devices through neighborhood communication.

[0057] Specifically, each V2G charging device has a built-in local decision-maker that continuously monitors local information closely related to its own state, including grid frequency, voltage, vehicle real-time SOC (state of charge), user-set minimum expected SOC, and planned departure time.

[0058] Simultaneously, each V2G charging device exchanges status information with neighboring devices through a neighborhood communication network. This status information includes their respective real-time charging / discharging power and the adjustable range of that power. Neighborhood communication can be achieved through conventional short-range communication technologies such as local area networks, power line carrier communication, or device-to-device communication. The significance of this step is that each device can not only understand its own status but also perceive the status of its neighboring devices, laying the information foundation for group collaboration. Through this local information exchange, self-organization and collaborative decision-making can be achieved without a central coordinator.

[0059] In step S300, based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0060] Specifically, the local decision-maker integrates the received global target, its own monitored local information, and the status information obtained from neighboring devices. By running a distributed decision-making algorithm, it independently calculates the most suitable charging / discharging power value for itself. This process considers how to cooperate with the grid's total power target, ensure that users' charging needs are met, and minimize battery loss, achieving a balance between the grid side, the user side, and the device side.

[0061] In some embodiments, the local decision-maker obtains the optimal charging / discharging power through a multi-objective optimization model, wherein the objective function of the multi-objective optimization model is:

[0062]

[0063] in, The charging / discharging power of the i-th V2G charging device, The standardized power reference value comes from the cooperative algorithm. , , These are power deviation weight, SOC adaptation weight, and equipment loss weight, respectively. This is an adaptation function for the vehicle's current SOC and departure time. Let be the equipment loss function. This represents the objective function value.

[0064] in, , , The values ​​of all values ​​are in the range of 0-1, and satisfy the following conditions: + + =1. The adaptation function It is positively correlated with the difference between the vehicle's current SOC and the expected minimum SOC, and the remaining time before departure.

[0065] Specifically, regarding the components of the above objective function:

[0066] 1. Power Tracking Items:

[0067] This aims to increase the final output power of device i. Approaching a standardized power reference value This standardized power reference value It is not fixed but dynamically generated during the iteration process. It reflects the reference power level that device i should contribute at the current moment in order to collaboratively achieve the overall power regulation target of the power grid. By minimizing this term, it is ensured that the behavior of individual devices is consistent with the collaborative goal of the cluster. Weighting coefficient This is used to adjust the importance of this item within the overall objective.

[0068] 2. User needs guarantee items:

[0069] This is the core of ensuring the user's expected charging goals. It is an adaptation function related to real-time State of Charge (SOC) and departure time. Its design goal is to ensure that the vehicle battery reaches the user-defined minimum SOC before the planned departure time. Weighting coefficients... This determines the relative priority of user needs in decision-making.

[0070] 3. Equipment Loss Items:

[0071] This is used to achieve the goal of reducing battery loss on the device side. This is a device loss function, typically related to the magnitude and direction of charge / discharge power. For example, high-power discharge or charging usually accelerates battery aging, thus imposing a higher "cost" on such operations. Minimizing this term guides the local decision-maker to prioritize battery-health-friendly and gentler charge / discharge strategies while meeting both grid-side and user-side requirements. Weighting coefficients This is used to balance battery life with other objectives.

[0072] In other words, the process of the local decision-maker finding the minimum value of the objective function is essentially about finding an optimal balance among three competing objectives: power tracking deviation, user demand satisfaction, and battery health. (Weighting coefficients) , , It acts as an "adjustment knob," capable of being adjusted according to actual application scenarios and policy guidance. For example, in emergency situations where the power grid urgently needs support, the adjustment can be increased. In normal times, however, more attention may be paid to user satisfaction and battery protection, and adjustments may be made accordingly. and .

[0073] This embodiment embeds this model into each local decision maker, enabling each V2G charging device to make trade-off-based decisions. It no longer simply responds to a unified charge / discharge command, but instead, while understanding the global objective, comprehensively considers its own specific circumstances and the long-term economic viability of the device, calculating an optimal charge / discharge power that is more beneficial to the grid side, the user side, and the device side.

[0074] In some embodiments, the distributed decision-making algorithm is a constrained consensus algorithm; the local decision-maker performs iterative calculations based on the exchanged real-time charging / discharging power, and in each iteration projects the calculation results onto its own adjustable charging / discharging power range dynamically determined by the local information, so as to generate the optimal charging / discharging power.

