A dynamic priority charging and discharging scheduling method and system based on community V2X vehicle-network interaction

Abstract

The application provides a dynamic priority charging and discharging scheduling method and system based on community V2X vehicle network interaction, obtains vehicle battery state, travel regularity, community load and power grid state data through V2X communication, jointly evaluates vehicle dynamic priority and scheduling time window, and forms power cell division based on load demand and vehicle adaptability. Then, the conflict is resolved by combining the deterministic travel plan, an emergency adjustment set is generated, power optimization distribution is completed within the power grid safety boundary, and a scheduling instruction is output. Thus, the aggregation, evaluation, grading and optimized scheduling of community electric vehicle energy storage resources are realized, the electric vehicle cluster is converted into a controllable distributed energy storage system under the premise of guaranteeing the travel demand of the vehicle owner, which not only supports the power grid and stabilizes the fluctuation, but also creates benefits.

Description

Technical Field

[0001] This invention relates to the field of smart grids, specifically to a dynamic priority charging and discharging scheduling method and system based on community V2X vehicle-to-grid interaction. Background Technology

[0002] With the booming development of the new energy vehicle industry and the continuous advancement of smart community construction, community power grids are facing increasing electricity load pressure, especially during peak electricity consumption periods such as "summer peak season," when many communities experience load alarms or even overload operation. On the other hand, the large number of electric vehicles in communities, due to their large battery capacity and long parking time, can theoretically serve as high-quality distributed energy storage resources. They can charge and store energy during off-peak hours and discharge to the community during peak hours, thereby alleviating grid pressure. This vehicle-to-grid (V2G / V2X) interaction model is considered one of the key technological paths to improve grid flexibility and promote the consumption of new energy, and its application demand is becoming increasingly urgent.

[0003] Currently, existing community vehicle charging and discharging management technologies mainly focus on unidirectional orderly charging or adopt relatively simple V2G scheduling strategies. These strategies are mostly demand-centric, such as issuing charging or discharging commands uniformly within a set time period, with the core objective of peak shaving and valley filling. Although some solutions consider electricity price signals and guide user behavior by setting price incentives, they are essentially still a crude, one-dimensional scheduling model based on macro policies or electricity price mechanisms.

[0004] However, existing technologies present significant technical challenges: First, the utilization rate of energy storage resources in community electric vehicles is low, and effective evaluation and aggregation models have not been established to accurately convert dispersed vehicle batteries into reliable energy storage capacity. Second, disordered charging and discharging may affect grid security, and simple dispatching commands do not consider the real-time status of the community distribution network (such as node voltage and frequency fluctuations), and a large number of vehicles responding to commands simultaneously may exacerbate grid disturbances. Most importantly, there is a lack of refined dispatching capabilities that take into account both the travel needs of car owners and the regulation needs of the grid. Existing solutions cannot deeply integrate the real-time status of vehicles (SOC / SOH), the travel patterns of car owners, and high-priority deterministic travel plans, thus making it difficult to achieve safe, reliable, and efficient vehicle-grid interaction while ensuring users' core travel rights. Summary of the Invention

[0005] In view of the above-mentioned actual situation, this application proposes a dynamic priority charging and discharging scheduling method and system based on community V2X vehicle-to-grid interaction to solve the problems of low utilization rate of community electric vehicle energy storage resources, disorderly charging and discharging that may affect grid security, and lack of fine-grained scheduling capabilities that take into account both the travel needs of car owners and the regulation needs of the grid in the existing technology.

[0006] A dynamic priority charging and discharging scheduling method based on community V2X vehicle-to-grid interaction, the method comprising the following steps:

[0007] S1. Obtain data to be processed through the V2X communication network. The data to be processed includes real-time battery status data of all vehicles in the community, vehicle owner travel pattern data, community load demand data, vehicle confirmed trip data, and community power grid status data. The real-time battery status data includes the vehicle's remaining charge (SOC) and battery health (SOH). The community load demand data includes basic load demand and adjustable load demand. The community power grid status data includes power grid frequency fluctuations and node voltage deviations.

[0008] S2, perform joint scheduling evaluation processing on the real-time battery status data of the vehicle and the travel pattern data of the vehicle owner to obtain the vehicle dynamic score set and its corresponding scheduling time window data. The joint scheduling evaluation processing is based on the collaborative calculation of battery status data and travel time patterns obtained through probability mapping to generate scheduling priorities and time windows.

[0009] S3, the vehicle dynamic rating set, scheduling time window data and community load demand data are processed by resource pooling to obtain the power pool partition set. The resource pooling process is based on determining the energy storage capacity allocation parameters according to load demand and optimizing the allocation by combining vehicle rating and time window matching degree.

[0010] S4, perform trip conflict resolution processing on the power pool partition set, scheduling time window data and vehicle confirmed trip data to obtain an emergency adjustment set. The trip conflict resolution processing is based on deterministic trip plan and predictive scheduling time window to identify conflict status and perform high-priority coverage adjustment.

[0011] S5, perform grid adaptability optimization processing on the emergency adjustment set and community power grid status data to obtain a dispatch instruction set. The grid adaptability optimization processing is based on determining the safety boundary based on the real-time operating status of the power grid and optimizing the allocation of charging and discharging power within the boundary.

[0012] Furthermore, step S2 includes the following sub-steps:

[0013] S201, Based on the vehicle owner's travel pattern data and real-time time, calculate the departure time matching degree to obtain vehicle dispatch available window data. The departure time matching degree calculation is performed by mapping the current time with the probability distribution of the vehicle's historical departure time and outputting the vehicle's dispatch safety duration at the current moment.

[0014] S202, the real-time battery status data of the vehicle and the available window data for vehicle scheduling are dynamically scored and the scheduling window is calculated to obtain the vehicle dynamic score set and scheduling time window data. The dynamic scoring and scheduling window calculation adopts a linear weighting method to combine SOC, SOH and scheduling safety duration to obtain a real-time priority score, and generates a suggested scheduling period based on the score and available window.

[0015] Furthermore, step S3 includes the following sub-steps:

[0016] S301, Based on the community load demand data, solve the constraints of guaranteed energy storage capacity and market-based energy storage capacity through a linear programming model to generate energy storage capacity allocation parameters;

[0017] S302, the vehicle dynamic score set, scheduling time window data and energy storage capacity ratio parameters are processed for vehicle allocation to obtain a power pool partition set. The vehicle allocation process is based on the score ranking and scheduling time window matching degree, and a weighted round-robin scheduling algorithm is used to allocate vehicles to the guaranteed energy storage pool and the market-based energy storage pool.

[0018] Furthermore, step S4 includes the following sub-steps:

[0019] S401, perform trip conflict detection on the vehicle confirmed trip data and scheduling time window data to obtain trip conflict flag data. The trip conflict detection is to compare the reported trip time with the scheduling time window to generate a binary conflict status flag.

[0020] S402, perform high-priority overlay processing on the power pool partition set and trip conflict flag data to obtain an emergency adjustment set. The high-priority overlay processing is to remove the occupied vehicle resources from the scheduling pool based on the conflict flags.

[0021] Furthermore, step S5 includes the following sub-steps:

[0022] S501, perform safety boundary calculation on the community power grid status data to obtain power grid safety operation domain data; the safety boundary calculation is to convert node voltage deviation and power grid frequency fluctuation into linear inequality constraints with respect to node net power by linearizing the power grid model;

[0023] S502, power optimization processing is performed on the emergency adjustment set and the power grid safe operation domain data to obtain a scheduling instruction set. The power optimization processing optimizes the charging and discharging power allocation within the safe operation domain through a quadratic programming algorithm.

[0024] Furthermore, the departure time matching degree calculation in S201 is achieved by mapping the current time with the probability distribution of the vehicle's historical departure time, and outputting the vehicle's scheduling safety duration at the current moment, including probability accumulation processing and safety duration calculation processing; the dynamic scoring and scheduling window calculation in S202 is achieved by using a linear weighting method to integrate SOC, SOH and scheduling safety duration to obtain a real-time priority score, and generating a suggested scheduling period based on the score and available window, including score calculation processing and scheduling window generation processing.

