Day-ahead frequency modulation-oriented battery swap station and motorcade double-layer scheduling method and system

Abstract

The invention provides a day-ahead frequency modulation-oriented double-layer scheduling method and system for a battery swap station and a motorcade, and relates to the field of battery swap stations. The method comprises the following steps: based on a preprocessed power grid frequency modulation signal, electricity price and battery charging and discharging capacity, establishing a target function with maximized battery replacement service income and frequency modulation market income as targets, and constructing a day-ahead frequency modulation bidding and inventory planning model of a battery replacement station; the method comprises the following steps: modeling a road network into a directed graph based on a full-charge battery replacement scheduling scheme, and generating an augmented network flow graph of time, space and SOC; and constructing an automatic driving motorcade scheduling model by using an objective function with the objective of maximizing the income of an automatic driving motorcade operator. According to the invention, the coupling potential between the economic value of the battery swap station in the frequency modulation market and the fleet flexibility can be excavated.

Description

Technical Field

[0001] This invention relates to the field of battery swapping stations, and more specifically, to a two-tiered scheduling method and system for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation. Background Technology

[0002] With the large-scale integration of renewable energy and the rapid growth of electric vehicle ownership, the risk of frequency fluctuations in the power system is constantly increasing, placing higher demands on flexible adjustment resources with rapid charging and discharging capabilities. Battery swapping stations, which centrally deploy a large number of standardized power batteries, provide high-efficiency energy replenishment services while possessing power and capacity characteristics similar to large-scale energy storage stations. They are being explored for use in supporting ancillary services such as grid frequency regulation. Meanwhile, on-demand mobility services based on electric vehicles and autonomous driving fleets are beginning to be implemented in some scenarios. Operating platforms can centrally monitor and schedule the spatiotemporal distribution of fleets and battery state of charge, creating conditions for the collaborative operation of multiple entities within the "fleet-battery swapping station-grid" system.

[0003] Existing technology CN109149599A proposes a control method for electric vehicle fleets participating in grid frequency regulation. Through frequency deviation measurement and vehicle information collection, it divides the electric vehicles connected to the grid into a frequency regulation control domain, a forced charging control domain, and a charge / discharge uncontrollable domain. It generates frequency regulation power using the droop control principle and controls the charging and discharging of the vehicle fleet according to the battery state of charge, thus balancing grid frequency regulation capacity requirements and user driving needs to a certain extent. However, this method focuses on the centralized control of vehicles with decentralized charging access and does not consider the service orders and rebalancing needs of autonomous driving fleets in the road network. Existing technology CN118536777B also discloses a joint scheduling method for fleets and replenishment systems considering a dual-modality charging / battery swapping system. It uses a time-space-energy three-dimensional augmented graph to uniformly describe the path selection and charging / swapping behavior of the Modal Dashboard (MoD) fleet, and jointly optimizes the fleet and replenishment system with the goal of minimizing fleet operating costs and charging / swapping station operating costs. However, this scheme mainly focuses on the coordination of travel orders and replenishment costs, making it difficult to fully explore the coupling potential between the economic value of battery swapping stations in the frequency regulation market and fleet flexibility. While ensuring users' travel needs and the quality of battery swapping services, how to coordinate fleet scheduling, battery swapping station inventory, and participation in the power frequency regulation market to achieve synergistic optimization of frequency regulation revenue and travel operation revenue has become an urgent technical problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a two-layer scheduling method and system for battery swapping stations and fleets for day-ahead frequency regulation. It can model the centralized battery inventory and battery swapping business characteristics of battery swapping stations, consider the service orders and rebalancing needs of autonomous driving fleets in the road network, and introduce historical signals and frequency regulation pricing mechanisms from the power frequency regulation market. It constructs a two-layer optimization framework with maximizing the frequency regulation revenue of battery swapping stations as the upper-level objective and the operational efficiency of autonomous driving fleets as the lower-level objective, thereby exploring the coupling potential between the economic value of battery swapping stations in the frequency regulation market and the flexibility of the fleet.