[0075] Specifically, after the local decision-maker starts, it will perform continuous iterative calculations based on the real-time charging / discharging power exchanged with neighboring devices. First, the local decision-maker performs collaborative calculations, collecting the power values ​​of all its neighboring devices at the previous time step and calculating a reference power value through specific collaborative rules. This reference value reflects the general power level of the "neighbor group" at the current time step and serves as a collaborative signal guiding devices towards cluster consensus. Next, the local decision-maker performs a constrained projection operation, which is the core of the algorithm known as "constrained." The local decision-maker maps the reference power value calculated in the previous step to its own adjustable charging / discharging power range, dynamically determined by local information. This projection operation ensures that: if the reference value falls within its own feasible range, it is considered; if it exceeds the range, it is adjusted to the nearest boundary value within the range. The projected result is the new optimal charging / discharging power generated after this iteration.

[0076] In this embodiment, a rigid constraint dynamically calculated from local information to guarantee user needs is embedded as a safety boundary into the distributed collaborative process. This means that regardless of how power information interacts between neighbors, the algorithm's output in each iteration strictly satisfies the device's individual conditions, thus fundamentally ensuring that the user's charging needs are not sacrificed in response to grid regulation. Through this constraint-protected iterative mechanism, the power values ​​of all devices gradually reach a stable state under the combined effect of mutual cooperation and their respective constraints. At this point, not only does the power of each device meet its feasible region requirements, but the sum of the power of all devices can effectively respond to the total power regulation target issued by the grid.

[0077] In some embodiments, the iterative process of the constrained consensus algorithm is defined by the following mathematical formula:

[0078]

[0079] in, The optimal charge / discharge power calculated after this iteration is... For projection operators, The adjustable range of its own charging / discharging power. Gather for the neighbors, These are the weighting coefficients. Real-time charging / discharging power of neighboring devices at the previous moment;

[0080] in, Based on the vehicle's real-time SOC and departure time dynamic changes.

[0081] Specifically, the above mathematical formula describes the update process of the charging / discharging power value from time k to time k+1. Each local decision-maker periodically recalculates its own power setpoint based on the latest acquired neighbor information, forming a continuously optimized closed-loop control.

[0082] Regarding the above mathematical formula:

[0083] 1. Weighted summation of power from neighboring devices: Device i collects its neighbor set Power values ​​of all devices in the previous moment And through weighting coefficients Weighted fusion is performed. The weighting coefficients are typically designed based on the network topology to satisfy... =1, ensuring that a valid weighted average is calculated. This value provides a reference direction for the group behavior of each device.

[0084] 2. Projection Operation: It maps the weighted summation result to the device's own feasible domain. Inside.

[0085] In this embodiment, through this constraint-protected iterative mechanism, all devices perform a parallel loop of "collecting neighbor information - calculating reference values ​​- projecting onto the feasible region". After multiple iterations, even if each device only communicates with a limited number of neighbors, the power value of the entire cluster will spontaneously converge to a stable consensus state under the combined effect of mutual cooperation and individual constraints.

[0086] The significant advantages of this distributed algorithm in this embodiment are: 1. Fully parallel computing: Each device performs computation independently, greatly improving computational efficiency and avoiding the bottleneck of centralized computing. 2. Naturally satisfies constraints: The projection operation ensures that the intermediate and final results of each iteration strictly satisfy the device's own operating limitations. 3. Achieves global optimization: The converged power allocation automatically approximates Pareto optimality, achieving the group objective while satisfying all individual constraints.

[0087] An embodiment of this application also provides a V2G interactive control method, which is executed by a local decision-maker corresponding to each V2G charging device in a V2G charging device cluster, including:

[0088] Step S1: Receive the regulation command from the power grid, wherein the regulation command includes a total power regulation target.

[0089] Step S2: Monitor local information in real time and exchange status information with neighboring V2G charging devices through neighborhood communication.

[0090] Specifically, the local information includes grid frequency, voltage, vehicle real-time SOC (state of charge), user-set minimum expected SOC, and planned departure time. The status information includes each device's real-time charging / discharging power and its adjustable range. This step is significant because it enables each device not only to understand its own status but also to perceive the status of neighboring devices, laying the information foundation for group collaboration. Through this local information exchange, self-organization and collaborative decision-making can be achieved without a central coordinator.