[0025] Furthermore, the step S301, which involves solving the constraints of guaranteed energy storage capacity and market-based energy storage capacity using a linear programming model based on community load demand data, includes load demand decomposition and capacity constraint optimization. The step S302, which involves allocating vehicles to guaranteed energy storage pools and market-based energy storage pools using a weighted round-robin scheduling algorithm based on scoring, ranking, and scheduling time window matching, includes vehicle ranking and capacity weighted allocation.

[0026] Furthermore, in S401, the trip conflict detection involves comparing the reported trip time with the scheduling time window to generate a binary conflict status identifier, including time window intersection judgment processing and conflict flag generation processing; in S402, the high priority coverage processing involves removing the occupied vehicle resources from the scheduling pool based on the conflict flag, including conflict vehicle identification processing and scheduling pool update processing.

[0027] Furthermore, the safety boundary calculation in S501 is to convert node voltage deviation and grid frequency fluctuation into linear inequality constraints with respect to node net power by linearizing the grid model, including grid model linearization processing and safety constraint transformation processing; the power optimization processing in S502 is to optimize the charging and discharging power allocation in the safe operating domain by using a quadratic programming algorithm, including optimization target construction processing and constraint integration solution processing.

[0028] Furthermore, this application also discloses a dynamic priority charging and discharging scheduling system based on community V2X vehicle-to-grid interaction, characterized in that the system includes:

[0029] The acquisition unit is used to acquire data to be processed through a V2X communication network. This data includes real-time battery status data of all vehicles within the community, vehicle owner travel pattern data, community load demand data, confirmed vehicle trip data, and community power grid status data. The real-time battery status data includes the vehicle's remaining charge (SOC) and battery health (SOH). The community load demand data includes basic load demand and adjustable load demand. The confirmed vehicle trip data consists of highly certain recent travel plans proactively reported by vehicle owners. The community power grid status data includes power grid frequency fluctuations and node voltage deviations.

[0030] The joint evaluation unit is used to perform joint scheduling evaluation processing on the real-time battery status data of the vehicle and the travel pattern data of the vehicle owner, so as to obtain the vehicle dynamic score set and its corresponding scheduling time window data. The joint scheduling evaluation processing is based on the collaborative calculation of battery status data and travel time patterns obtained through probability mapping to generate scheduling priorities and time windows.

[0031] The resource pooling unit is used to perform resource pooling processing on the vehicle dynamic score set, scheduling time window data and community load demand data to obtain a power pool partition set. The resource pooling processing is based on determining the energy storage capacity allocation parameters according to load demand and optimizing the allocation by combining vehicle score and time window matching degree.

[0032] The conflict resolution unit is used to process the power pool partition set, scheduling time window data and vehicle confirmed trip data to resolve trip conflicts, thereby obtaining an emergency adjustment set. The trip conflict resolution process is based on deterministic trip plans and predictive scheduling time windows to identify conflict states and perform high-priority coverage adjustments.

[0033] The power grid optimization unit is used to perform power grid adaptive optimization processing on the emergency adjustment set and community power grid status data to obtain a dispatch instruction set. The power grid adaptive optimization processing determines the safety boundary based on the real-time operating status of the power grid and optimizes the allocation of charging and discharging power within the boundary.

[0034] The proposed method and system for dynamic priority charging and discharging scheduling based on community V2X vehicle-to-grid interaction realizes the aggregation, evaluation, classification and optimized scheduling of community electric vehicle energy storage resources. Under the premise of absolutely guaranteeing the travel needs of car owners, the electric vehicle cluster is transformed into a controllable community distributed energy storage system, which can support the community power grid and smooth load fluctuations during peak electricity consumption periods, and can also participate in market regulation to generate revenue. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow for a dynamic priority charging and discharging scheduling method based on community V2X vehicle-to-grid interaction proposed in this application;

[0036] Figure 2 A schematic diagram of a dynamic priority charging and discharging scheduling system based on community V2X vehicle-to-grid interaction is provided for embodiments of this application; Detailed Implementation

[0037] The simulation technology route in 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.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] The features and performance of the present invention will be further described in detail below with reference to embodiments. Please refer to the appendix. Figure 1 As shown, a dynamic priority charging and discharging scheduling method based on community V2X vehicle-to-grid interaction is proposed, the method comprising the following steps:

[0040] S1. Acquire data to be processed through the V2X communication network. The data to be processed includes real-time battery status data of all vehicles in the community, vehicle owner travel pattern data, community load demand data, vehicle confirmed trip data, and community power grid status data. The real-time battery status data includes the vehicle's remaining charge (SOC) and battery health (SOH). The vehicle owner travel pattern data is a daily departure time probability distribution based on long-term statistics. The community load demand data includes basic load demand and adjustable load demand. The vehicle confirmed trip data consists of recent travel plans actively reported by vehicle owners with high certainty. The community power grid status data includes power grid frequency fluctuations and node voltage deviations.

[0041] In some implementations, the V2X communication network uses the C-V2X technology standard to realize the wireless transmission link between the vehicle and the cloud platform, and establishes a real-time data exchange channel between the vehicle and the community dispatch center. The data to be processed constitutes the input data set of the dispatch decision system, including real-time battery status data of all vehicles in the community, vehicle owner travel pattern data, community load demand data, vehicle confirmed trip data, and community power grid status data. These data are aggregated to the central dispatch platform through different collection terminals and communication protocols, forming a foundation for multi-source heterogeneous data fusion.

[0042] In some implementations, the real-time battery status data is collected via the vehicle's CAN bus interface and the onboard T-Box communication module, and uploaded to the cloud platform at a sampling frequency of once per second. This data includes two parameters: the vehicle's remaining charge (SOC) and battery health (SOH). It should be noted that SOC reflects the current energy state of the battery, with a value ranging from 0% to 100%; SOH characterizes the degree of battery aging, and its value is calculated based on changes in battery internal resistance and the number of charge-discharge cycles. These parameters provide a fundamental assessment basis for the vehicle's energy status in subsequent scheduling evaluations. In some implementations, the vehicle owner's travel pattern data is generated using big data analytics, employing machine learning modeling based on vehicle travel records from the past six months. Specifically, a Gaussian mixture model is used to cluster daily departure times, generating a travel time distribution model expressed as a probability density function. The output of this model is the probability of departure for each vehicle at any given time, providing a probabilistic prediction basis for subsequent scheduling time window calculations. In some implementations, the community load demand data originates from a smart meter data collection system and a load forecasting system. This data comprises two components: basic load demand and adjustable load demand. Basic load demand refers to the essential electricity load for residents, predicted through time series analysis. Adjustable load demand refers to flexible loads that can participate in demand response. These two types of load data together constitute the community's total electricity demand curve. In some implementations, the vehicle trip confirmation data is collected through the input interface of the vehicle owner's APP, using digital signature technology to ensure data authenticity and immutability. This data includes three fields: planned travel time, expected return time, and minimum required electricity consumption. These data have the highest priority and are treated as hard constraints in scheduling decisions, ensuring that vehicle owners' travel needs are absolutely guaranteed. In some implementations, the community power grid status data is collected through a distribution automation system, including power grid frequency fluctuations and node voltage deviations. Frequency fluctuation data is measured using a PMU device at a sampling frequency of 50Hz, and voltage deviation data is collected by smart meters at intervals of once per minute. These real-time power grid parameters provide dynamic boundary conditions for subsequent calculations of the power grid's safe operation domain, ensuring that charging and discharging scheduling does not cause power grid safety issues.

[0043] In this embodiment, all collected data is encapsulated and transmitted using the IoT data standard format. The data packets include timestamps, device IDs, and data verification codes. The V2X communication network employs an end-to-end encryption mechanism to ensure data transmission security, while using data redundancy transmission technology to ensure communication reliability. These technical measures together guarantee the integrity, accuracy, and timeliness of the collected data.

