A park reverse charging energy transaction management method and system

Abstract

The present application relates to the technical field of energy transaction management, and provides a park reverse charging energy transaction management method and system, the method comprising: acquiring power grid state data and V2G vehicle reverse power supply behavior data of the park; obtaining auxiliary service quality evaluation results according to the power grid state data and the V2G vehicle reverse power supply behavior data; acquiring V2G vehicle battery information and obtaining battery loss evaluation results according to the V2G vehicle reverse power supply behavior data; generating real-time incentive unit price according to the auxiliary service quality evaluation results, the battery loss evaluation results and the basic electricity price; acquiring V2G vehicle actual power supply and performing transaction clearing and settlement according to the real-time incentive unit price; and after the transaction clearing and settlement, adaptively adjusting incentive strategy parameters related to the generation process of the real-time incentive unit price according to V2G scheduling response data and power grid operation feedback. The present application has the effects of optimizing the utilization efficiency of the energy storage system and improving the consumption capacity of photovoltaic electric energy.

Description

Technical Field

[0001] This invention relates to the technical field of energy trading management, specifically to a method and system for managing reverse charging energy trading in industrial parks. Background Technology

[0002] In park environments where smart grids and the new energy industry are deeply integrated, photovoltaic power plants, energy storage systems, and electric vehicle V2G reverse power supply technology together constitute an integrated energy management system of photovoltaic, energy storage, and charging. However, in actual operation, the existing energy trading clearing and settlement rules fail to fully reflect the dynamic value and ancillary service contributions of electric vehicle reverse power supply, resulting in low participation from vehicle owners, which in turn affects the effective utilization of energy storage systems and the efficiency of photovoltaic power consumption. In addition, the inaccuracy of electricity prices and load forecasts, as well as the coordination difficulties among heterogeneous equipment from multiple brands, further exacerbate the complexity and inefficiency of energy management in the park.

[0003] Against the backdrop of insufficient V2G capacity aggregation, energy storage systems within industrial parks face greater pressure in coping with load fluctuations. To ensure grid stability, energy storage system charging and discharging scheduling strategies must adopt a more conservative operating mode, reserving substantial backup capacity. For example, to cope with potential load surges or sudden drops in photovoltaic power, energy storage systems may be limited to operating within 40% to 60% of their capacity range, ensuring both the absorption of excess energy and its rapid release when needed. While this conservative strategy guarantees system stability, it results in a lower actual deep cycle life of the energy storage battery modules, further reducing their energy throughput and economic utilization. The energy storage system cannot fully utilize its capacity for arbitrage or deep peak shaving, thus lowering its return on investment.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a method and system for managing reverse charging energy transactions in industrial parks. It aims to address the problems that existing industrial park energy transaction clearing and settlement rules fail to fully reflect the dynamic value and ancillary service contributions of electric vehicles' reverse power supply, resulting in low participation enthusiasm from vehicle owners, which in turn affects the effective utilization of energy storage systems and the efficiency of photovoltaic power consumption. It also addresses the problems of inaccurate electricity price and load forecasting, coordination difficulties among heterogeneous equipment from multiple brands, and the exacerbation of the complexity and inefficiency of industrial park energy management.

[0006] The technical solution of this application is as follows:

[0007] Firstly, this application discloses a method for managing reverse charging energy transactions in a park, including:

[0008] Acquire data on the power grid status of the park and data on the reverse power supply behavior of V2G vehicles;

[0009] Based on grid status data and V2G vehicle reverse power supply behavior data, the quality of ancillary services provided by V2G vehicles is evaluated, and the ancillary service quality evaluation results are obtained.

[0010] Obtain V2G vehicle battery information and assess V2G vehicle battery loss based on V2G vehicle reverse power supply behavior data to obtain battery loss assessment results.

[0011] Based on the ancillary service quality assessment results, battery loss assessment results, and base electricity price, a real-time incentive unit price is generated.

[0012] Obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price;

[0013] After transaction clearing and settlement, the incentive strategy parameters related to the generation process of real-time incentive unit price are adaptively adjusted based on V2G dispatch response data and power grid operation feedback.

[0014] This technical solution enables a quantitative assessment of the dynamic value and ancillary service contribution of V2G vehicles' reverse power supply, and generates a real-time incentive price accordingly, effectively increasing vehicle owners' enthusiasm for participating in reverse charging and optimizing the efficiency of energy management in the park.

[0015] Secondly, this application also discloses a park reverse charging energy trading management system for performing park reverse charging energy trading management, including:

[0016] The data synchronization acquisition module is used to acquire power grid status data and V2G vehicle reverse power supply behavior data in the park.