[0005] The embodiments of the present invention are implemented as follows:

[0006] This application provides a two-tier scheduling method for battery swapping stations and vehicle fleets for day-ahead frequency regulation, which includes the following steps:

[0007] Preprocessing of grid frequency regulation signals, electricity prices, and battery charging and discharging capabilities;

[0008] Based on the preprocessed grid frequency regulation signal, electricity price and battery charging and discharging capacity, an objective function is established to maximize the revenue of battery swapping services and the revenue of the frequency regulation market, so as to construct a frequency regulation bidding and inventory planning model for the day-ahead of battery swapping stations for outputting a fully charged battery swapping scheduling scheme.

[0009] Based on the full-charge battery swapping scheduling scheme, the road network is modeled as a directed graph, generating an augmented network flow graph of time, space and SOC, wherein road arcs are set to describe the vehicle rescheduling process and swapping arcs are set to describe the vehicle battery swapping and energy replenishment process.

[0010] Based on the augmented network flow graph, an autonomous driving fleet scheduling model is constructed with the objective function of maximizing the revenue of autonomous driving fleet operators.

[0011] The aforementioned two-layer scheduling method for battery swapping stations and fleets oriented to day-ahead frequency regulation further includes: the day-ahead frequency regulation bidding model of the battery swapping station updates the fully charged battery swapping scheduling scheme based on the actual number of battery swapping vehicles of the autonomous driving fleet operator;

[0012] The autonomous driving fleet scheduling model generates a new autonomous driving fleet scheduling scheme based on the updated full-charge battery swapping scheduling scheme.

[0013] The day-ahead frequency regulation bidding model for the battery swapping station and the autonomous driving fleet scheduling model adopt an alternating iterative update strategy until the rate of change of frequency regulation revenue and fleet operation revenue both meet the preset convergence threshold.

[0014] The steps for preprocessing the power grid frequency modulation signal, electricity price, and battery charge / discharge capacity specifically include:

[0015] The hourly cumulative frequency modulation (FM) signal for each time period of the day is statistically analyzed and then divided into positive hourly cumulative FM signals that are FM-up and negative hourly cumulative FM signals that are FM-down.

[0016] The construction of the frequency regulation bidding and inventory planning model also includes: setting constraints on charging and discharging power and frequency regulation capacity, battery energy state equation constraints, battery swapping number constraints, upper and lower limits of battery capacity constraints, full-charged battery determination constraints, energy consistency constraints at the beginning and end of the scheduling cycle, full-charged battery swapping constraints, and full-charged battery quantity equation constraints.

[0017] The upper and lower limits of battery capacity constraints specifically include: by analyzing historical data of the PJM frequency modulation market, the maximum fluctuation coefficient of the upper and lower limits of battery capacity frequency modulation is obtained by fitting extreme value distribution.

[0018] The construction of the autonomous driving fleet scheduling model also includes setting constraints on road network traffic flow conservation, battery swapping demand, vehicle driving power and time, travel demand matching, and vehicle remaining power at the final moment.

[0019] The constraints for determining a fully charged battery specifically include: a battery is considered fully charged when its energy reaches 100% of its rated capacity, and a constraint equation is constructed by introducing extremely small positive numbers and integers.

[0020] The nodes of the augmented network flow graph of time, space, and SOC specifically include: road network location, discrete time, and battery state of charge; the road arc describes the vehicle rescheduling process, specifically including: the flow through the road arc when the vehicle travels through the current road segment is represented as 1, otherwise it is represented as 0; the battery swapping arc describes the vehicle battery swapping process, specifically including: the flow through the battery swapping arc when the vehicle completes battery swapping at the current station is represented as 1, otherwise it is represented as 0.