[0044] S2, perform joint scheduling evaluation processing on the real-time battery status data of the vehicle and the travel pattern data of the vehicle owner to obtain the vehicle dynamic score set and its corresponding scheduling time window data. The joint scheduling evaluation processing is based on the collaborative calculation of battery status data and travel time patterns obtained through probability mapping to generate scheduling priorities and time windows.

[0045] Specifically, this step includes the following sub-steps:

[0046] S201, Based on the vehicle owner's travel pattern data and real-time time, calculate the departure time matching degree to obtain vehicle dispatch available window data. The departure time matching degree calculation is performed by mapping the current time with the probability distribution of the vehicle's historical departure time and outputting the vehicle's dispatch safety duration at the current moment.

[0047] In some implementations, the departure time matching degree calculation is performed by probabilistically mapping the current time with the probability distribution of the vehicle's historical departure time, and outputting the scheduling safety duration of the vehicle at the current moment, which includes probability accumulation processing and safety duration calculation processing. The probability accumulation processing is to perform an integral operation on the probability distribution function of the vehicle's historical departure time starting from the current moment, and the safety duration calculation processing determines the maximum schedulable duration based on a preset probability threshold.

[0048] In this embodiment, the probability accumulation process uses mathematical integration to calculate the probability from the current time. The cumulative probability that a vehicle will not leave the field initially; specifically, the probability density function of the historical departure times for a given vehicle. Its time interval Cumulative retention probability within Represented as: ,in The exit time probability density function is obtained through kernel density estimation.

[0049] Dispatch safety duration Defined as satisfying The maximum time interval, i.e.: This calculation ensures that the probability of a vehicle being dispatched within the dispatch time window is kept within an acceptable range.

[0050] Furthermore, the available window data for vehicle scheduling is represented in the form of a time interval, defined as starting from the current time. By the deadline A continuous time period; it is expressed as: ,in This indicates the effective time window in which a vehicle can participate in scheduling; this time window data provides time constraints for subsequent dynamic scoring calculations and resource pooling processing.

[0051] Preferably, the probability threshold An adaptive adjustment mechanism is adopted to dynamically adjust the vehicle's historical travel patterns based on their reliability. For vehicles with sufficient historical data and stable travel patterns, a higher probability threshold is set; for vehicles with insufficient historical data and scattered travel patterns, a lower probability threshold is set. This adaptive mechanism improves the schedulability of vehicle resources while ensuring scheduling security.

[0052] In some implementations, the departure time matching degree calculation also includes a real-time correction mechanism. When the vehicle owner actively reports confirmed trip data, the confirmed trip time is directly overwritten with the probability prediction result. This correction mechanism ensures that the dispatching system prioritizes trip information with high certainty, thereby improving the accuracy of dispatching arrangements.

[0053] The scheduling safety duration As a vehicle dispatchability indicator, it, together with the vehicle's real-time battery status data, constitutes the input parameter for subsequent dynamic scoring calculations. This parameter directly determines the time range within which a vehicle can participate in dispatching, providing time-dimensional constraints for subsequent battery allocation.

[0054] S202, the real-time battery status data of the vehicle and the available window data for vehicle scheduling are dynamically scored and the scheduling window is calculated to obtain the vehicle dynamic score set and scheduling time window data. The dynamic scoring and scheduling window calculation is to use a linear weighting method to combine SOC, SOH and scheduling safety duration to obtain a real-time priority score, and generate a suggested scheduling period based on the score and available window.

[0055] In some implementations, the dynamic scoring and scheduling window calculation uses a linear weighted method to combine SOC, SOH, and scheduling safety duration to obtain a real-time priority score, and generates suggested scheduling periods based on the score and available windows. This includes score calculation processing and scheduling window generation processing. The score calculation processing uses a weighted summation model to fuse multi-dimensional evaluation indicators to generate a scalarized priority score. The scheduling window generation processing prioritizes and divides available scheduling windows based on this score to generate specific scheduling period suggestions.

[0056] In this embodiment, the scoring calculation process uses a linear weighted model to comprehensively evaluate three core parameters; specifically, the vehicle real-time priority score. The calculation formula is expressed as: Where SOC is the current remaining battery percentage of the vehicle, and SOH is the battery health percentage. The scheduling safety duration calculated in step S201, The maximum possible scheduling duration set for the system; These are the weighting coefficients for each parameter, satisfying... The constraints are as follows. This score aims to quantify the real-time dispatch value of a single vehicle from two dimensions: battery status and availability.

[0057] It should be noted that the core of the scheduling window generation process is based on scoring. To The defined available window is internally divided; its purpose is to provide more granular timing guidance for subsequent resource pooling processing, rather than a final decision. In one implementation, the available window is divided into time periods of length Δt, and these time periods are assigned a priority label based on their rating. : ,in and The preset scoring threshold. The suggested scheduling period. Therefore, it can be represented as a set of time intervals with priority labels: .

[0058] Preferably, the weighting coefficient The value can be preset according to the global strategy; for example, when focusing on energy throughput, it can be increased. The weight of (SOC) is increased when the focus is on scheduling reliability. ( The weights are set independently of the real-time load demand of the community power grid, and their output is only a priority score and time period characterizing the vehicle's own characteristics. In this embodiment, the vehicle dynamic score set is indeed the set of priority scores for all vehicles in the community, represented as... , where n is the total number of vehicles. This represents the rating of the i-th vehicle. The scheduling time window data is based on the available window for each vehicle. and its rating This generates a set of time intervals with priority labels. Its mathematical expression is: ,in From the vehicle availability window A sub-period divided into the period, This is the priority label assigned to this sub-time period, and this priority... The vehicle's rating Decide.

[0059] Furthermore, the vehicle dynamic score set and scheduling time window data constitute the direct input for subsequent S3 resource pooling processing; this set of data provides a unified, quantitative value ranking of all available vehicle resources in the community that includes time dimension information, so that the next step can, based on this, combine community load demand data to perform optimized allocation of guaranteed and market-based energy storage capacity.

[0060] S3, the vehicle dynamic rating set, scheduling time window data and community load demand data are processed by resource pooling to obtain the power pool partition set. The resource pooling process is based on determining the energy storage capacity allocation parameters according to load demand and optimizing the allocation by combining vehicle rating and time window matching degree.

[0061] Specifically, this step includes the following sub-steps:

[0062] S301, Based on the community load demand data, solve the constraints of guaranteed energy storage capacity and market-based energy storage capacity through a linear programming model to generate energy storage capacity allocation parameters;

[0063] In some implementations, the step of solving for the guaranteed energy storage capacity constraints and market-based energy storage capacity constraints using a linear programming model based on community load demand data includes load demand decomposition processing and capacity constraint optimization processing. The load demand decomposition processing separates the total load demand into a guaranteed component and a market-regulated component, while the capacity constraint optimization processing uses mathematical programming methods to solve for the minimum energy storage capacity configuration that satisfies both types of demand.

[0064] In some embodiments, the load demand decomposition process is based on community load demand data. Specifically, the demand for guaranteed energy storage capacity. Based on basic load demand The fluctuation characteristics are determined, which is expressed as meeting a certain confidence level. Maximum load fluctuation: Market demand for energy storage capacity Then it is determined by adjustable load demand. The integral area is determined and expressed as: , where T is a complete scheduling cycle.

[0065] In some implementations, the capacity constraint optimization process uses a linear programming model to solve for the optimal energy storage capacity ratio; decision variables are defined. and These represent the proportions of energy storage capacity allocated to guaranteed functions and market-oriented functions, respectively, to meet [the following needs]. The optimization objective is to minimize the total capacity configuration cost. ,in and These are the unit capacity guarantee cost and the market revenue coefficient, respectively.