[0017] The quality assessment execution module is used to assess the quality of ancillary services provided by V2G vehicles based on grid status data and V2G vehicle reverse power supply behavior data, and obtain ancillary service quality assessment results.

[0018] The battery loss assessment module is used to acquire V2G vehicle battery information and assess the battery loss of V2G vehicles based on V2G vehicle reverse power supply behavior data, and obtain battery loss assessment results.

[0019] The real-time incentive generation module is used to generate a real-time incentive unit price based on the ancillary service quality assessment results, battery loss assessment results, and the base electricity price.

[0020] The transaction clearing and settlement module is used to obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price;

[0021] The incentive strategy modulation module is used to adaptively adjust the incentive strategy parameters related to the generation process of real-time incentive unit price based on V2G dispatch response data and power grid operation feedback after transaction clearing and settlement.

[0022] This technical solution provides a system that integrates data acquisition, quality assessment, loss assessment, incentive generation, transaction clearing, and strategy adjustment, enabling comprehensive management of reverse charging energy transactions in the park and effectively improving transaction efficiency and system intelligence.

[0023] Beneficial Effects: The energy trading management method for reverse charging in industrial parks disclosed in this application acquires grid status data and V2G vehicle reverse power supply behavior data of the park, evaluates the quality of ancillary services provided by V2G vehicles and battery losses based on this data, generates a real-time incentive unit price in conjunction with the base electricity price, and then conducts transaction clearing and settlement. After the transaction, the incentive strategy parameters are adaptively adjusted based on V2G dispatch response data and grid operation feedback. This method effectively solves the problem that existing energy trading clearing and settlement rules fail to fully reflect the dynamic value and ancillary service contribution of electric vehicles' reverse power supply, resulting in low participation enthusiasm among vehicle owners. By quantifying the ancillary service value and battery losses of V2G vehicles and incorporating them into the generation of the real-time incentive unit price, the actual contribution and cost of V2G vehicles can be more accurately reflected, thereby significantly improving the enthusiasm of vehicle owners to participate in reverse charging. In addition, the mechanism of adaptively adjusting incentive strategy parameters allows the system to be optimized according to actual operating conditions, further improving the efficiency and flexibility of park energy management, and overcoming the complexity and inefficiency caused by the inaccuracy of electricity price and load forecasting and the coordination difficulties between multi-brand heterogeneous equipment in existing technologies. Therefore, the technical solution of this application can effectively promote the application of V2G technology in park energy management, optimize the utilization efficiency of energy storage system, and improve the absorption capacity of photovoltaic power. Attached Figure Description

[0024] Figure 1 This is a flowchart of a method for managing reverse charging energy transactions in a park, as described in one embodiment of the present invention.

[0025] Figure 2 This is a flowchart of a method for managing reverse charging energy transactions in a park, according to another embodiment of the present invention.

[0026] Figure 3 This is a system block diagram of a park reverse charging energy trading management system according to another embodiment of the present invention;

[0027] Explanation of reference numerals in the attached figures:

[0028] 1. Park Reverse Charging Energy Trading Management System; 11. Data Synchronization Acquisition Module; 12. Quality Assessment Execution Module; 13. Battery Loss Assessment Module; 14. Real-time Incentive Generation Module; 15. Transaction Clearing and Settlement Module; 16. Incentive Strategy Modulation Module. Detailed Implementation

[0029] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] This application provides a method for managing reverse charging energy trading in a park, combined with... Figure 1 As shown, it includes:

[0032] S1, acquire power grid status data and V2G vehicle reverse power supply behavior data of the park;

[0033] S2, based on grid status data and V2G vehicle reverse power supply behavior data, evaluate the quality of ancillary services provided by V2G vehicles and obtain the ancillary service quality evaluation results;

[0034] S3: Obtain V2G vehicle battery information and assess V2G vehicle battery loss based on V2G vehicle reverse power supply behavior data to obtain battery loss assessment results.

[0035] S4 generates a real-time incentive unit price based on the ancillary service quality assessment results, battery loss assessment results, and basic electricity price;

[0036] S5 obtains the actual power supply of V2G vehicles and performs transaction clearing and settlement based on the real-time incentive unit price;

[0037] S6, after transaction clearing and settlement, adaptively adjusts the incentive strategy parameters related to the generation process of real-time incentive unit price based on V2G dispatch response data and power grid operation feedback.

[0038] To better understand the embodiments of this application, some key terms involved are explained below.

[0039] "Power grid status data" refers to data reflecting the real-time operating status of the park's power grid, such as voltage, current, frequency, load, and power factor. This data can be collected in real time by devices such as sensors and smart meters deployed in the park's power grid.

