Fleet participation in demand response collaborative optimization decision-making methods, devices, equipment and media

Abstract

This invention discloses a method, apparatus, equipment, and medium for collaborative optimization decision-making involving a fleet of vehicles in demand response. The method involves: receiving V2G demand response event signals from a grid operator, including demand power, response time period, and real-time electricity price; secondly, acquiring in real-time the location, battery state of charge (SoC), and task status of each autonomous electric vehicle in the fleet, as well as the location information of V2G charging stations; then, using a resource-task network (RTN) model to uniformly model vehicle resources, charging station resources, passenger service resources, and grid demand resources, constructing all potential task sequences, and quantifying resource consumption and task benefits; next, with the objective of maximizing the total profit of the fleet, performing optimization solutions under conditions such as battery SoC boundaries, charging station power constraints, and minimum grid response power, generating task sequences, path planning, and charging / discharging plans for each vehicle; finally, distributing the optimization decision results to the fleet for automatic execution.

Description

Technical Field

[0001] This invention relates to the field of smart grid and electric vehicle collaborative optimization technology, and in particular to a method, apparatus, equipment and medium for fleet participation in demand response collaborative optimization decision-making. Background Technology

[0002] New power systems with a high proportion of renewable energy integration face severe challenges such as continuously decreasing rotational inertia and insufficient peak-shaving and frequency regulation capabilities. The severe shortage of flexible grid adjustment resources has become a key bottleneck restricting the absorption of renewable energy and the safe and stable operation of the power system. Against this backdrop, there is an urgent need to explore distributed flexible resources with rapid response characteristics and large adjustable capacity.

[0003] Electric vehicles, especially commercially operated autonomous electric vehicle fleets, are considered ideal resources for demand response due to their advantages such as scalability and high dispatchability. Enabling electric vehicle fleets to participate in grid interaction through V2G (Vehicle-to-Grid) technology can not only effectively improve grid flexibility but also reduce the investment cost of building new energy storage facilities, demonstrating significant economic benefits. However, existing technologies face multiple challenges in practical applications: First, commercially operated fleets of autonomous electric vehicles possess the dual attributes of "transportation service" and "electricity service," with these two services exhibiting complex coupling relationships across time and space. Traditional scheduling methods often treat these two aspects separately, failing to achieve synergistic optimization and resulting in low fleet resource utilization, making it difficult to fully realize their demand response potential.

[0004] Secondly, existing scheduling strategies are not precise enough in quantifying battery degradation costs. Frequent charge and discharge operations accelerate the degradation of battery state of health (SOH), while existing models often ignore the coupled effects of multiple factors such as depth of discharge, charge / discharge rate, and temperature, leading to inaccurate battery degradation cost assessments and affecting the economic decisions of fleets participating in V2G services.

[0005] Furthermore, there is a lack of reasonable compensation mechanisms when fleets disrupt passenger journeys in response to grid demands. Existing methods are insufficient in quantifying passenger inconvenience and struggle to balance the relationship between grid service revenue and passenger service quality, thus limiting the feasibility of large-scale fleet participation in V2G services.

[0006] Finally, existing optimization methods often suffer from high modeling complexity and low solution efficiency when dealing with complex scheduling problems involving multiple vehicles, multiple tasks, and multiple constraints, making it difficult to meet the real-time scheduling requirements of commercially operated fleets. Summary of the Invention

[0007] Based on this, it is necessary to propose a method, device, equipment, and medium for fleet participation in demand response collaborative optimization decision-making to address the above problems.

[0008] A method for collaborative optimization decision-making involving fleet participation in demand response, the method comprising: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

[0009] Preferably, the method further includes: if the optimization decision requires interrupting the vehicle performing the passenger-carrying task, determining and providing compensation to the affected passengers, the compensation being calculated based on the passengers' expected total delay time and remaining distance, and meeting a preset minimum compensation price limit.

[0010] Preferably, receiving the V2G demand response event containing grid power demand released by the grid operator specifically includes: The parsing representation is The V2G demand response event released by the power grid; among them, The start time of the event. The end time of the event. DR power request, duration is ; The real-time electricity price Dynamically adjusted based on the duration of the demand response event: ;in, Based on the benchmark V2G electricity price, This is the V2G price coefficient.