[0066] Furthermore, the optimization problem needs to satisfy two types of constraints; the guaranteed capacity constraint ensures that the basic load demand is met: Market-based capacity constraints ensure the dispatching needs of adjustable loads: ,in Aggregate the total capacity of electric vehicles in the community.

[0067] Preferably, the energy storage capacity ratio parameters Represented in binary form: This parameter clearly specifies the ratio of total energy storage capacity used for guaranteed functions and market-oriented functions, providing a basis for capacity allocation in subsequent vehicle distribution and processing.

[0068] In some implementations, the linear programming model also includes capacity redundancy constraints, requiring that the guaranteed capacity configuration reserve a certain amount of capacity. Redundancy of proportions: ,in With a value between 0.1 and 0.2, this constraint ensures that the system has the ability to buffer against sudden load changes.

[0069] The energy storage capacity ratio parameters Together with the vehicle dynamic score set and scheduling time window data, it forms the input for subsequent vehicle allocation processing. Through a weighted round-robin scheduling algorithm, vehicle resources are allocated to the guaranteed energy storage pool and the market-based energy storage pool to achieve the optimal allocation of community energy resources.

[0070] S302, the vehicle dynamic score set, scheduling time window data and energy storage capacity ratio parameters are processed for vehicle allocation to obtain the power pool partition set. The vehicle allocation process is based on the score ranking and scheduling time window matching degree, and a weighted round-robin scheduling algorithm is used to allocate vehicles to the guaranteed energy storage pool and the market-based energy storage pool.

[0071] In some implementations, the vehicle allocation process involves allocating vehicles to both guaranteed energy storage pools and market-based energy storage pools using a weighted round-robin scheduling algorithm based on score ranking and scheduling time window matching. This includes vehicle ranking and capacity-weighted allocation. The vehicle ranking process is based on a comprehensive value ranking of vehicles using multi-dimensional evaluation indicators, while the capacity-weighted allocation process allocates vehicles to different functional pools according to the energy storage capacity ratio parameters.

[0072] In this embodiment, the vehicle sorting process calculates a comprehensive evaluation value for each vehicle. Specifically, the comprehensive evaluation value is composed of the vehicle's dynamic score. Matching degree with scheduling time window The decision is made jointly, and its calculation formula is expressed as follows: ,in These are the weighting coefficients, and Scheduling time window matching degree This is obtained by calculating the overlap between the available vehicle scheduling time window and the expected scheduling period: ,in This represents the suggested scheduling period for the i-th vehicle, calculated and output by step S202. This data is a set of time intervals. This represents the target scheduling period that the system has preset and that is desired to be used for charging and discharging. This period usually corresponds to the peak-valley cycle of the power grid or the electricity market trading period. The operator indicates the total length of the time interval to be calculated. All vehicles are evaluated based on the comprehensive assessment value. Sorting from highest to lowest to form an orderly queue of vehicles. .

[0073] In some implementations, the capacity-weighted allocation process is based on energy storage capacity ratio parameters. Determine the allocation ratio; define the target capacity of the backup energy storage pool as follows: The target capacity for market-based energy storage pools is: ,in This indicates the target capacity that the backup energy storage pool needs to achieve. Indicates the energy storage capacity ratio parameter The capacity ratio coefficient in the calculation is a dimensionless scalar. This parameter is derived from the linear programming model optimization in step S301. This represents the total available battery capacity of all electric vehicles participating in the dispatch within the community. This indicates the target capacity that market-based energy storage pools need to achieve. Indicates the energy storage capacity ratio parameter The market-based capacity ratio coefficient is a dimensionless scalar. This parameter is also derived from the optimization in step S301.

[0074] Furthermore, a weighted round-robin scheduling algorithm is used for vehicle allocation; vehicles are selected sequentially from the ordered queue Q, and allocation weights are calculated based on the current capacity gaps of each pool; for the i-th vehicle, the weight allocated to the backup energy storage pool is: The weights allocated to market-based energy storage pools are: The final ownership of a vehicle is determined using a random round-robin method based on weighted proportions. This represents the weight of the i-th vehicle being assigned to the energy storage pool (the larger the value, the higher the probability that the vehicle will be assigned to the pool). This indicates the weight of the i-th vehicle in the market-based energy storage pool (the larger the value, the higher the probability that the vehicle will be allocated to the pool). This indicates the target total capacity of the backup energy storage pool. This indicates the target total capacity of market-based energy storage pools. This represents the total capacity that the backup energy storage pool has accumulated and allocated before the allocation of the i-th vehicle begins. This represents the total capacity that has been accumulated and allocated by the market-based energy storage pool before the allocation of the i-th vehicle begins. It is a function that takes the maximum value, ensuring that the weight calculation result is not negative.

[0075] In some implementations, the weight and This is used to generate a probability distribution for each vehicle to be assigned in the weighted round-robin scheduling algorithm, thereby determining which energy storage pool it will be assigned to; the weight value dynamically reflects the gap ratio between the current capacity and the target capacity of each energy storage pool, and is the core mechanism for achieving accurate capacity allocation.

[0076] In this embodiment, when the algorithm processes the vehicles in the sorted queue Q in order, for the current vehicle v i Calculate the probability that it will be allocated to the backup energy storage pool. The probability of market-based energy storage : , Subsequently, the algorithm performs a random sampling based on this probability distribution to determine the vehicle v. i The final allocation. This stochastic process ensures that the allocation meets the capacity ratio requirements on a macroscopic level, while introducing a certain degree of flexibility on a microscopic level.

[0077] It should be noted that the weight calculation is dynamically updated; the aforementioned and These represent the total accumulated capacity of the guaranteed energy storage pool and the market-based energy storage pool before the current vehicle is allocated. The current accumulated capacity of the corresponding pool is updated immediately after each successful vehicle allocation. , C i For vehicle v i The available energy storage capacity. This real-time update mechanism enables weighting. and It changes dynamically as the allocation process proceeds.

[0078] Furthermore, the dynamic weighting mechanism ensures that the macro-allocation result accurately approximates the target ratio; if a pool's capacity is about to be filled, its weight will approach zero, meaning that the probability of subsequent vehicles being allocated to that pool will also approach zero, thus automatically guiding more vehicles to another pool with a larger capacity gap. Preferably, to avoid division-by-zero errors and ensure algorithm convergence, when the current capacity of a pool reaches or exceeds its target capacity, its weight is forcibly set to zero; for example, if Then set directly and This means that the vehicle will inevitably be allocated to a market-based energy storage pool.

[0079] In this embodiment, the power pool partition set is represented in binary form: ,in This refers to the collection of vehicles allocated to the backup energy storage pool. This refers to the set of vehicles allocated to the market-based energy storage pool. It should be noted that the guaranteed energy storage pool consists of vehicles with high dispatch priority scores, long availability windows, and high determinism. Its core task is to provide highly reliable energy storage services to ensure the basic load needs of the community. The vehicles in this pool are considered near-reliable distributed energy storage units. The dispatch system can issue discharge commands (feeding power to the grid) or charging commands (absorbing excess power from the grid) to them based on grid demand without excessive concern about the risks of sudden grid disconnection. The market-based energy storage pool consists of the remaining vehicles. Its core task is to participate in economic dispatching such as peak shaving and frequency regulation. The vehicles in this pool participate in charging and discharging based on their dynamic scores and real-time grid electricity price signals, creating revenue for vehicle owners or the community. Its dispatch commands can be dynamically adjusted according to changes in vehicle availability. In this embodiment, the dispatching system generates a total power command based on the grid status (such as excessive load requiring discharge support, or frequency fluctuations requiring discharge stabilization). Then, according to the division of the two power pools, the total power command is decomposed and allocated to each vehicle within the pool. Finally, specific control commands, including discharge power, voltage, and duration, are issued to the on-board energy management system of the designated vehicle via the V2X communication network. Upon receiving the commands, the vehicle performs grid-connected discharge operations through its bidirectional charger. Therefore, the division of power pools is fundamental to achieving refined management and differentiated application of vehicle charging and discharging resources.