[0040] "V2G vehicle reverse power supply behavior data" refers to the behavioral data generated by V2G vehicles during the process of supplying power to the grid, such as power supply power, power supply duration, power supply start time, power supply end time, and power supply amount. This data can be obtained through V2G charging piles or vehicle management systems.

[0041] V2G (Vehicle-to-Grid) is short for "vehicle-to-grid" technology, which refers to a system that enables bidirectional power transmission between electric vehicles and the power grid, achieving two-way energy flow and information exchange between electric vehicles and the power grid.

[0042] "Ancillary service quality" refers to the performance and effectiveness of the ancillary services provided by V2G vehicles to the power grid through reverse power supply, such as frequency regulation capability, voltage support capability, and reserve capacity provision capability.

[0043] "V2G vehicle battery information" refers to various parameters of the V2G vehicle battery, such as battery type, rated capacity, current state of charge (SOC), state of health (SOH), and cycle life. This information can be obtained through the vehicle's battery management system (BMS).

[0044] "Battery loss" refers to the capacity decay or shortened lifespan of a V2G vehicle battery during reverse power supply due to factors such as charge-discharge cycles and temperature changes.

[0045] "Basic electricity price" refers to the basic electricity trading price set by the power grid in the industrial park, which is usually determined by the electricity market or regulatory agency.

[0046] "Real-time incentive price" refers to the unit electricity price dynamically generated based on the quality of auxiliary services provided by V2G vehicles, battery wear, and base electricity price, used to incentivize vehicle owners to participate in reverse power supply.

[0047] "V2G dispatch response data" refers to the response of V2G vehicles to the reverse power supply dispatch instructions issued by the park's energy management system, such as response time, response power, and the deviation between the actual power supply and the dispatch instructions.

[0048] "Grid operation feedback" refers to the changes in the operating status of the park's power grid after V2G vehicles supply power in reverse, such as the degree of improvement in grid stability and the effect of reducing the peak-valley difference in load.

[0049] "Incentive strategy parameters" refer to various parameters that affect the real-time incentive unit price generation process, such as the ancillary service contribution weight, battery loss compensation coefficient, and market supply and demand factors.

[0050] The park reverse charging energy trading management method of this application embodiment is mainly characterized by the following aspects:

[0051] Firstly, various methods can be employed to acquire power grid status data and V2G vehicle reverse power supply behavior data within the park. For example, smart meters and various sensors can be widely deployed within the park to collect real-time data on power grid voltage, current, frequency, and load, and transmit this data to a central data processing platform via wired or wireless communication networks. Simultaneously, V2G charging stations can record the reverse power supply power, duration, and amount of power supplied by each V2G vehicle and upload this data to the data platform. Alternatively, existing SCADA (Supervisory Control and Data Acquisition) systems or EMS (Energy Management System) systems can be utilized. These systems typically integrate power grid data acquisition capabilities, requiring only interface adaptation to obtain the necessary power grid status data. For V2G vehicle reverse power supply behavior data, data interaction can be achieved through the vehicle's OBD (On-Board Diagnostics) interface or the API (Application Programming Interface) provided by the vehicle manufacturer, directly obtaining more detailed power supply behavior information from the vehicle.

[0052] Secondly, regarding the assessment of the quality of ancillary services provided by V2G vehicles based on grid status data and V2G vehicle reverse power supply behavior data, the following methods can be adopted to obtain ancillary service quality assessment results. For example, an assessment model can be preset, which comprehensively considers indicators such as the response speed of V2G vehicles, the stability of power supply, and the effect of regulating grid frequency or voltage. When a V2G vehicle supplies power in reverse, its real-time power supply data is compared with grid status data to calculate its contribution to grid stability. Specifically, the frequency regulation service quality can be quantified based on the power support provided by the V2G vehicle during grid frequency fluctuations; or the voltage support service quality can be quantified based on the reactive power support provided during grid voltage anomalies. As another implementation method, a machine learning-based assessment model can be used. The model is trained using historical data to automatically identify the ancillary service performance of V2G vehicles under different grid conditions and output quantified ancillary service quality assessment results. For example, a large amount of data on V2G vehicles supplying power in reverse under different grid operating conditions can be collected, and combined with feedback from grid operators, a classification or regression model can be trained to predict the ancillary service quality of V2G vehicles.