[0011] Preferably, obtaining the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet specifically includes: updating the real-time location and speed of the vehicles through a kinematic update model. ;in, For vehicles in The acceleration control input at any given moment, The vehicle's current speed. This represents the mapping of the vehicle's current location within the road network. Simultaneously determined based on battery dynamics model ;in, These are the charge and discharge efficiencies, respectively. This refers to the charging / discharging power. This refers to the battery's rated energy. For time step; And define the vehicle's current task state from arrive Evolutionary rules: ;in, Assign passenger pick-up and drop-off tasks to dispatch orders; This is a charging instruction, requiring the vehicle to proceed to a charging station for charging; This is a V2G service instruction that requires vehicles to participate in grid interaction; The vehicle remains in standby mode as indicated by the idle command; and the number of available charging stations. .

[0012] Preferably, in the Resource-Task Network (RTN) model, V2G revenue Travel revenue Battery loss cost ,in, For battery purchase costs; This refers to the residual value of the battery. Overall battery health; This refers to the amount of SOH decay caused by a single V2G discharge. Compensation costs ,in, The minimum compensation amount is preset; the objective function is to maximize total profit. ,in, It is the team's total profit; At a certain point in time Revenue generated from V2G services, which is based on the real-time electricity price of V2G services; It is the revenue from passenger travel services; This represents the operating costs for that period, including vehicle depreciation and other routine vehicle costs. It is the monetized cost of battery degradation caused by charge-discharge cycles, which is calculated using a health state model; This is the compensation fee that needs to be paid for passengers whose journeys are interrupted due to V2G tasks; This is the demand response period.

[0013] Preferably, the step of selecting the optimal sequence from all potential task sequences to maximize the total profit of the fleet, and solving the problem under all constraints such as the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid, to obtain the optimized decision result, specifically includes: using the objective function max ω At its core, apply battery SoC boundaries Charging and discharging power limits Total power constraint of charging station ,in and the minimum response power constraint of the power grid It outputs the optimal task sequence, path planning, and charge / discharge plan through mathematical programming.

[0014] Preferably, the step of identifying and generating a set of vehicles that meet grid demand and are most profitable based on the optimization decision results, and determining the optimal task sequence and path planning for each vehicle within the response time period, specifically includes: the optimal task sequence for each vehicle within the response time period is determined through state transition rules. It is determined that path planning optimizes the trajectory using kinematic equations, and the charge / discharge plan ensures optimal energy management through SoC dynamic equations; where, in the formula: for This means that the current battery level is sufficient to perform the takeover task. This is the minimum battery level required to start carrying passengers. This refers to the electricity consumption from the time the order is received until the passenger boards the vehicle. For the existence of available charging stations and , Indicates the maximum battery capacity; V2G service point reachable and , This indicates the minimum charge required to participate in V2G discharge.

[0015] A vehicle fleet participation demand response collaborative optimization decision-making device, the device comprising: The response module is used to receive V2G demand response events released by the grid operator, which include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); The parsing module is used to obtain the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet when the V2G demand response event is received; at the same time, it obtains the location, total number of charging piles, and number of available V2G charging piles of each V2G charging station in the city. The resource-task network model construction module is used to define the SoC and current location of each autonomous electric vehicle as vehicle energy resources and location resources, the available charging piles of the V2G charging station as infrastructure resources, the current passenger order as passenger service resources, and the power grid demand as external constraint resources. Through the resource-task network (RTN) model, it constructs a set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle, and quantifies the resource consumption and task benefits corresponding to each task sequence. The decision module is used to select the optimal sequence from all potential task sequences to maximize the total profit of the fleet. It solves the problem under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the power grid, to obtain the optimized decision results. The optimized decision results include the task sequence, path planning, and charging and discharging plan for each vehicle. The determination module is used to identify and generate a set of vehicles that meet the grid demand and are most profitable based on the optimization decision results, and to determine the optimal task sequence and path planning for each vehicle during the response period. The distribution module is used to distribute the optimal task sequence, path planning, and charging / discharging plan to the autonomous electric vehicle fleet for execution.