[0080] In some implementations, the allocation process also considers the compatibility of vehicle scheduling time windows; specifically, when the matching degree between the vehicle time window and the target scheduling period is lower than a threshold... Even if the overall assessment value is high, it will not be allocated to the backup energy storage pool; this mechanism ensures the reliability of vehicle dispatching in the backup energy storage pool.

[0081] In this embodiment, the power pool is divided into sets. Together with scheduling time window data and vehicle confirmed trip data, these data form the input for subsequent trip conflict resolution processing, providing a clustering basis for generating executable scheduling instructions. This division ensures that community energy storage resources are functionally differentiated according to predetermined capacity ratios, while also taking into account the scheduling value and time availability of individual vehicles.

[0082] S4, perform trip conflict resolution processing on the power pool partition set, scheduling time window data and vehicle confirmed trip data to obtain an emergency adjustment set. The trip conflict resolution processing is based on deterministic trip plan and predictive scheduling time window to identify conflict status and perform high-priority coverage adjustment.

[0083] Specifically, this step includes the following sub-steps:

[0084] S401, perform trip conflict detection on the vehicle confirmed trip data and scheduling time window data to obtain trip conflict flag data. The trip conflict detection is to compare the reported trip time with the scheduling time window to generate a binary conflict status flag.

[0085] In some implementations, the trip conflict detection involves comparing the reported trip time with the scheduling time window to generate a binary conflict status identifier, including time window intersection judgment processing and conflict flag generation processing. The time window intersection judgment processing detects whether the reported trip time overlaps with the planned scheduling period through mathematical set operations, and the conflict flag generation processing generates a binary identifier representing the conflict status based on the intersection judgment result.

[0086] In this embodiment, the time window intersection judgment process is performed on each vehicle in the battery partition set; for vehicle v i Its vehicle confirmed trip data includes the planned departure time range. The scheduling time window data includes suggested scheduling periods. Conflict detection requires judging each scheduling sub-segment individually. With the planned travel time Does there exist any temporal overlap, that is, does the two time periods intersect?

[0087] It should be noted that the conflict flag generation process generates the final conflict state flag based on the intersection judgment result; specifically, vehicle v i Itinerary conflict symbol F i Defined as follows: when any scheduling sub-segment overlaps with the planned trip time, the flag is set to 1 to indicate a conflict; otherwise, the flag is set to 0 to indicate no conflict.

[0088] Furthermore, the trip conflict flag data is represented as a set of conflict states for all vehicles: Where n is the total number of vehicles in the community. This represents the conflict state of the i-th vehicle.

[0089] Preferably, the conflict detection also considers a time buffer mechanism; specifically, a protection interval is set before and after the travel time. The actual testing time range is This mechanism avoids potential conflicts caused by time synchronization errors or trip preparation time. The protection interval duration is determined based on the operation time required for disconnecting and connecting the vehicle charging station.

[0090] In some implementations, a more stringent conflict detection standard is adopted for vehicles in guaranteed energy storage pools; even if there is a conflict in only one scheduling sub-period, it is immediately marked as a conflict state; for vehicles in market-based energy storage pools, a proportional threshold mechanism can be adopted, and a conflict state is only marked when the proportion of conflict periods exceeds a predetermined threshold.

[0091] The trip conflict flag data F, together with the power pool partition set, constitutes the input for subsequent high-priority coverage processing, providing the scheduling system with conflict vehicle identifiers and ensuring that the scheduling plan does not affect the vehicle owner's deterministic travel needs.

[0092] S402, perform high-priority overlay processing on the power pool partition set and trip conflict flag data to obtain an emergency adjustment set. The high-priority overlay processing is to remove the occupied vehicle resources from the scheduling pool based on the conflict flags.

[0093] In some implementations, the high-priority coverage process involves removing occupied vehicle resources from the scheduling pool based on conflict flags, including conflict vehicle identification processing and scheduling pool update processing. The conflict vehicle identification processing filters out the set of vehicles with trip conflicts based on binary conflict status flags, and the scheduling pool update processing removes conflict vehicles from the original power pool partition set to form an updated scheduling resource set.

[0094] In this embodiment, the conflict vehicle identification process involves traversing the trip conflict marker data.

[0095] To identify all vehicles involved in a conflict; specifically, to define the set of conflicting vehicles. This set includes all vehicles whose scheduling time windows overlap with the trips reported by car owners; these vehicles will be excluded from the final scheduling scope.

[0096] In some implementations, the scheduling pool update process is based on the original power pool partition set. and conflict vehicle collection Perform the following: For both guaranteed energy storage pools and market-based energy storage pools, perform set difference operations to generate updated pool member sets: It indicates that all those who were originally in And not in The composition of the vehicles, It indicates that all those who were originally in And not in Regarding the composition of vehicles in the data, it should be noted that the set difference operation... The definition is: the set of all elements that belong to set A but not to set B. This operation ensures that all vehicle resources with trip conflicts are completely removed from the two energy storage pools.

[0097] Furthermore, the emergency adjustment set It adopts the same binary tuple structure as the original power pool partition set, but contains the set of vehicles after conflict resolution: This dataset represents the set of vehicle resources that can be safely accessed by the scheduling system under the premise of absolutely guaranteeing the travel needs of car owners.

[0098] Preferably, the processing procedure also includes a capacity recalculation mechanism; after removing conflicting vehicles, the total available capacity of the two energy storage pools is recalculated. Specifically, the total available capacity of the guaranteed energy storage pool is obtained by summing the available energy storage capacity of each remaining vehicle in the pool, and the total available capacity of the market-based energy storage pool is obtained by the same summation method. These two capacity values ​​represent the scale of energy storage resources that can actually be called upon after conflict resolution.

[0099] It should be noted that the "high priority" in the high priority coverage has a specific meaning. It does not refer to a priority comparison between two energy storage pools, but rather that the confirmed trip data actively reported by the vehicle owner has the highest and indisputable priority over the scheduling time window predicted by the system based on historical patterns. Specifically, when the system detects that the scheduling time window of a vehicle (regardless of whether the vehicle belongs to the guaranteed pool or the market-based pool) conflicts with the planned travel time reported by the vehicle owner, no matter how important the scheduling arrangement is to the power grid or how much economic benefit it can bring, the system will unconditionally perform the coverage operation to remove the vehicle from its scheduling pool, so as to ensure that the vehicle owner's travel needs are not affected in any way. For example, suppose an electric vehicle is allocated to a guaranteed energy storage pool, with its scheduling window from 14:00 to 16:00 today. However, the owner reports a planned departure time of 15:00 via the app at 10:00 that day. After the system detects this conflict in step S401, it will remove the vehicle from the guaranteed energy storage pool in step S402, even if this may temporarily reduce the pool's capacity. The same logic applies to vehicles in market-based energy storage pools, which reflects the core principle of "user travel takes precedence over grid scheduling".

[0100] In some implementations, when the capacity loss of the backup energy storage pool exceeds a predetermined threshold, an inter-pool resource compensation mechanism is triggered; several vehicles with the highest scores from the market-based energy storage pools are temporarily allocated to the backup energy storage pool to ensure the integrity of the basic load support capacity. This compensation operation follows the principle of minimizing capacity gaps.

[0101] The emergency adjustment set Together with community power grid status data, it forms the input for subsequent power grid adaptive optimization processing, providing a conflict-verified set of vehicle resources for the final generation of safe and reliable dispatch instructions; this processing ensures that the dispatch system always prioritizes the vehicle owner's deterministic travel plans, fundamentally eliminating the risk of conflict between dispatch operations and vehicle use.

[0102] S5, perform grid adaptability optimization processing on the emergency adjustment set and community power grid status data to obtain a dispatch instruction set. The grid adaptability optimization processing is based on determining the safety boundary based on the real-time operating status of the power grid and optimizing the allocation of charging and discharging power within the boundary.