[0091] Step S3: Based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0092] Specifically, the local decision-maker integrates the received global target, its own monitored local information, and the status information obtained from neighboring devices. By running a distributed decision-making algorithm, it independently calculates the most suitable charging / discharging power value for itself. This process considers how to cooperate with the grid's total power target, ensure that users' charging needs are met, and minimize battery loss, achieving a balance between the grid side, the user side, and the device side.

[0093] The local decision-maker obtains the optimal charging / discharging power through a multi-objective optimization model, the objective function of which is:

[0094]

[0095] in, The charging / discharging power of the i-th V2G charging device, The standardized power reference value comes from the cooperative algorithm. , , These are power deviation weight, SOC adaptation weight, and equipment loss weight, respectively. This is an adaptation function for the vehicle's current SOC and departure time. Let be the equipment loss function. This represents the objective function value.

[0096] in, , , The values ​​of all values ​​are in the range of 0-1, and satisfy the following conditions: + + =1. The adaptation function It is positively correlated with the difference between the vehicle's current SOC and the expected minimum SOC, and the remaining time before departure.

[0097] Specifically, regarding the components of the above objective function:

[0098] 1. Power Tracking Items:

[0099] This aims to increase the final output power of device i. Approaching a standardized power reference value This standardized power reference value It is not fixed but dynamically generated during the iteration process. It reflects the reference power level that device i should contribute at the current moment in order to collaboratively achieve the overall power regulation target of the power grid. By minimizing this term, it is ensured that the behavior of individual devices is consistent with the collaborative goal of the cluster. Weighting coefficient This is used to adjust the importance of this item within the overall objective.

[0100] 2. User needs guarantee items:

[0101] This is the core of ensuring the user's expected charging goals. It is an adaptation function related to real-time State of Charge (SOC) and departure time. Its design goal is to ensure that the vehicle battery reaches the user-defined minimum SOC before the planned departure time. Weighting coefficients... This determines the relative priority of user needs in decision-making.

[0102] 3. Equipment Loss Items:

[0103] This is used to achieve the goal of reducing battery loss on the device side. This is a device loss function, typically related to the magnitude and direction of charge / discharge power. For example, high-power discharge or charging usually accelerates battery aging, thus imposing a higher "cost" on such operations. Minimizing this term guides the local decision-maker to prioritize battery-health-friendly and gentler charge / discharge strategies while meeting both grid-side and user-side requirements. Weighting coefficients This is used to balance battery life with other objectives.

[0104] In other words, the process of the local decision-maker finding the minimum value of the objective function is essentially about finding an optimal balance among three competing objectives: power tracking deviation, user demand satisfaction, and battery health. (Weighting coefficients) , , It acts as an "adjustment knob," capable of being adjusted according to actual application scenarios and policy guidance. For example, in emergency situations where the power grid urgently needs support, the adjustment can be increased. In normal times, however, more attention may be paid to user satisfaction and battery protection, and adjustments may be made accordingly. and .

[0105] This embodiment embeds this model into each local decision maker, enabling each V2G charging device to make trade-off-based decisions. It no longer simply responds to a unified charge / discharge command, but instead, while understanding the global objective, comprehensively considers its own specific circumstances and the long-term economic viability of the device, calculating an optimal charge / discharge power that is more beneficial to the grid side, the user side, and the device side.

[0106] The distributed decision-making algorithm is a constrained consensus algorithm; the local decision-maker performs iterative calculations based on the exchanged real-time charging / discharging power, and in each iteration projects the calculation results onto its own adjustable charging / discharging power range dynamically determined by the local information, so as to generate the optimal charging / discharging power.

[0107] Specifically, after the local decision-maker starts, it will perform continuous iterative calculations based on the real-time charging / discharging power exchanged with neighboring devices. First, the local decision-maker performs collaborative calculations, collecting the power values ​​of all its neighboring devices at the previous time step and calculating a reference power value through specific collaborative rules. This reference value reflects the general power level of the "neighbor group" at the current time step and serves as a collaborative signal guiding devices towards cluster consensus. Next, the local decision-maker performs a constrained projection operation, which is the core of the algorithm known as "constrained." The local decision-maker maps the reference power value calculated in the previous step to its own adjustable charging / discharging power range, dynamically determined by local information. This projection operation ensures that: if the reference value falls within its own feasible range, it is considered; if it exceeds the range, it is adjusted to the nearest boundary value within the range. The projected result is the new optimal charging / discharging power generated after this iteration.