[0021] A two-tier dispatching system for battery swapping stations and vehicle fleets for day-ahead frequency regulation includes:

[0022] Signal preprocessing module: preprocesses grid frequency modulation signals, electricity prices, and battery charging and discharging capabilities;

[0023] Upper-level frequency regulation bidding module: Based on the preprocessed grid frequency regulation signal, electricity price and battery charging and discharging capacity, an objective function is established to maximize the revenue of battery swapping services and the revenue of the frequency regulation market, so as to construct a frequency regulation bidding and inventory planning model for the day-ahead of battery swapping stations for outputting a fully charged battery swapping scheduling scheme.

[0024] Road network modeling module: Based on the full-charge battery swapping scheduling scheme, the road network is modeled as a directed graph, generating an augmented network flow graph of time, space and SOC, wherein road arcs are set to describe the vehicle rescheduling process and swapping arcs are set to describe the vehicle battery swapping and energy replenishment process.

[0025] Lower-level fleet scheduling module: Based on the augmented network flow graph, an autonomous driving fleet scheduling model is constructed with the objective function of maximizing the revenue of autonomous driving fleet operators.

[0026] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0027] This application proposes a two-layer scheduling method for battery swapping stations and vehicle fleets for day-ahead frequency regulation, aiming to improve the economic efficiency of battery swapping stations and the revenue of autonomous driving fleet operators. Based on preprocessed grid frequency regulation signals, electricity prices, and battery charging / discharging capabilities, this application establishes an objective function for battery swapping stations aimed at maximizing the revenue from battery swapping services and the frequency regulation market. This leads to the construction of a day-ahead frequency regulation bidding and inventory planning model for battery swapping stations, enabling rational allocation of frequency regulation resources and inventory management. The application models the road network as a directed graph, generating augmented network flow graphs for time, space, and State of Charge (SOC). In this graph, road arcs describe the vehicle rescheduling process, and battery swapping arcs describe the vehicle battery swapping and replenishment process, accurately describing vehicle travel paths and battery swapping needs. Furthermore, aiming to maximize the revenue of autonomous driving fleet operators, this application constructs an autonomous driving fleet scheduling model that considers vehicle scheduling, battery swapping needs, and battery status to achieve efficient operation of autonomous driving fleets. This application can optimize the frequency regulation bidding strategy of battery swapping stations while ensuring vehicle operating efficiency, thereby improving the revenue of battery swapping services and the frequency regulation market. This application can improve the economic benefits of battery swapping stations and the operational efficiency of autonomous driving fleets, and achieve the rational allocation and utilization of resources. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of a two-layer scheduling method for battery swapping stations and vehicle fleets for day-ahead frequency regulation according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] Example

[0032] Please refer to Figure 1 , Figure 1 The following is a two-tiered scheduling method for battery swapping stations and vehicle fleets for day-ahead frequency regulation provided in this embodiment, which includes the following steps:

[0033] Preprocessing of grid frequency regulation signals, electricity prices, and battery charging and discharging capabilities;

[0034] Based on the preprocessed grid frequency regulation signal, electricity price and battery charging and discharging capacity, an objective function is established to maximize the revenue of battery swapping services and the revenue of the frequency regulation market, so as to construct a frequency regulation bidding and inventory planning model for the day-ahead of battery swapping stations for outputting a fully charged battery swapping scheduling scheme.

[0035] Based on the full-charge battery swapping scheduling scheme, the road network is modeled as a directed graph, generating an augmented network flow graph of time, space and SOC, wherein road arcs are set to describe the vehicle rescheduling process and swapping arcs are set to describe the vehicle battery swapping and energy replenishment process.

[0036] Based on the augmented network flow graph, an autonomous driving fleet scheduling model is constructed with the objective function of maximizing the revenue of autonomous driving fleet operators.

[0037] The aforementioned two-layer scheduling method for battery swapping stations and fleets oriented to day-ahead frequency regulation further includes: the day-ahead frequency regulation bidding model of the battery swapping station updates the fully charged battery swapping scheduling scheme based on the actual number of battery swapping vehicles of the autonomous driving fleet operator;

[0038] The autonomous driving fleet scheduling model generates a new autonomous driving fleet scheduling scheme based on the updated full-charge battery swapping scheduling scheme.