[0103] Specifically, this step includes the following sub-steps:

[0104] S501, perform safety boundary calculation on the community power grid status data to obtain power grid safety operation domain data. The safety boundary calculation is to convert node voltage deviation and power grid frequency fluctuation into linear inequality constraints with respect to node net power by linearizing the power grid model.

[0105] In some implementations, the safety boundary calculation involves converting node voltage deviations and grid frequency fluctuations into linear inequality constraints on node net power by linearizing the grid model. This includes grid model linearization and safety constraint transformation. The grid model linearization is achieved by performing a first-order Taylor expansion of the nonlinear power flow equations to obtain the node power-voltage sensitivity relationship. The safety constraint transformation uses this sensitivity relationship to convert voltage and frequency safety limits into a set of linear inequality constraints on node net power.

[0106] In this embodiment, the power grid model linearization process approximates the AC power flow equations based on the current operating point. Specifically, it uses the linear relationship between the change in node injected power and the changes in voltage phase angle and amplitude to describe the small-signal changes in the power grid state. The coefficient matrix of this linear relationship is the Jacobian matrix J, which contains the partial derivative information of node power with respect to voltage, reflecting the sensitivity of voltage changes when injected power changes. It should be noted that the sensitivity matrix S can be obtained by calculating the inverse of this Jacobian matrix. This sensitivity matrix quantitatively describes the linear mapping relationship between the change in node voltage and the change in injected power, i.e., the voltage change caused by a unit power change. The sensitivity matrix S is the core data bridge connecting the power grid model linearization process and the safety constraint transformation process, and its mathematical expression is: This matrix will serve as the direct input for the safety constraint transformation process, used to convert voltage safety constraints into power inequality constraints.

[0107] In some implementations, the safety constraint transformation process converts voltage safety constraints into power constraints based on the sensitivity matrix S; for node voltage deviation constraints, they are converted into power inequality constraints through sensitivity relationships. , ,in and These are submatrices of the sensitivity matrix S, representing the voltage sensitivity coefficients to active power and reactive power, respectively. and This is a vector representing the changes in net active and reactive power at the node. and This represents the upper and lower bound vectors for node voltage safety. This represents the voltage vector of the current node. The processing outputs a series of linear inequalities, which represent the regulation limits that the injected power (including V2G charging and discharging power) of each node must adhere to in order to ensure that the voltage of each node in the community distribution network is within a safe range.

[0108] Furthermore, frequency security constraints are converted into power constraints through regional frequency response characteristics; the linear relationship between frequency deviation and power imbalance is expressed as: , where K is the system frequency response coefficient, representing the frequency change caused by a unit power change; The total power imbalance of the system; frequency safety constraints are converted to: , ,in Let represent the change in active power of the i-th node (or the i-th V2G vehicle), indicating the increase (positive value during discharge) or decrease (negative value during charging) of the power injected into the grid by that node. The algebraic sum of the changes in active power at all nodes represents the net active power exchanged between the entire V2G vehicle cluster and the grid. A positive value indicates that the entire vehicle cluster is discharging into the grid, while a negative value indicates that the entire vehicle cluster is charging from the grid. K is the system frequency response coefficient, a parameter characterizing the grid's inertia and frequency regulation capability, representing how many hertz the frequency drops for every 1 megawatt of power deficit in the system. This coefficient is determined by the grid operating mechanism. The system frequency deviation represents the difference between the actual measured frequency and the rated frequency (50Hz or 60Hz). The upper limit of the permissible positive frequency deviation indicates the maximum value at which the system frequency is allowed to exceed the rated frequency. The lower limit of the permissible negative frequency deviation is represented as the minimum value by which the system frequency is allowed to fall below the rated frequency. This process outputs another linear inequality constraint, which, to ensure the frequency of the entire power grid system remains stable within a safe range, limits the total power variation resulting from the aggregation of all V2G vehicles to an acceptable range. It complements the voltage constraint mentioned above, jointly ensuring power grid safety.

[0109] Preferably, the power grid safe operation domain data is represented as a set of linear inequalities with respect to node net power. , A column vector consisting of the net active power changes of all nodes. Its meaning is the same as above. A column vector consisting of the net reactive power changes at all nodes represents the reactive power injection or absorption changes at each node. A is a coefficient matrix that integrates the active power sensitivity coefficients under voltage constraints. ) and the coefficients in the frequency constraint (from It defines the linear effect of active power variation on all safety constraints (voltage + frequency). B is a coefficient matrix consisting of the sensitivity coefficients of reactive power in the voltage constraint. This is because frequency changes are primarily related to active power. It defines the linear effect of reactive power changes on voltage safety constraints. C is a constant vector that integrates the difference between the voltage safety limit and the current value (e.g., ...). ) and frequency security limits (such as This represents the maximum boundary value allowed by various safety constraints. This set of inequalities integrates all safety requirements from both the node voltage and system frequency dimensions of the distribution network. Specifically, it establishes an n-dimensional space composed of all linearized safety constraints (node ​​voltage constraints and system frequency constraints), where any power change vector ( All of these can ensure the safe operation of the power grid. This space is called the power grid safe operation domain. The optimization task in the subsequent S502 step is to find the optimal charging and discharging power for each vehicle within this safe domain.

[0110] In some implementations, the linearization process employs a real-time adaptive mechanism; when the change in the grid operating point exceeds a threshold, the Jacobian matrix and sensitivity matrix are recalculated to ensure the accuracy of the linearization model; the threshold is dynamically adjusted based on the node voltage change rate and load change rate. The grid safety operation domain data provides linear constraints for subsequent power optimization processing, ensuring that the charging and discharging power allocation scheme simultaneously meets the grid's voltage safety requirements and frequency stability requirements; this set of linear inequalities directly serves as constraints for the quadratic programming problem, achieving an effective combination of grid safety constraints and optimization objectives.

[0111] S502, power optimization processing is performed on the emergency adjustment set and the power grid safe operation domain data to obtain a scheduling instruction set. The power optimization processing optimizes the charging and discharging power allocation within the safe operation domain through a quadratic programming algorithm.

[0112] In some implementations, the power optimization process optimizes the allocation of charging and discharging power within the safe operating domain using a quadratic programming algorithm, including optimization objective construction and constraint integration solution. The optimization objective construction process constructs optimization objective functions for the different functional positioning of the guaranteed energy storage pool and the market-oriented energy storage pool, respectively. The constraint integration solution process combines the constraints of the power grid safe operating domain, the vehicle's own capability constraints, and the optimization objective to form a complete quadratic programming problem and solve it.

[0113] In this embodiment, the optimization target construction process involves setting optimization targets for two energy storage pools respectively; for the backup energy storage pool... Its optimization objective is to minimize the deviation between the community's basic load demand and the total power of the backup pool: Where t is the time index, The community's basic load demand during time period t is derived from the community load demand data in step S1 and represents the core electricity demand that the power grid needs to meet. i is the vehicle index. The planned charging and discharging power of vehicle i during time period t is the decision variable that the optimization algorithm needs to solve. Discharging behavior is represented by positive power (power supplied to the grid), and charging behavior is represented by negative power (power drawn from the grid). For market-based energy storage... Its optimization objective is to maximize scheduling benefits or minimize adjustment costs: ,in The planned charging and discharging power of vehicle i during time period t is the decision variable that the optimization algorithm needs to solve. Discharging behavior is represented by positive power (power sent to the grid), and charging behavior is represented by negative power (power taken from the grid). c(t) is the electricity price signal for time period t.

[0114] In this embodiment, the constraint integration solution process integrates multiple constraints into the optimization problem; these constraints include constraints related to the power grid safe operation domain: Vehicle charging and discharging power capacity constraints: and constraints on vehicle energy state changes: ,in This represents the remaining battery charge of vehicle i at the start of time period t. It represents the remaining battery charge of vehicle i at the start of the next time period t+1. The energy conversion efficiency of vehicle i (typically including charging / discharging efficiency and inverter efficiency) is a dimensionless coefficient (ranging from 0 to 1). It should be noted that in actual modeling, the charging efficiency... and discharge efficiency They are usually treated differently; in this implementation, to simplify the model, the formula... exist (Charging) can be represented as ,exist (Discharge) can be represented as . It is the duration of each scheduling period. It is the rated total capacity of the vehicle's battery.