[0108] In this embodiment, a rigid constraint dynamically calculated from local information to guarantee user needs is embedded as a safety boundary into the distributed collaborative process. This means that regardless of how power information interacts between neighbors, the algorithm's output in each iteration strictly satisfies the device's individual conditions, thus fundamentally ensuring that the user's charging needs are not sacrificed in response to grid regulation. Through this constraint-protected iterative mechanism, the power values ​​of all devices gradually reach a stable state under the combined effect of mutual cooperation and their respective constraints. At this point, not only does the power of each device meet its feasible region requirements, but the sum of the power of all devices can effectively respond to the total power regulation target issued by the grid.

[0109] The iterative process of the constrained consensus algorithm is defined by the following mathematical formula:

[0110]

[0111] in, The optimal charge / discharge power calculated after this iteration is... For projection operators, The adjustable range of its own charging / discharging power. Gather for the neighbors, These are the weighting coefficients. Real-time charging / discharging power of neighboring devices at the previous moment;

[0112] in, Based on the vehicle's real-time SOC and departure time dynamic changes.

[0113] Specifically, the above mathematical formula describes the update process of the charging / discharging power value from time k to time k+1. Each local decision-maker periodically recalculates its own power setpoint based on the latest acquired neighbor information, forming a continuously optimized closed-loop control.

[0114] Regarding the above mathematical formula:

[0115] 1. Weighted summation of power from neighboring devices: Device i collects its neighbor set Power values ​​of all devices in the previous moment And through weighting coefficients Weighted fusion is performed. The weighting coefficients are typically designed based on the network topology to satisfy... =1, ensuring that a valid weighted average is calculated. This value provides a reference direction for the group behavior of each device.

[0116] 2. Projection Operation: It maps the weighted summation result to the device's own feasible domain. Inside.

[0117] In this embodiment, through this constraint-protected iterative mechanism, all devices perform a parallel loop of "collecting neighbor information - calculating reference values ​​- projecting onto the feasible region". After multiple iterations, even if each device only communicates with a limited number of neighbors, the power value of the entire cluster will spontaneously converge to a stable consensus state under the combined effect of mutual cooperation and individual constraints.

[0118] The significant advantages of this distributed algorithm in this embodiment are: 1. Fully parallel computing: Each device performs computation independently, greatly improving computational efficiency and avoiding the bottleneck of centralized computing. 2. Naturally satisfies constraints: The projection operation ensures that the intermediate and final results of each iteration strictly satisfy the device's own operating limitations. 3. Achieves global optimization: The converged power allocation automatically approximates Pareto optimality, achieving the group objective while satisfying all individual constraints.

[0119] like Figure 2 As shown, one embodiment of this application also provides a V2G interactive control device, the device comprising:

[0120] The guidance module, which is a station controller or cloud platform, is used to receive the grid's regulation instructions and broadcast the regulation instructions to the V2G charging equipment cluster; the regulation instructions include the grid's total power regulation target;

[0121] A V2G charging device cluster, wherein each V2G charging device in the cluster is configured with a local decision-maker, the local decision-maker including a sensing module, a communication module and a decision-making module;

[0122] The sensing module is used to monitor local information in real time;

[0123] The communication module is used to exchange status information with adjacent V2G charging devices through neighborhood communication.

[0124] The decision module is used to autonomously calculate the optimal charging / discharging power based on the adjustment command, the local information, and the status information using a distributed decision algorithm, so as to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

[0125] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0126] An embodiment of this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described V2G interactive control method.

[0127] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements the aforementioned methods. The display screen can be an LCD screen or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0128] An embodiment of this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the V2G interactive control method described above.

[0129] An embodiment of this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the V2G interactive control method described above.

[0130] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0131] In summary, this application provides a V2G interactive control method, apparatus, device, and storage medium, which are capable of:

[0132] 1. Improved system reliability and robustness: Decision-making power is decentralized to the local decision-maker of each V2G charging device, eliminating reliance on a single centralized controller. This decentralized architecture avoids single points of failure; when a local device or communication malfunctions, the rest of the cluster can continue to work collaboratively, thus improving the overall reliability of the system.