[0016] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

[0017] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

[0018] The embodiments of the present invention have the following beneficial effects: This invention, through a Resource-Task Network (RTN) framework, achieves for the first time intelligent coordination and unified scheduling of autonomous electric vehicle fleets between traffic services and grid V2G response. At the grid level, it effectively enhances the flexible adjustment capabilities of the new power system, significantly improving peak-shaving and frequency regulation reliability under high-proportion renewable energy access by utilizing the fleet as a distributed flexibility resource. In terms of economic benefits, this method aims to maximize the total profit of the fleet, comprehensively considering multiple factors such as V2G service revenue, trip revenue, operating costs, battery depreciation costs, and trip interruption compensation. It not only optimizes the overall revenue of the fleet but also protects passenger rights through a reasonable compensation mechanism. Simultaneously, this method extends battery life and reduces total lifecycle costs through refined management of battery health. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] in: Figure 1 This invention provides a flowchart of a method for collaborative optimization decision-making involving a fleet in demand response. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] This invention provides a method for collaborative optimization decision-making involving fleet participation in demand response, such as... Figure 1 As shown, the method includes: Step 101: Receive V2G demand response events containing grid power demand released by the grid operator; Specifically, the power demand of the power grid includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); The parsing representation is The V2G demand response event released by the power grid; among them, The start time of the event. The end time of the event. DR power request, duration is ; The real-time electricity price Dynamically adjusted based on the duration of the demand response event: ;in, Based on the benchmark V2G electricity price, This is the V2G price coefficient.

[0023] Step 102: Upon receiving the V2G demand response event, obtain the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; simultaneously obtain the location, total number of charging piles, and number of available V2G charging piles of each V2G charging station in the city; Specifically, the vehicle's real-time position and velocity are updated through a kinematic update model. ;in, For vehicles in The acceleration control input at any given moment, The vehicle's current speed. This represents the mapping of the vehicle's current location within the road network. Simultaneously determined based on battery dynamics model ;in, These are the charge and discharge efficiencies, respectively. This refers to the charging / discharging power. This refers to the battery's rated energy. For time step.

[0024] And define the vehicle's current task state from arrive Evolutionary rules: ;in, Assign passenger pick-up and drop-off tasks to dispatch orders; This is a charging instruction, requiring the vehicle to proceed to a charging station for charging; This is a V2G service instruction that requires vehicles to participate in grid interaction; The vehicle remains in standby mode as indicated by the idle command; and the number of available charging stations. .

[0025] Step 103: Define the SoC and current location of each autonomous electric vehicle as vehicle energy resources and location resources, define the available charging piles of the V2G charging station as infrastructure resources, define the current passenger order as passenger service resources, and define the power grid demand as external constraint resources. Construct a set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle through the resource-task network (RTN) model, and quantify the resource consumption and task benefits corresponding to each task sequence. Specifically, in the resource-task network (RTN) model, V2G revenue... Travel revenue Battery loss cost ,in, For battery purchase costs; This refers to the residual value of the battery. Overall battery health; This refers to the amount of SOH decay caused by a single V2G discharge. Compensation costs ,in, The minimum compensation amount is preset; the objective function is to maximize total profit. ,in, It is the team's total profit; At a certain point in time Revenue generated from V2G services, which is based on the real-time electricity price of V2G services; It is the revenue from passenger travel services; This represents the operating costs for that period, including vehicle depreciation and other routine vehicle costs. It is the monetized cost of battery degradation caused by charge-discharge cycles, which is calculated using a health state model; This is the compensation fee that needs to be paid for passengers whose journeys are interrupted due to V2G tasks; This is the demand response period.

[0026] Step 104: Select the optimal sequence from all potential task sequences to maximize the total profit of the fleet. Solve the problem under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid, to obtain the optimization decision results. The optimization decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Specifically, with the objective function max ω At its core, apply battery SoC boundaries Charging and discharging power limits Total power constraint of charging station ,in and the minimum response power constraint of the power grid It outputs the optimal task sequence, path planning, and charge / discharge plan through mathematical programming.

[0027] Step 105: Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; Specifically, the optimal task sequence for each vehicle within the response time period is determined by state transition rules. It is determined that path planning optimizes the trajectory using kinematic equations, and the charge / discharge plan ensures optimal energy management through SoC dynamic equations; where, in the formula: for This means that the current battery level is sufficient to perform the takeover task. This is the minimum battery level required to start carrying passengers. This refers to the electricity consumption from the time the order is received until the passenger boards the vehicle. For the existence of available charging stations and , Indicates the maximum battery capacity; V2G service point reachable and , This indicates the minimum charge required to participate in V2G discharge.

[0028] Step 106: Distribute the optimal task sequence, path planning, and charging / discharging plan to the autonomous electric vehicle fleet for execution.

[0029] Furthermore, the method also includes: if the optimization decision requires interrupting a vehicle performing a passenger-carrying task, determining and providing compensation to the affected passengers, the compensation being calculated based on the passengers' expected total delay time and remaining distance, and meeting a preset minimum compensation price limit.