[0115] Furthermore, the complete description of the quadratic programming problem is as follows: The following constraints must be met. ; ; ,in The optimization objectives are for the two energy storage pools, respectively. These weighting coefficients are used to coordinate the priorities of the two objectives. Specifically, The optimization objective of the backup energy storage pool is to minimize the deviation between the total power of the backup pool and the basic load demand of the community, ensuring power supply reliability. Its mathematical expression is: ,in The charging and discharging power of vehicle i in the safety pool during time period t (decision variable), where T is the total number of time periods within a scheduling cycle. The optimization objective of market-based energy storage pools aims to minimize the total dispatch cost (or negative revenue) to achieve economic optimality. Its mathematical expression is: Where T is the total number of time periods within a scheduling period. The charging and discharging power (decision variable) of vehicle i in the market-based pool during time period t. The scheduling instruction set. It is the solution to the entire quadratic programming problem, and It is the objective function that needs to be minimized. It is worth mentioning that step S301 defines the market-based energy storage capacity demand as the integral area of ​​the adjustable load demand over time. This calculation method accurately quantifies the total energy value required to smooth the peak-valley difference of the community power grid from a macro perspective. This energy value determines the total capacity scale required for the market-based energy storage pool. This capacity scale parameter guides step S302 to allocate the corresponding number of flexible vehicle resources to the market-based energy storage pool, providing the pool with the material basis required to perform energy time-shifting tasks. Finally, in step S502, the quadratic programming algorithm aims at optimal economic efficiency and optimizes the specific charging and discharging power of the pool's vehicles at the minute or hour level. The scheduling result, under the premise of satisfying all safety constraints, is precisely the optimal solution to achieve the above-mentioned total energy regulation target, thus ensuring the logical consistency and unified goal of the entire process from macro capacity planning to micro power allocation.

[0116] Preferably, the solution process uses an interior-point solver to handle constrained quadratic programming problems. The algorithm first converts inequality constraints into equality constraints and non-negative variable constraints, and then iteratively solves the Karush-Kuhn-Tucker optimality conditions using the primal-dual interior-point method to finally obtain the global optimal solution.

[0117] In some implementations, the optimization process employs a rolling time-domain optimization strategy; each scheduling cycle solves for the optimal power allocation for several future time periods, but only executes the instructions for the current time period. The next cycle re-optimizes based on the latest state, enhancing the robustness and adaptability of the scheduling strategy. The final scheduling instruction set is represented as: ,in Let P represent the set of scheduling periods, containing all time points where power commands need to be issued. Let P represent the scheduling command set, a set containing all scheduling commands. This command set contains the specific charging and discharging power values ​​for each schedulable vehicle in each scheduling period, and is issued to each vehicle for execution via the V2X communication network, achieving the final optimized scheduling of community vehicle-to-grid interaction. In summary, the set of linear inequalities in the grid safety operation domain obtained in step S501, the upper and lower limits of the vehicle's own charging and discharging power, and the battery SOC variation constraints, along with the optimization objective functions (minimizing load deviation and minimizing electricity cost) established for the two energy storage tanks in step S502, are integrated to form a standard quadratic programming problem mathematical model. Then, a numerical optimization algorithm solver, such as the interior-point method, is automatically and uniformly solved. The solver works by systematically searching within the feasible solution space (i.e., the "safety operation domain") enclosed by all constraints for the unique set of values ​​that minimizes the aforementioned optimization objective function. Numerical solution. Essentially, it's a multi-objective constrained optimization problem. The solver outputs the optimal performance state achievable under the strict constraints of all grid safety constraints (voltage and frequency not exceeding limits), vehicle battery protection constraints (power and SOC not exceeding limits), and user travel constraints (S402 is guaranteed). This "optimal" means: maximizing the fulfillment of basic community electricity needs by vehicles in the guaranteed supply pool (reliability objective), while maximizing economic benefits by allowing vehicles in the market-based supply pool to charge during periods of low electricity prices and discharge during periods of high electricity prices (economic objective). Therefore, each obtained... The instructions are all the optimal allocation results under the global trade-off.

[0118] Based on the description of the above embodiments of the dynamic priority charging and discharging scheduling method based on community V2X vehicle-to-grid interaction, this application also discloses a dynamic priority charging and discharging scheduling system based on community V2X vehicle-to-grid interaction. This system can be a computer program (including program code) that runs the aforementioned dynamic priority charging and discharging scheduling method based on community V2X vehicle-to-grid interaction. Please see the appendix. Figure 2 As shown, the dynamic priority charging and discharging scheduling system based on community V2X vehicle-to-grid interaction can operate the following units:

[0119] The acquisition unit 110 is used to acquire data to be processed through a V2X communication network. The data to be processed includes real-time battery status data of all vehicles in the community, vehicle owner travel pattern data, community load demand data, vehicle confirmed trip data, and community power grid status data. The real-time battery status data includes the vehicle's remaining charge (SOC) and battery health (SOH). The community load demand data includes basic load demand and adjustable load demand. The vehicle confirmed trip data consists of recent travel plans actively reported by vehicle owners with high certainty. The community power grid status data includes power grid frequency fluctuations and node voltage deviations.

[0120] The joint evaluation unit 120 is used to perform joint scheduling evaluation processing on the real-time battery status data of the vehicle and the travel pattern data of the vehicle owner, so as to obtain the vehicle dynamic score set and its corresponding scheduling time window data. The joint scheduling evaluation processing is based on the collaborative calculation of battery status data and travel time patterns obtained through probability mapping to generate scheduling priorities and time windows.

[0121] Resource pooling unit 130 is used to perform resource pooling processing on the vehicle dynamic score set, scheduling time window data and community load demand data to obtain a power pool partition set. The resource pooling processing is based on determining the energy storage capacity allocation parameters according to load demand and optimizing the allocation by combining vehicle score and time window matching degree.

[0122] The conflict resolution unit 140 is used to perform trip conflict resolution processing on the power pool partition set, scheduling time window data and vehicle confirmed trip data to obtain an emergency adjustment set. The trip conflict resolution processing is based on deterministic trip plan and predictive scheduling time window to identify conflict status and perform high-priority coverage adjustment.

[0123] The power grid optimization unit 150 is used to perform power grid adaptive optimization processing on the emergency adjustment set and the community power grid status data to obtain a dispatch instruction set. The power grid adaptive optimization processing is based on determining the safety boundary based on the real-time operating status of the power grid and optimizing the allocation of charging and discharging power within the boundary.