[0039] The day-ahead frequency regulation bidding model for the battery swapping station and the autonomous driving fleet scheduling model adopt an alternating iterative update strategy until the rate of change of frequency regulation revenue and fleet operation revenue both meet the preset convergence threshold.

[0040] The steps for preprocessing the power grid frequency modulation signal, electricity price, and battery charge / discharge capacity specifically include:

[0041] The hourly cumulative frequency modulation (FM) signal for each time period of the day is statistically analyzed and then divided into positive hourly cumulative FM signals that are FM-up and negative hourly cumulative FM signals that are FM-down.

[0042] The construction of the frequency regulation bidding and inventory planning model also includes: setting constraints on charging and discharging power and frequency regulation capacity, battery energy state equation constraints, battery swapping number constraints, upper and lower limits of battery capacity constraints, full-charged battery determination constraints, energy consistency constraints at the beginning and end of the scheduling cycle, full-charged battery swapping constraints, and full-charged battery quantity equation constraints.

[0043] The upper and lower limits of battery capacity constraints specifically include: by analyzing historical data of the PJM frequency modulation market, the maximum fluctuation coefficient of the upper and lower limits of battery capacity frequency modulation is obtained by fitting extreme value distribution.

[0044] The construction of the autonomous driving fleet scheduling model also includes setting constraints on road network traffic flow conservation, battery swapping demand, vehicle driving power and time, travel demand matching, and vehicle remaining power at the final moment.

[0045] The constraints for determining a fully charged battery specifically include: a battery is considered fully charged when its energy reaches 100% of its rated capacity, and a constraint equation is constructed by introducing extremely small positive numbers and integers.

[0046] The nodes of the augmented network flow graph of time, space, and SOC specifically include: road network location, discrete time, and battery state of charge; the road arc describes the vehicle rescheduling process, specifically including: the flow through the road arc when the vehicle travels through the current road segment is represented as 1, otherwise it is represented as 0; the battery swapping arc describes the vehicle battery swapping process, specifically including: the flow through the battery swapping arc when the vehicle completes battery swapping at the current station is represented as 1, otherwise it is represented as 0.

[0047] In practice, this invention constructs a two-layer hybrid integer programming model for the coordinated optimization of intelligent connected vehicle fleet scheduling and battery swapping station frequency regulation, while balancing vehicle battery swapping service and grid frequency regulation requirements. The aim is to maximize the revenue of both fleet operators and battery swapping station operators. The upper layer of the model combines hourly accumulated grid frequency regulation signals and battery charging / discharging capabilities to optimize the frequency regulation capacity configuration and fully charged battery reserves of battery swapping stations at different times. The lower layer constructs a "time-space-SOC" augmented network flow to uniformly model vehicle order services, empty vehicle scheduling, and battery swapping management, optimizing fleet scheduling and battery swapping requirements. Based on this, the invention employs an iterative solution strategy of alternating upper and lower layer updates to coordinate the solution of the constructed two-layer optimization model until frequency regulation revenue and vehicle operation indicators converge, obtaining a coordinated optimization scheme that balances frequency regulation service capabilities and travel service quality.

[0048] This invention proposes a two-layer scheduling method for battery swapping stations and fleets oriented towards day-ahead frequency regulation. It designs a vehicle-station collaborative day-ahead frequency regulation bidding strategy framework for battery swapping stations, with the upper layer being a day-ahead frequency regulation bidding and inventory planning model for battery swapping stations, and the lower layer being an autonomous driving fleet scheduling model.

[0049] Frequency regulation resources such as battery swapping stations need to participate in the bidding for frequency regulation auxiliary markets before the day to determine the standby capacity for each hour of the following day. In order to obtain the impact of frequency regulation on the hourly scale, all RegD signals within an hour are statistically analyzed to obtain the hourly cumulative regulation signals (HCRS).