[0133] 2. Improved dynamic response speed to meet the rapid adjustment needs of the power grid: By using a local decision-maker to perform parallel calculations based on local and neighborhood information, the processing of instructions at the central node and the multi-level distribution are eliminated, shortening the control loop. This enables the cluster to achieve millisecond to second-level response speeds, providing a technical foundation for participating in rapid services such as primary frequency regulation of the power grid.

[0134] 3. Achieved refined protection of users' personalized needs: The user's charging expectations are taken as one of the core optimization objectives and embedded in the local decision-making logic. This enables each V2G charging device to autonomously weigh its options based on real-time local information when responding to grid commands, ensuring that users' personalized charging needs are met and improving the user experience problems caused by the rigidity of centralized control strategies.

[0135] 4. Enhanced system scalability: The distributed architecture distributes computing and communication loads across the nodes in the cluster. As the cluster grows, each device only needs to collaborate with a limited number of neighbors, and its local computing and communication burden does not increase significantly, enabling better support for the access and application of large-scale V2G charging devices in the future.

[0136] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0137] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A V2G interactive control method, characterized in that, The method includes: The guidance module receives the adjustment command from the power grid and broadcasts the adjustment command to the V2G charging equipment cluster; wherein, the adjustment command includes a total power adjustment target; Each V2G charging device in the cluster has its own local decision-maker that monitors local information in real time and exchanges status information with the local decision-makers of neighboring V2G charging devices through neighborhood communication. Based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

2. The method according to claim 1, characterized in that: The local information includes grid frequency, voltage, vehicle real-time SOC, user-set minimum expected SOC, and departure time.

3. The method according to claim 2, characterized in that: The status information includes real-time charging / discharging power and the adjustable range of charging / discharging power.

4. The method according to claim 3, characterized in that, The local decision-maker obtains the optimal charging / discharging power through a multi-objective optimization model, the objective function of which is: in, The charging / discharging power of the i-th V2G charging device, The standardized power reference value comes from the cooperative algorithm. , , These are power deviation weight, SOC adaptation weight, and equipment loss weight, respectively. This is an adaptation function for the vehicle's current SOC and departure time. Let be the equipment loss function. This represents the objective function value.

5. The method according to claim 4, characterized in that, The distributed decision-making algorithm is a constrained consensus algorithm; the local decision-maker performs iterative calculations based on the exchanged real-time charging / discharging power, and in each iteration projects the calculation results onto its own adjustable charging / discharging power range dynamically determined by the local information, so as to generate the optimal charging / discharging power.

6. The method according to claim 5, characterized in that, The iterative process of the constrained consensus algorithm is defined by the following mathematical formula: in, The optimal charge / discharge power calculated after this iteration is... For projection operators, The adjustable range of its own charging / discharging power. Gather for the neighbors, These are the weighting coefficients. Real-time charging / discharging power of neighboring devices at the previous moment; in, Based on the vehicle's real-time SOC and departure time dynamic changes.

7. A V2G interactive control method, characterized in that, The method is executed by the local decision-maker corresponding to each V2G charging device in the V2G charging device cluster, including: Receive regulation instructions from the power grid, wherein the regulation instructions include a total power regulation target; It monitors local information in real time and exchanges status information with neighboring V2G charging devices through neighborhood communication; Based on the adjustment command, the local information, and the status information, the local decision-maker autonomously calculates the optimal charging / discharging power using a distributed decision-making algorithm to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

8. A V2G interactive control device, characterized in that, The system includes: The guidance module, which is a station controller or cloud platform, is used to receive the grid's regulation instructions and broadcast the regulation instructions to the V2G charging equipment cluster; the regulation instructions include the grid's total power regulation target; A V2G charging device cluster, wherein each V2G charging device in the cluster is configured with a local decision-maker, the local decision-maker including a sensing module, a communication module and a decision-making module; The sensing module is used to monitor local information in real time; The communication module is used to exchange status information with adjacent V2G charging devices through neighborhood communication. The decision module is used to autonomously calculate the optimal charging / discharging power based on the adjustment command, the local information, and the status information using a distributed decision algorithm, so as to simultaneously meet the total power adjustment target on the grid side, the charging expectation target on the user side, and the battery loss reduction target on the device side.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the V2G interactive control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the V2G interactive control method according to any one of claims 1 to 7.

Images