[0053] Furthermore, regarding the acquisition of V2G vehicle battery information and the assessment of V2G vehicle battery degradation based on V2G vehicle reverse power supply behavior data, the following methods can be used to obtain battery degradation assessment results. For example, the current state of charge (SOC), state of health (SOH), cycle count, and temperature of the battery can be obtained through the vehicle's battery management system (BMS). Combined with V2G vehicle reverse power supply behavior data, such as power supply power, power supply duration, and charge / discharge depth, the impact of this reverse power supply on battery life can be calculated using a preset battery aging model or empirical formula. For example, the amount of battery capacity degradation can be estimated based on the number of charge / discharge cycles and the depth of each cycle. As another approach, a data-driven battery degradation assessment method can be adopted, which establishes a more accurate battery degradation prediction model by long-term monitoring of the actual operating data of V2G vehicle batteries. For example, a neural network model can be used, taking various battery parameters and charge / discharge behavior data as input, and outputting battery degradation assessment results.

[0054] Next, regarding the generation of real-time incentive prices based on ancillary service quality assessment results, battery degradation assessment results, and base electricity prices, the following methods can be adopted. For example, a base electricity price can be set, and then increased based on the ancillary service quality assessment results to reward V2G vehicles for providing high-quality ancillary services. Simultaneously, based on the battery degradation assessment results, the base electricity price can be increased compensatorily to compensate vehicle owners for losses caused by battery degradation. Specifically, an ancillary service contribution coefficient and a battery degradation compensation coefficient can be set, and the base electricity price, these two coefficients, and the assessment results can be weighted to obtain the real-time incentive price. As another implementation method, a dynamic pricing model can be used. This model not only considers ancillary service quality and battery degradation but can also incorporate factors such as market supply and demand and grid urgency. The incentive price can be adjusted in real-time through optimization algorithms to maximize vehicle owner participation and grid benefits. For example, when grid load is tight and ancillary service demand is high, the incentive price can be increased to attract more V2G vehicles to participate in reverse power supply.

[0055] Then, in obtaining the actual power supply of V2G vehicles and conducting transaction clearing and settlement based on the real-time incentive unit price, the following methods can be adopted. For example, V2G charging piles can accurately measure the actual power supply of each V2G vehicle during the reverse power supply period. After the power supply ends, the actual power supply is multiplied by the real-time incentive unit price to calculate the total revenue of this transaction. Then, this revenue is settled to the V2G vehicle owner through a preset payment interface, such as a mobile payment platform or bank transfer. As another implementation method, a blockchain-based transaction clearing and settlement platform can be established, utilizing the decentralized and tamper-proof characteristics of blockchain to ensure the transparency and security of transaction data. The actual power supply of the V2G vehicle and the real-time incentive unit price serve as inputs to the smart contract, automatically executing the clearing and settlement process and recording the transaction on the blockchain, improving transaction efficiency and trust.

[0056] Finally, after transaction clearing and settlement, the following methods can be used to adaptively adjust the incentive strategy parameters related to the generation process of real-time incentive unit prices, based on V2G dispatch response data and grid operation feedback. For example, data such as the response time of V2G vehicles to dispatch commands, the deviation between actual power supply and commanded power, and feedback data on the improvement of grid frequency and voltage stability after V2G vehicles supply power in reverse can be collected. Based on this data, the effectiveness of the current incentive strategy can be evaluated. If it is found that vehicle owner participation is low or grid operation improvement is not significant, the incentive strategy parameters can be adjusted, such as increasing the ancillary service contribution coefficient or battery loss compensation coefficient, to achieve a better incentive effect. As another implementation method, reinforcement learning algorithms can be used, treating V2G dispatch response data and grid operation feedback as environmental observations, and adjusting incentive strategy parameters as actions of the agent. Through continuous trial and error and learning, the incentive strategy parameters can be automatically optimized to adapt to the constantly changing grid environment and vehicle owner behavior patterns.

[0057] Optional, combined Figure 2 As shown, the steps in S4 to generate a real-time incentive unit price based on the ancillary service quality assessment results, battery loss assessment results, and base electricity price include:

[0058] S41, based on the power grid status data and the V2G vehicle reverse power supply behavior data, determine the urgency of the power grid for ancillary services, and determine the contribution weight of the V2G vehicle in combination with the urgency.

[0059] S42, Determine the compensation weight for battery loss based on the V2G vehicle battery information and the V2G vehicle reverse power supply behavior data;

[0060] S43, the base electricity price, the ancillary service quality assessment result and the battery loss assessment result are weighted and summed according to the contribution weight and the compensation weight to obtain the initial real-time incentive unit price;

[0061] S44, compare the initial real-time incentive unit price with the car owner's expected income to obtain the expected comparison result, and adjust the initial real-time incentive unit price according to the expected comparison result to obtain the real-time incentive unit price.