[0124] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A dynamic priority charging and discharging scheduling method based on community V2X vehicle network interaction, characterized in that, The method comprises the following steps: S1, obtaining to-be-processed data through a V2X communication network, the to-be-processed data comprising real-time battery state data of all vehicles in a community, vehicle owner travel regularity data, community load demand data, vehicle confirmed trip data and community power grid state data; the real-time battery state data comprising vehicle residual capacity SOC and battery health SOH; the community load demand data comprising basic load demand and adjustable load demand; the community power grid state data comprising power grid frequency fluctuation and node voltage deviation; S2, jointly scheduling and evaluating the vehicle real-time battery state data and the vehicle owner travel regularity data, thereby obtaining a vehicle dynamic score set and corresponding scheduling time window data, the joint scheduling and evaluation being based on collaborative calculation of the battery state data and travel time regularity obtained through probability mapping to generate scheduling priority and a time window; S3, resource pooling the vehicle dynamic score set, the scheduling time window data and the community load demand data, thereby obtaining an electric power cell division set, the resource pooling being based on load demand to determine energy storage capacity matching parameters and combining vehicle score and time window matching degree for optimized allocation; The S3 step comprises the following sub-steps: S301, based on the community load demand data, solving the security energy storage capacity constraint and the market energy storage capacity constraint through a linear programming model, thereby generating energy storage capacity matching parameters; S302, vehicle allocation processing the vehicle dynamic score set, the scheduling time window data and the energy storage capacity matching parameters, thereby obtaining an electric power cell division set, the vehicle allocation processing being based on score ranking and scheduling time window matching degree, and using a weighted round-robin scheduling algorithm to allocate vehicles to security energy storage pools and market energy storage pools; S4, trip conflict resolution processing the electric power cell division set, the scheduling time window data and the vehicle confirmed trip data, thereby obtaining an emergency adjustment set, the trip conflict resolution processing being based on deterministic trip planning and predictive scheduling time window to identify conflict states and perform high-priority override adjustment, the high-priority override adjustment being based on trip conflict to remove occupied vehicle resources from the electric power cell division set; S5, power grid adaptability optimization processing the emergency adjustment set and the community power grid state data, thereby obtaining a scheduling instruction set, the power grid adaptability optimization processing being based on real-time running state of the power grid to determine a safety boundary and optimize allocation of charging and discharging power within the boundary.

2. The dynamic priority charging and discharging scheduling method based on community V2X vehicle network interaction according to claim 1, characterized in that, The S2 step comprises the following sub-steps: S201, based on the vehicle owner travel regularity data and real-time time, calculating off-site time matching degree, thereby obtaining vehicle scheduling available window data, the off-site time matching degree calculation being based on probability mapping of current time and vehicle historical off-site time probability distribution to output scheduling safety duration of the vehicle at the current time; S202, the real-time battery status data of the vehicle and the available window data for vehicle scheduling are dynamically scored and the scheduling window is calculated to obtain the vehicle dynamic score set and scheduling time window data. The dynamic scoring and scheduling window calculation adopts a linear weighting method to combine SOC, SOH and scheduling safety duration to obtain a real-time priority score, and generates a suggested scheduling period based on the score and available window.

3. The dynamic priority charging and discharging scheduling method based on community V2X vehicle-network interaction according to any one of claims 1-2, characterized in that, Step S4 includes the following sub-steps: S401, perform trip conflict detection on the vehicle confirmed trip data and scheduling time window data to obtain trip conflict flag data. The trip conflict detection is to compare the reported trip time with the scheduling time window to generate a binary conflict status flag. S402, perform high-priority overlay processing on the power pool partition set and trip conflict flag data to obtain an emergency adjustment set. The high-priority overlay processing is based on the trip conflict flag data to remove the occupied vehicle resources from the power pool partition set.

4. The dynamic priority charging and discharging scheduling method based on community V2X vehicle-network interaction according to claim 3, characterized in that, Step S5 includes the following sub-steps: S501, perform safety boundary calculation on the community power grid status data to obtain power grid safety operation domain data; the safety boundary calculation is to convert node voltage deviation and power grid frequency fluctuation into linear inequality constraints with respect to node net power by linearizing the power grid model; S502, power optimization processing is performed on the emergency adjustment set and the power grid safe operation domain data to obtain a scheduling instruction set. The power optimization processing optimizes the charging and discharging power allocation within the safe operation domain through a quadratic programming algorithm.

5. The dynamic priority charging and discharging scheduling method based on community V2X vehicle network interaction according to claim 2, characterized in that, The departure time matching degree calculation in S201 is achieved by mapping the current time with the probability distribution of the vehicle's historical departure time, and outputting the vehicle's scheduling safety duration at the current moment, including probability accumulation processing and safety duration calculation processing. The dynamic scoring and scheduling window calculation in S202 is achieved by using a linear weighting method to integrate SOC, SOH and scheduling safety duration to obtain a real-time priority score, and generating a suggested scheduling period based on the score and available window, including score calculation processing and scheduling window generation processing.

6. The dynamic priority charging and discharging scheduling method based on community V2X vehicle network interaction according to claim 3, characterized in that, The process described in S301, which uses a linear programming model to solve for the constraints of guaranteed energy storage capacity and market-based energy storage capacity based on community load demand data, includes load demand decomposition and capacity constraint optimization. The process described in S302, which uses a weighted round-robin scheduling algorithm to allocate vehicles to guaranteed energy storage pools and market-based energy storage pools based on scoring, ranking, and scheduling time window matching, includes vehicle ranking and capacity weighted allocation.

7. The dynamic priority charging and discharging scheduling method based on community V2X vehicle-network interaction according to claim 3, characterized in that, In S401, the trip conflict detection involves comparing the reported trip time with the scheduling time window to generate a binary conflict status identifier, including time window intersection judgment processing and conflict flag generation processing; in S402, the high priority coverage processing involves removing the occupied vehicle resources from the power pool partition set based on the trip conflict flag data, including conflict vehicle identification processing and power pool partition set update processing.

8. The dynamic priority charging and discharging scheduling method based on community V2X vehicle-network interaction according to claim 4, characterized in that, The safety boundary calculation in S501 is to convert node voltage deviation and grid frequency fluctuation into linear inequality constraints about node net power by linearizing the grid model, including grid model linearization processing and safety constraint transformation processing; the power optimization processing in S502 is to optimize the charging and discharging power allocation in the safe operating domain by using a quadratic programming algorithm, including optimization target construction processing and constraint integration solution processing.

9. A dynamic priority charging and discharging scheduling system based on community V2X vehicle network interaction, characterized in that, The system includes: The acquisition unit is used to acquire data to be processed through a V2X communication network. This data includes real-time battery status data of all vehicles within the community, vehicle owner travel pattern data, community load demand data, confirmed vehicle trip data, and community power grid status data. The real-time battery status data includes the vehicle's remaining charge (SOC) and battery health (SOH). The community load demand data includes basic load demand and adjustable load demand. The confirmed vehicle trip data consists of highly certain recent travel plans proactively reported by vehicle owners. The community power grid status data includes power grid frequency fluctuations and node voltage deviations. The joint evaluation unit is used to perform joint scheduling evaluation processing on the real-time battery status data of the vehicle and the travel pattern data of the vehicle owner, so as to obtain the vehicle dynamic score set and its corresponding scheduling time window data. The joint scheduling evaluation processing is based on the collaborative calculation of battery status data and travel time patterns obtained through probability mapping to generate scheduling priorities and time windows. The resource pooling unit is used to perform resource pooling processing on the vehicle dynamic score set, scheduling time window data and community load demand data to obtain a power pool partition set. The resource pooling processing is based on determining the energy storage capacity allocation parameters according to load demand and optimizing the allocation by combining vehicle score and time window matching degree. The resource pooling unit is specifically used to perform the following processes: S301, Based on the community load demand data, solve the constraints of guaranteed energy storage capacity and market-based energy storage capacity through a linear programming model to generate energy storage capacity allocation parameters; S302, the vehicle dynamic score set, scheduling time window data and energy storage capacity ratio parameters are processed for vehicle allocation to obtain the power pool partition set. The vehicle allocation process is based on the score ranking and scheduling time window matching degree, and a weighted round-robin scheduling algorithm is used to allocate vehicles to the guaranteed energy storage pool and the market-based energy storage pool. The conflict resolution unit is used to perform trip conflict resolution processing on the power pool partition set, scheduling time window data and vehicle confirmed trip data to obtain an emergency adjustment set. The trip conflict resolution processing is based on deterministic trip plans and predictive scheduling time windows to identify conflict status and perform high-priority coverage adjustment. The high-priority coverage adjustment is based on removing the occupied vehicle resources from the power pool partition set according to the trip conflict. The power grid optimization unit is used to perform power grid adaptive optimization processing on the emergency adjustment set and community power grid status data to obtain a dispatch instruction set. The power grid adaptive optimization processing determines the safety boundary based on the real-time operating status of the power grid and optimizes the allocation of charging and discharging power within the boundary.

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