[0050] The hourly cumulative frequency modulation signal is divided into positive and negative parts, corresponding to up-modulation and down-modulation. The up-modulation part indicates that the dispatch center hopes that frequency modulation resources can be used to help increase the system frequency; for battery swapping stations, this means reducing charging power or increasing discharging power. The down-modulation part has the opposite effect. Specifically, it can be represented as:

[0051] ;

[0052] ;

[0053] In the formula: Numbering the time periods within a 24-hour period of a day. Number the frequency modulation signal every 2 seconds within a time period. These are hourly cumulative frequency modulation signals with positive and negative values, respectively. For a moment The frequency modulation signal emitted by the power grid (a dimensionless number between -1 and 1), and , , The time within this period (1 hour) The set, The time interval (2s) between two adjacent frequency modulation signals.

[0054] In practice, the power grid dispatch center sends frequency modulation signals to frequency modulation resources such as battery swapping stations participating in frequency regulation. Each resource responds to the frequency modulation signal and adjusts its own power to maintain the system frequency near its rated value. The power adjustment amount is the product of the frequency modulation signal and the frequency modulation capacity, based on the grid's demand.

[0055] The objective function of the frequency regulation bidding and inventory planning model is to maximize the revenue from battery swapping services and the revenue from the frequency regulation market, while reducing the cost of electricity purchase. During hourly scheduling periods, the impact of battery degradation is not significant and would significantly increase the nonlinear complexity of the model; therefore, this part can be ignored. The objective function is shown below:

[0056] ;

[0057] In the formula: The total revenue of the battery swapping station; These are the battery swapping revenue, frequency regulation revenue, and electricity purchase cost of the battery swapping station, respectively.

[0058] The revenue from battery swapping stations can be generated by...

[0059]

[0060] Indicates the battery number within the battery swapping station; This represents the total number of time periods; This represents the total number of batteries in the battery swapping station. The price for battery swapping; This is the battery's maximum capacity; This indicates the initial charge level of the battery swapping vehicle; A binary variable representing a time period. Battery inside the station Whether to replace with the vehicle battery Indicates exchange, or vice versa. 0 indicates no swapping; This represents the penalty coefficient for failing to meet battery swapping requirements; This indicates the number of unmet battery swapping needs.

[0061] ;

[0062] It is the frequency regulation mileage of the battery swapping station; Price for frequency modulation capacity; Let n be the frequency regulation capacity of battery n in the battery swapping station during time period t; Price based on FM mileage; It is the mileage utilization rate of the battery swapping station.

[0063] ;

[0064] The electricity purchase price; For batteries inside the battery swapping station During the period The charging power; For batteries inside the battery swapping station During the period The discharge power.

[0065] Optionally, the constraints on battery charging and discharging capacity in the frequency regulation bidding and inventory planning model include constraints on the charging and discharging power of the battery swapping station, frequency regulation capacity, battery capacity, and the number of fully charged batteries.

[0066] Among them, charging and discharging power and and frequency modulation capacity The constraint can be expressed as:

[0067] ;

[0068] In the formula: , These are the battery's maximum charging and discharging power, respectively.

[0069] Among them, the energy state equation constraint of the battery can be limited by the battery's charging and discharging efficiency and the number of battery swapping vehicles.

[0070] The number of battery swapping vehicles can be expressed as:

[0071] ;

[0072] in, This indicates whether the vehicle battery with soc=c needs to be exchanged with the station battery n within time period t.

[0073] Initial charge of battery swapping vehicles It can be represented as:

[0074] .

[0075] The battery swapping constraint is the constraint between the actual number of batteries swapped in the battery swapping station and the number of vehicles coming to swap batteries. It can be expressed as the number of vehicles coming to swap batteries, which is passed from the lower-level AMoD autonomous driving fleet scheduling model to the upper level, is equal to the sum of the number of vehicles swapping batteries in operation and the actual number of batteries swapped in the battery swapping station.