[0062] Specifically, determining the urgency of the grid's demand for ancillary services involves analyzing the intensity and urgency of the grid's current demand for these services based on real-time grid status data, such as grid load, frequency deviation, and voltage fluctuations, combined with V2G vehicle reverse power supply behavior data. For example, when the grid load is at its peak or there are significant frequency fluctuations, the urgency of the grid's demand for ancillary services such as frequency regulation and peak shaving will increase significantly. The contribution weight of V2G vehicles can be understood as the proportion of value demonstrated by V2G vehicles in meeting the grid's urgent needs, with the aim of incentivizing V2G vehicles to provide services when the grid needs them most. This contribution weight can be dynamically adjusted based on factors such as the V2G vehicle's response speed, power supply capacity, and duration, combined with the grid's urgency. Determining the compensation weight for battery loss involves quantifying the degree of battery loss during reverse power supply based on V2G vehicle battery information, such as battery type, state of health (SOH), and cycle life, as well as V2G vehicle reverse power supply behavior data, and setting a corresponding compensation ratio accordingly. The aim is to ensure that V2G vehicle owners receive reasonable and adequate compensation for battery degradation caused by participating in ancillary services, thereby alleviating their concerns about the impact on battery life. In practical application, the base electricity price, ancillary service quality assessment results, and battery degradation assessment results are weighted and summed according to contribution weight and compensation weight to obtain the initial real-time incentive unit price. This is a pricing mechanism that comprehensively considers multiple factors, where the contribution weight reflects the value of the V2G vehicle to the grid, and the compensation weight reflects the coverage of the owner's costs. Furthermore, the initial real-time incentive unit price is compared with the owner's expected benefits to obtain the expected comparison result, and the initial real-time incentive unit price is adjusted according to the expected comparison result to obtain the real-time incentive unit price. The owner's expected benefits can be obtained through questionnaires, historical data analysis, or machine learning model prediction, reflecting the owner's minimum expected benefit from participating in reverse power supply. The aim is to ensure that the final real-time incentive unit price meets or exceeds the owner's psychological expectations, thereby effectively increasing the owner's willingness to participate. If the initial real-time incentive price is lower than the car owner's expectations, it can be appropriately increased; otherwise, it can be maintained or slightly adjusted to achieve a balance between incentive effect and economic benefits.

[0063] Optionally, after transaction clearing and settlement, the steps for adaptively adjusting the incentive strategy parameters related to the generation process of real-time incentive unit prices, based on V2G dispatch response data and grid operation feedback, include:

[0064] Acquire the performance data of the reverse charging pile itself, similar data of nearby reverse charging piles, and power grid status data of nearby power grid sensors;

[0065] A preliminary confidence assessment is conducted on the performance data of the reverse charging pile itself, the similar data of nearby reverse charging piles, and the power grid status data of nearby power grid sensors to obtain a preliminary confidence label.

[0066] The preliminary confidence level is corrected based on contextual information to obtain the corrected confidence level.

[0067] Based on the modified confidence level, the quality of auxiliary services provided by V2G vehicles is evaluated to obtain the auxiliary service quality evaluation results and generate a service premium factor.

[0068] Based on the service premium factor, the ancillary service quality assessment results, the battery loss assessment results, and the base electricity price, and in conjunction with the corrected confidence level, a real-time incentive unit price is generated;

[0069] The system displays the real-time incentive unit price and provides adjustment information for data points with a confidence level below a preset threshold.

[0070] Specifically, acquiring the performance data of a reverse charging station refers to collecting the operating parameters of a single reverse charging station, such as its maximum output power, actual charging and discharging efficiency, historical fault records, maintenance cycle, and real-time readings from internal sensors (such as temperature, voltage, and current). Data from neighboring reverse charging stations can be understood as acquiring similar performance data from other reverse charging stations in the same area, with the aim of providing a reference point for cross-validation or identifying abnormal patterns. Grid status data from nearby grid sensors refers to local grid operating data obtained from grid sensors near the reverse charging station, such as voltage fluctuations, frequency deviations, and load changes, with the aim of reflecting the actual grid environment when V2G vehicles provide auxiliary services.

[0071] The preliminary confidence assessment of the acquired data, resulting in a preliminary confidence rating, involves making an initial judgment on the reliability, completeness, timeliness, and consistency of each data source. For example, data can be labeled as "high confidence," "medium confidence," or "low confidence" based on indicators such as packet loss rate, sensor calibration date, and deviation of data from historical averages.

[0072] In practical applications, the initial confidence level is adjusted based on contextual information to obtain a revised confidence level. Contextual information may include, but is not limited to, weather conditions (such as thunderstorms that may affect communication), peak power grid load (which may cause data transmission delays), equipment maintenance records (which may cause temporary data interruptions or anomalies), and historical fault modes (identifying known problems of a specific data source). By combining this contextual information, the initial confidence level can be dynamically adjusted. For example, under severe weather conditions, even if the data is complete, its confidence level may be appropriately lowered.