[0030] This invention, through a Resource-Task Network (RTN) framework, achieves for the first time intelligent coordination and unified scheduling of autonomous electric vehicle fleets between traffic services and grid V2G response. At the grid level, it effectively enhances the flexible adjustment capabilities of the new power system, significantly improving peak-shaving and frequency regulation reliability under high-proportion renewable energy access by utilizing the fleet as a distributed flexibility resource. In terms of economic benefits, this method aims to maximize the total profit of the fleet, comprehensively considering multiple factors such as V2G service revenue, trip revenue, operating costs, battery depreciation costs, and trip interruption compensation. It not only optimizes the overall revenue of the fleet but also protects passenger rights through a reasonable compensation mechanism. Simultaneously, this method extends battery life and reduces total lifecycle costs through refined management of battery health.

[0031] This invention also provides a fleet participation demand response collaborative optimization decision-making device, the device comprising: The response module is used to receive V2G demand response events released by the grid operator, which include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); The parsing module is used to obtain the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet when the V2G demand response event is received; at the same time, it obtains the location, total number of charging piles, and number of available V2G charging piles of each V2G charging station in the city. The resource-task network model construction module is used to define the SoC and current location of each autonomous electric vehicle as vehicle energy resources and location resources, the available charging piles of the V2G charging station as infrastructure resources, the current passenger order as passenger service resources, and the power grid demand as external constraint resources. Through the resource-task network (RTN) model, it constructs a set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle, and quantifies the resource consumption and task benefits corresponding to each task sequence. The decision module is used to select the optimal sequence from all potential task sequences to maximize the total profit of the fleet. It solves the problem under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the power grid, to obtain the optimized decision results. The optimized decision results include the task sequence, path planning, and charging and discharging plan for each vehicle. The determination module is used to identify and generate a set of vehicles that meet the grid demand and are most profitable based on the optimization decision results, and to determine the optimal task sequence and path planning for each vehicle during the response period. The distribution module is used to distribute the optimal task sequence, path planning, and charging / discharging plan to the autonomous electric vehicle fleet for execution.

[0032] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

[0033] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

[0034] 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 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, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0035] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0036] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for collaborative optimization decision-making involving fleet participation in demand response, characterized in that, The method includes: Receive V2G demand response events from grid operators that include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); Upon receiving the V2G demand response event, the system acquires the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet; it also acquires the location, total number of charging piles, and number of available V2G charging piles for each V2G charging station in the city. The SoC and current location of each autonomous electric vehicle are defined as vehicle energy resources and location resources, the available charging piles of the V2G charging station are defined as infrastructure resources, the current passenger order is defined as passenger service resources, and the power grid demand is defined as external constraint resources. A set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle is constructed through the resource-task network (RTN) model, and the resource consumption and task benefits corresponding to each task sequence are quantified. The optimal sequence is selected from all potential task sequences to maximize the total profit of the fleet. The solution is obtained under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimal decision results include the task sequence, path planning, and charging / discharging plan for each vehicle. Based on the optimization decision results, identify and generate a set of vehicles that meet the grid demand and are most profitable, and determine the optimal task sequence and path planning for each vehicle during the response period; The optimal task sequence, path planning, and charging / discharging plan are then distributed to the autonomous electric vehicle fleet for execution.

2. The fleet participation demand response collaborative optimization decision-making method according to claim 1, characterized in that, The method further includes: If the optimization decision requires interrupting vehicles performing passenger transport tasks, compensation is determined and provided to affected passengers. The compensation is calculated based on the passengers' expected total delay time and remaining distance, and meets a preset minimum compensation price limit.

3. The fleet participation demand response collaborative optimization decision-making method according to claim 1 or 2, characterized in that, The receipt of V2G demand response events containing grid power demand from grid operators specifically includes: The parsing representation is The V2G demand response event released by the power grid; among them, The start time of the event. The end time of the event. DR power request, duration is ; The real-time electricity price Dynamically adjusted based on the duration of the demand response event: ;in, Based on the benchmark V2G electricity price, This is the V2G price coefficient.