[0076] The upper and lower limits of battery capacity constraints include: the range of battery energy within the battery swapping station is located between the sum of the battery's maximum capacity and the maximum upward fluctuation coefficient, and the sum of the battery's minimum capacity and the maximum downward fluctuation coefficient.

[0077] Frequency modulation (FM) command data from the PJM market over the past 3-5 years was collected, including real-time adjustment amounts, durations, fluctuation amplitudes, and cumulative adjustment depths for both upward and downward FM modulation, forming a large-capacity FM signal sample set. The FM signals were normalized to the battery's rated capacity to obtain the relative FM fluctuation per unit battery capacity. Abnormal jumps and atypical disturbances were removed to ensure the samples conformed to extreme value distribution characteristics. The upper and lower tails of the FM fluctuations were fitted using either the generalized extreme value distribution GEV or the generalized Pareto distribution GPD: upward FM corresponds to battery discharge, fitting the maximum discharge fluctuation; downward FM corresponds to battery charging, fitting the maximum charging fluctuation. Distribution parameters were determined through maximum likelihood estimation, yielding the probability density function and cumulative distribution function of the FM fluctuations. Under a preset confidence level, the maximum upward and downward FM fluctuation coefficients were obtained from the upper and lower quantiles of the extreme value distributions, respectively. The final selection was based on the maximum upward fluctuation coefficient. Maximum downward fluctuation coefficient .

[0078] The constraint for determining a fully charged battery is that a battery with 100% charge is considered a fully charged battery.

[0079] The battery swapping station must have the same energy level at the beginning and end of the scheduling cycle to ensure stable operation.

[0080] Battery swapping constraint for fully charged batteries ensures that swapping is only permitted for fully charged batteries.

[0081] The constraint of the equation for the number of fully charged batteries is passed from the upper layer to the lower layer autonomous driving fleet scheduling model. The number of fully charged batteries is constrained by whether the batteries in the battery swapping unit are fully charged.

[0082] Augmented network flow representation: The road network is modeled as a directed graph. ,in This represents a set of nodes. This represents an arc set. Arc sets are divided into two categories: road arcs and battery swapping arcs. To represent the time-varying characteristics and battery levels of the AMoD fleet, this paper extends the directed graph into an augmented network flow graph of time, space, and SoC, namely: a time-space-SoC graph. Specifically, the nodes in the augmented graph correspond to arrays... ,in It refers to the node location in the road network. It is discrete time. It indicates the battery's state of charge.

[0083] The vehicle rescheduling process is modeled as a road arc, characterized by changes in vehicle location over time and a decrease in battery power. The battery swapping process is modeled as a swapping arc, characterized by increases in vehicle power over time while its spatial location remains constant. Network flow is used to describe the vehicle's trajectory within the road network, where road arcs and swapping arcs together constitute the path-flow correspondence: if an arc (road or swapping arc) belongs to a path, its flow is 1; otherwise, it is 0. For each travel demand, the vehicle traverses several nodes and arcs in the road network during its journey; therefore, for any node... In general, the flow of vehicles entering and leaving should be consistent.

[0084] To construct a computationally efficient autonomous driving fleet scheduling model (AMoD fleet scheduling model), we make the following assumptions:

[0085] Assumption 1: Passenger travel demand is known and determined.

[0086] Assumption 2: There is only one battery swapping station in the transportation network, the battery swapping process takes a period of time, and the queuing process during battery swapping is not considered.

[0087] Assumption 3: In order to reduce the computational complexity of optimization, the passenger's travel route is predetermined, and no route optimization is performed on the passenger vehicles.

[0088] The objective function of the autonomous driving fleet scheduling model is to maximize the revenue of the AMoD operator, which is the revenue from passenger pick-up and drop-off services minus the cost of battery swapping. (Travel demand) From array To indicate, among which It is the starting point and the end point of travel demand. This refers to the departure time for travel needs. This refers to the number of travel requests.