[0073] Furthermore, based on the adjusted confidence level, the quality of ancillary services provided by V2G vehicles is assessed, resulting in an ancillary service quality assessment and generating a service premium factor. This means that the adjusted confidence level is used as a weighting factor or adjustment parameter when assessing the quality of ancillary services provided by V2G vehicles. For example, for data with low confidence levels, their weight in the ancillary service quality assessment is reduced to minimize their negative impact on the assessment results. The service premium factor, on the other hand, quantifies the additional value of ancillary services provided by V2G vehicles after considering the data confidence level. For example, when the power grid urgently needs ancillary services and the data confidence level is high, the service premium factor will be higher.

[0074] Therefore, based on the service premium factor, ancillary service quality assessment results, battery degradation assessment results, and base electricity price, and combined with the adjusted confidence level, a real-time incentive unit price is generated. The adjusted confidence level plays a role again in this step, ensuring that the final generated real-time incentive unit price not only reflects service quality, battery degradation, and base electricity price, but also fully considers the reliability level of all input data, making the formulation of the incentive unit price more fair and accurate.

[0075] Finally, the system displays the real-time incentive price and provides adjustment information for data points with confidence levels below a preset threshold. The purpose is to improve system transparency, allowing V2G vehicle owners or park managers to understand the incentive price formation mechanism. In particular, when the confidence level of certain data sources is low, the system will provide corresponding prompts, such as "Charging pile data confidence level is low; it is recommended to check the equipment," thereby guiding users to take measures to improve data quality and further optimize the accuracy of the incentive strategy.

[0076] Optionally, the steps of assessing the quality of auxiliary services provided by V2G vehicles based on the adjusted confidence level, obtaining the auxiliary service quality assessment results, and generating a service premium factor include:

[0077] Identify the types of ancillary service needs and priorities of the power grid;

[0078] Based on the V2G vehicle reverse power supply behavior data and the adjusted confidence level, evaluate the contribution score of V2G vehicles in each ancillary service demand type.

[0079] Based on the priority of the auxiliary services, assign weights to each contribution score;

[0080] Based on the weight of each contribution score, the weighted contribution scores are aggregated to obtain the comprehensive auxiliary service quality assessment result;

[0081] Based on the comprehensive ancillary service quality assessment results and the urgency of the power grid for ancillary services, a service premium factor is generated.

[0082] Specifically, identifying the types and priorities of ancillary service needs for the power grid means that the system first monitors and analyzes the current operating status of the power grid in real time to determine the types of ancillary services currently required by the grid, such as frequency regulation, voltage regulation, reserve capacity, or black start support. Simultaneously, based on factors such as real-time grid load, fluctuations in renewable energy generation, and unforeseen events, the system dynamically determines the priority of these ancillary service needs. For example, when the grid frequency deviates significantly, the priority of frequency regulation services will be set to the highest.

[0083] The assessment of V2G vehicle contribution scores for each ancillary service demand type, based on the V2G vehicle reverse power supply behavior data and the adjusted confidence level, can be understood as follows: for each identified ancillary service demand type, the system combines V2G vehicle reverse power supply behavior data (such as actual power supply, response speed, duration, etc.) and the adjusted confidence level (reflecting data reliability) to quantify the actual contribution of the V2G vehicle in that specific service type. For example, for frequency modulation services, the contribution score might be based on the vehicle's response speed and power regulation accuracy; for standby capacity, it might be based on its available continuous power and capacity. The adjusted confidence level is used to weight or correct the original behavior data to ensure the accuracy of the assessment.

[0084] In practical applications, the weighting of each contribution score based on the ancillary service priority is specifically as follows: the system assigns corresponding weights to the contribution scores of V2G vehicles in different ancillary service types based on the previously identified ancillary service priorities. The higher the priority of the ancillary service, the greater the weight of its corresponding contribution score, reflecting the grid's urgent need for that type of service. For example, if frequency regulation service has the highest priority, then the contribution score of V2G vehicles in frequency regulation service will receive a higher weight.

[0085] Furthermore, based on the weight of each contribution score, the weighted contribution scores are aggregated to obtain the comprehensive auxiliary service quality assessment result. This means summarizing and calculating the weighted contribution scores of V2G vehicles in different auxiliary service types, for example, by weighted averaging or weighted summation, to obtain a comprehensive assessment result that can fully reflect the overall auxiliary service quality of V2G vehicles.