4. The fleet participation demand response collaborative optimization decision-making method according to claim 3, characterized in that, The acquisition of the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet specifically includes: updating the real-time location and speed of the vehicles through a kinematic update model. ;in, For vehicles in The acceleration control input at any given moment, The vehicle's current speed. This represents the mapping of the vehicle's current location within the road network. Simultaneously determined based on battery dynamics model ;in, These are the charge and discharge efficiencies, respectively. This refers to the charging / discharging power. This refers to the battery's rated energy. The time step is defined; and the current task state of the vehicle is defined from... arrive Evolutionary rules: ;in, Assign passenger pick-up and drop-off tasks to dispatch orders; This is a charging instruction, requiring the vehicle to proceed to a charging station for charging; This is a V2G service instruction that requires vehicles to participate in grid interaction; The vehicle remains in standby mode as indicated by the idle command; and the number of available charging stations. .

5. The fleet participation demand response collaborative optimization decision-making method according to claim 4, characterized in that, In the resource-task network (RTN) model, V2G revenue Travel revenue Battery loss cost ,in, For battery purchase costs; This refers to the residual value of the battery. Overall battery health; This refers to the amount of SOH decay caused by a single V2G discharge. Compensation costs ,in, The minimum compensation amount is preset; the objective function is to maximize total profit. ,in, It is the team's total profit; At a certain point in time Revenue generated from V2G services, which is based on the real-time electricity price of V2G services; It is the revenue from passenger travel services; This represents the operating costs for that period, including vehicle depreciation and other routine vehicle costs. It is the monetized cost of battery degradation caused by charge-discharge cycles, which is calculated using a health state model; This is the compensation fee that needs to be paid for passengers whose journeys are interrupted due to V2G tasks; This is the demand response period.

6. The fleet participation demand response collaborative optimization decision-making method according to claim 5, characterized in that, The process involves selecting the optimal sequence from all potential task sequences to maximize the total profit of the fleet. This is achieved by solving the problem under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the grid. The optimization decision result is obtained by using the objective function max... ω At its core, apply battery SoC boundaries Charging and discharging power limits Total power constraint of charging station ,in and the minimum response power constraint of the power grid It outputs the optimal task sequence, path planning, and charge / discharge plan through mathematical programming.

7. The fleet participation demand response collaborative optimization decision-making method according to claim 6, characterized in that, The step of identifying and generating a set of vehicles that meet grid demand and maximize profits based on the optimization decision results, and determining the optimal task sequence and path planning for each vehicle within the response time period, specifically includes: the optimal task sequence for each vehicle within the response time period is determined through state transition rules. It is determined that path planning optimizes the trajectory using kinematic equations, and the charge / discharge plan ensures optimal energy management through SoC dynamic equations; where, in the formula: for This means that the current battery level is sufficient to perform the takeover task. This is the minimum battery level required to start carrying passengers. This refers to the electricity consumption from the time the order is received until the passenger boards the vehicle. For the existence of available charging stations and , Indicates the maximum battery capacity; V2G service point reachable and , This indicates the minimum charge required to participate in V2G discharge.

8. A vehicle fleet participation demand response collaborative optimization decision-making device, characterized in that, The device includes: The response module is used to receive V2G demand response events released by the grid operator, which include grid power demand; the grid power demand includes the demand power P. DR Response time period ΔT and real-time electricity price p V2G (t); The parsing module is used to obtain the real-time location, battery state of charge (SoC), and current task status of each autonomous electric vehicle in the fleet when the V2G demand response event is received; at the same time, it obtains the location, total number of charging piles, and number of available V2G charging piles of each V2G charging station in the city. The resource-task network model construction module is used to define the SoC and current location of each autonomous electric vehicle as vehicle energy resources and location resources, the available charging piles of the V2G charging station as infrastructure resources, the current passenger order as passenger service resources, and the power grid demand as external constraint resources. Through the resource-task network (RTN) model, it constructs a set of all potential task sequences for each autonomous electric vehicle to perform traffic services and power services within the optimized scheduling cycle, and quantifies the resource consumption and task benefits corresponding to each task sequence. The decision module is used to select the optimal sequence from all potential task sequences to maximize the total profit of the fleet. It solves the problem under all constraints, including the battery state of charge (SoC) boundary, the total power constraint of the charging station, and the minimum response power required by the power grid, to obtain the optimized decision results. The optimized decision results include the task sequence, path planning, and charging and discharging plan for each vehicle. The determination module is used to identify and generate a set of vehicles that meet the grid demand and are most profitable based on the optimization decision results, and to determine the optimal task sequence and path planning for each vehicle during the response period. The distribution module is used to distribute the optimal task sequence, path planning, and charging / discharging plan to the autonomous electric vehicle fleet for execution.

9. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

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