[0089] To comprehensively characterize the operational characteristics of the AMoD fleet in battery swapping mode, the autonomous driving fleet scheduling model also introduces the following key constraints:

[0090] Traffic flow conservation constraints on the road network ensure that every node The balance between vehicle inflow and outflow is maintained to reflect the continuity of the transportation network: the sum of the number of empty vehicles arriving at the current node via road arcs or power line transfer arcs, the number of passenger travel demands destined for the current node, and the number of vehicles available for dispatch at the current node, equals the sum of the number of empty vehicles arriving at the next node via road arcs or power line transfer arcs, the number of passenger travel demands arriving at the next node, and the number of vehicles available for dispatch at the next node.

[0091] Battery swapping demand constraints are addressed by using the available battery resources at battery swapping stations to meet the battery swapping decisions of bound vehicles, ensuring that the battery swapping requests of the fleet are both realistic and feasible, and are coordinated with the battery inventory of the stations.

[0092] The constraints of battery power and time during vehicle travel are as follows: for passenger travel demand with known origin and destination, since the travel route is already determined, the battery power and time consumed during the journey are also determined. That is, the number of vehicles departing from the origin node is equal to the number of vehicles arriving at the destination node at the corresponding arrival time.

[0093] Travel demand matching constraint: The number of departing vehicles and the number of arriving vehicles are the same as the passenger travel demand.

[0094] The final moment's remaining battery power constraint requires that at the end of the optimization cycle, the vehicle's remaining battery power must not be lower than a preset threshold to ensure that the vehicle can meet future travel needs.

[0095] In summary, embodiments of the present invention provide a two-layer scheduling method and system for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation: Based on preprocessed grid frequency regulation signals, electricity prices, and battery charging and discharging capabilities, this application establishes an objective function for battery swapping stations aimed at maximizing the revenue from battery swapping services and the frequency regulation market, thereby constructing a day-ahead frequency regulation bidding and inventory planning model for battery swapping stations to achieve rational allocation of frequency regulation resources and inventory management; This application models the road network as a directed graph, generating augmented network flow graphs of time, space, and SOC; In this graph, road arcs are set to describe the vehicle rescheduling process, and battery swapping arcs are set to describe the vehicle battery swapping and replenishment process, which can accurately describe the vehicle's driving path and battery swapping demand; This application constructs an autonomous driving vehicle fleet scheduling model with the goal of maximizing the revenue of autonomous driving fleet operators. This model considers vehicle scheduling, battery swapping demand, and battery status to achieve efficient operation of autonomous driving fleets. This application can optimize the frequency regulation bidding strategy of battery swapping stations while ensuring vehicle operating efficiency, thereby improving the revenue of battery swapping services and the frequency regulation market. This application can improve the economic benefits of battery swapping stations and the operational efficiency of autonomous driving fleets, and achieve the rational allocation and utilization of resources.

[0096] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A two-layer scheduling method for battery swapping stations and vehicle fleets for day-ahead frequency regulation, characterized in that, Includes the following steps: Preprocessing of grid frequency regulation signals, electricity prices, and battery charging and discharging capabilities; Based on the preprocessed grid frequency regulation signal, electricity price and battery charging and discharging capacity, an objective function is established to maximize the revenue of battery swapping services and the revenue of the frequency regulation market, so as to construct a frequency regulation bidding and inventory planning model for the day-ahead of battery swapping stations for outputting a fully charged battery swapping scheduling scheme. Based on the full-charge battery swapping scheduling scheme, the road network is modeled as a directed graph, generating an augmented network flow graph of time, space and SOC, wherein road arcs are set to describe the vehicle rescheduling process and swapping arcs are set to describe the vehicle battery swapping and energy replenishment process. Based on the augmented network flow graph, an autonomous driving fleet scheduling model is constructed with the objective function of maximizing the revenue of autonomous driving fleet operators.