[0086] Therefore, generating a service premium factor based on the comprehensive ancillary service quality assessment results and the grid's urgency for ancillary services means dynamically calculating a service premium factor based on the comprehensive ancillary service quality assessment results of V2G vehicles, combined with the grid's overall urgency for current ancillary services (e.g., during peak grid load periods or when renewable energy output fluctuates drastically). This factor will be used to adjust the base electricity price to more accurately incentivize V2G vehicles to provide ancillary services urgently needed by the grid.

[0087] Optionally, the steps for assessing the quality of ancillary services provided by V2G vehicles and obtaining the ancillary service quality assessment results based on grid status data and V2G vehicle reverse power supply behavior data include:

[0088] Obtain at least one of the following data from the V2G vehicle: brand, model, battery type, rated power, and maximum charge / discharge rate, and store it as vehicle technical parameters;

[0089] Obtain at least one of the following data from the V2G charging pile: brand, model, maximum output power, communication protocol type, and control response delay, and store it as the charging pile's technical parameters.

[0090] Based on the vehicle technical parameters and the charging pile technical parameters, adjust the weighting coefficients and benchmark thresholds in the auxiliary service quality assessment process;

[0091] The performance data of V2G vehicles of different brands and models in the actual reverse power supply process were collected and compared with the actual needs and response effects of the power grid to obtain the performance comparison results.

[0092] Based on the performance comparison results, performance characteristic curves and deviation correction factors are generated for each brand and model of V2G vehicle and charging pile combination.

[0093] When evaluating the quality of auxiliary services provided by V2G vehicles based on the adjusted weighting coefficients and benchmark thresholds, the corresponding performance characteristic curves and deviation correction factors are invoked according to the brand and model of the V2G vehicle and the brand and model of the V2G charging pile. The original behavioral data is then standardized to obtain the auxiliary service quality evaluation results.

[0094] This involves acquiring at least one of the following data points for V2G vehicles: brand, model, battery type, rated power, and maximum charge / discharge rate, and storing this data as vehicle technical parameters. This means the system obtains the inherent technical attributes of the vehicle from the V2G vehicle or its management platform. These parameters are fundamental to understanding the potential capabilities and limitations of V2G vehicles when providing ancillary services. For example, battery type affects charge / discharge cycle life and energy density, while rated power and maximum charge / discharge rate directly determine the maximum power and response speed the vehicle can provide. This data is uniformly stored as vehicle technical parameters for subsequent evaluation.

[0095] Furthermore, acquiring at least one of the following data points—brand, model, maximum output power, communication protocol type, and control response latency—of the V2G charging pile and storing it as charging pile technical parameters refers to the system acquiring key performance indicators of the V2G charging pile. As the interface between the vehicle and the power grid, the performance of the V2G charging pile is crucial to the quality of ancillary services. Maximum output power determines the upper limit of energy transmission, the communication protocol type affects the compatibility and efficiency of data interaction, and control response latency directly relates to the real-time execution of power grid dispatch commands. These data, stored as charging pile technical parameters, together with the vehicle technical parameters, constitute a complete picture of the equipment's performance.

[0096] In practical applications, adjusting the weighting coefficients and benchmark thresholds in the auxiliary service quality assessment process based on the vehicle and charging pile technical parameters means that the system dynamically adjusts the parameters in the assessment model according to the characteristics of different V2G vehicle and charging pile combinations. For example, for equipment combinations with fast response speed and low control latency, their weight in auxiliary services with high real-time requirements, such as frequency regulation, can be appropriately increased; for equipment combinations with large battery capacity and high energy density, their benchmark threshold in energy support services can be adjusted accordingly. This adjustment ensures the personalization and accuracy of the assessment.

[0097] In addition, performance data of different brands and models of V2G vehicles during actual reverse power supply processes are collected and compared with the actual needs and response effects of the power grid to obtain performance comparison results. This refers to the system's continuous monitoring of the performance of V2G vehicles when actually participating in grid ancillary services. These performance data include actual power output, response time, and voltage support effect. By comparing these data with the actual needs and expected response effects of the power grid at a specific moment, the gap between the actual performance and ideal performance of different equipment combinations can be quantified, thus providing a basis for subsequent adjustments.

[0098] Based on the performance comparison results above, performance characteristic curves and deviation correction factors are generated for each brand and model of V2G vehicle and charging pile combination. The performance characteristic curves can be understood as describing the typical performance of a specific V2G vehicle and charging pile combination under different operating conditions (e.g., different load rates, different ambient temperatures), such as the relationship between power output and response time, and the relationship between battery loss and depth of discharge. The deviation correction factor is used to quantify and correct systematic deviations between actual and ideal performance. For example, a specific model of charging pile may have a fixed communication delay, which can be compensated for by the deviation correction factor.