2. The two-layer scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation according to claim 1, characterized in that, Also includes: The frequency modulation bidding and inventory planning model updates the fully charged battery swapping scheduling scheme based on the actual number of battery swapping vehicles of the autonomous driving fleet operator. The autonomous driving fleet scheduling model generates a new autonomous driving fleet scheduling scheme based on the updated full-charge battery swapping scheduling scheme. The frequency modulation bidding and inventory planning model and the autonomous driving fleet scheduling model adopt an alternating iterative update strategy until the rate of change of frequency modulation revenue and fleet operation revenue both meet the preset convergence threshold.

3. The two-layer scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation according to claim 1, characterized in that, The steps for preprocessing the power grid frequency modulation signal, electricity price, and battery charge / discharge capacity specifically include: The hourly cumulative frequency modulation (FM) signal for each time period of the day is statistically analyzed and then divided into positive hourly cumulative FM signals that are FM-up and negative hourly cumulative FM signals that are FM-down.

4. The two-layer scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation according to claim 1, characterized in that, The construction of the frequency regulation bidding and inventory planning model also includes: setting constraints on charging and discharging power and frequency regulation capacity, battery energy state equation constraints, battery swapping number constraints, upper and lower limits of battery capacity constraints, full-charged battery determination constraints, energy consistency constraints at the beginning and end of the scheduling cycle, full-charged battery swapping constraints, and full-charged battery quantity equation constraints.

5. A two-tiered scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation, as described in claim 4, is characterized in that... The upper and lower limits of battery capacity constraints specifically include: by analyzing historical data of the PJM frequency modulation market, the maximum fluctuation coefficient of the upper and lower limits of battery capacity frequency modulation is obtained by fitting extreme value distribution.

6. A two-tiered scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation, as described in claim 1, is characterized in that... The construction of the autonomous driving fleet scheduling model also includes setting constraints on road network traffic flow conservation, battery swapping demand, vehicle driving power and time, travel demand matching, and vehicle remaining power at the final moment.

7. A two-tiered dispatching method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation, as described in claim 4, is characterized in that... The constraints for determining a fully charged battery specifically include: a battery is considered fully charged when its energy reaches 100% of its rated capacity, and a constraint equation is constructed by introducing extremely small positive numbers and integers.

8. A two-tiered scheduling method for battery swapping stations and vehicle fleets oriented towards day-ahead frequency regulation, as described in claim 1, is characterized in that... The nodes of the augmented network flow graph of time, space and SOC specifically include: road network location, discrete time, and battery state of charge; The road arc describes the vehicle rescheduling process, specifically including: the flow rate through the road arc when a vehicle travels through the current road segment is represented as 1, otherwise it is represented as 0; The setting of the battery swapping arc describes the vehicle's battery swapping and energy replenishment process, specifically including: when the vehicle completes the battery swap at the current station, the flow rate through the battery swapping arc is represented as 1, otherwise it is represented as 0.

9. A two-tier dispatching system for battery swapping stations and vehicle fleets for day-ahead frequency regulation, characterized in that, include: Signal preprocessing module: preprocesses grid frequency modulation signals, electricity prices, and battery charging and discharging capabilities; Upper-level frequency regulation bidding module: Based on the preprocessed grid frequency regulation signal, electricity price and battery charging and discharging capacity, an objective function is established to maximize the revenue of battery swapping services and the revenue of the frequency regulation market, so as to construct a frequency regulation bidding and inventory planning model for the day-ahead of battery swapping stations for outputting a fully charged battery swapping scheduling scheme. Road network modeling module: Based on the full-charge battery swapping scheduling scheme, the road network is modeled as a directed graph, generating an augmented network flow graph of time, space and SOC, wherein road arcs are set to describe the vehicle rescheduling process and swapping arcs are set to describe the vehicle battery swapping and energy replenishment process. Lower-level fleet scheduling module: Based on the augmented network flow graph, an autonomous driving fleet scheduling model is constructed with the objective function of maximizing the revenue of autonomous driving fleet operators.

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