[0099] Therefore, when evaluating the quality of ancillary services provided by V2G vehicles based on adjusted weighting coefficients and benchmark thresholds, the system standardizes the raw behavioral data by invoking corresponding performance characteristic curves and deviation correction factors according to the brand and model of the V2G vehicle and the V2G charging station, thus obtaining the ancillary service quality evaluation result. Specifically, when a V2G vehicle actually participates in reverse power supply, the system identifies its brand and model and the brand and model of the connected charging station, and automatically matches and invokes pre-generated performance characteristic curves and deviation correction factors. These factors are applied to the raw reverse power supply behavior data of the V2G vehicle for standardization, such as delay correction for response time or efficiency calibration for power output, thereby eliminating evaluation errors caused by equipment differences and obtaining more accurate and comparable ancillary service quality evaluation results.

[0100] Optionally, based on the brand and model of the V2G vehicle and the V2G charging station, the corresponding performance characteristic curves and deviation correction factors are used to standardize the raw behavioral data to obtain the auxiliary service quality assessment results. The steps include:

[0101] When a V2G vehicle or V2G charging station undergoes a firmware upgrade, it automatically triggers a collection and comparison of performance data. Based on the corresponding performance comparison results, it generates a new performance characteristic curve and a deviation correction factor, and then standardizes the original behavioral data based on the new performance characteristic curve and deviation correction factor.

[0102] When the cumulative running time of a V2G vehicle or V2G charging station reaches a preset threshold, or when the battery health status is detected to have decreased by more than a preset percentage, an aging assessment process is automatically initiated. Based on the results of the aging assessment process, the existing performance characteristic curves and deviation correction factors are attenuated and adjusted. Based on the attenuated performance characteristic curves and deviation correction factors, the original behavioral data is standardized.

[0103] When a V2G vehicle or V2G charging station experiences a deviation exceeding a preset threshold between its actual performance data and the current standardized processing results during multiple consecutive reverse power supply processes, the current event is marked as a potential fault, triggering a local data acquisition and expert system diagnostic process. Based on the diagnostic results of the process, the deviation correction factor is adjusted, and the original behavioral data is standardized based on the adjusted deviation correction factor.

[0104] Specifically, when a V2G vehicle or V2G charging station undergoes a firmware upgrade, its internal control logic, power output characteristics, or communication protocol may change. To ensure the accuracy of the ancillary service quality assessment, the system is designed to automatically trigger a performance data collection and comparison process. This process aims to acquire actual operating data after the upgrade and compare it with the performance benchmark before the upgrade, thereby generating performance comparison results that reflect the characteristics of the new firmware. Based on this performance comparison result, the system will generate entirely new performance characteristic curves and deviation correction factors to replace the old parameters. Subsequently, these new performance characteristic curves and deviation correction factors will be used to standardize the original behavioral data to ensure the real-time nature and accuracy of the assessment results.

[0105] Specifically, when the cumulative operating time of a V2G vehicle or V2G charging station reaches a preset threshold, or when a battery health decline exceeds a preset percentage, this typically indicates potential aging of the equipment. In response, the system will automatically initiate an aging assessment process. This process aims to evaluate the degree of performance degradation caused by aging through a series of diagnostic tests or data analysis. Based on the results of the aging assessment process, existing performance characteristic curves and deviation correction factors will be adjusted for degradation, for example, by reducing their output capacity or extending response time. The adjusted performance characteristic curves and deviation correction factors will then be used for subsequent standardization processing of raw behavioral data to accurately reflect the actual performance of the equipment under aging conditions.

[0106] In practical applications, when a V2G vehicle or V2G charging station experiences a deviation exceeding a preset threshold between its actual performance data and the current standardized processing results during multiple consecutive reverse power supply operations, this indicates a potential equipment malfunction. In this case, the system will mark the current event as a potential malfunction and immediately trigger a local data acquisition and expert system diagnostic process. Local data acquisition aims to obtain more detailed and accurate equipment operating parameters, while the expert system diagnostic process utilizes a preset fault knowledge base and diagnostic algorithms to analyze the acquired data to determine the type and cause of the potential malfunction. Based on the diagnostic results, the deviation correction factor will be adjusted accordingly to compensate for the performance deviation caused by the malfunction. The adjusted deviation correction factor will then be applied to the standardized processing of the original behavioral data, thus providing a relatively accurate ancillary service quality assessment even when a malfunction occurs.

[0107] Optionally, when the actual performance data of a V2G vehicle or V2G charging station deviates from the current standardized processing result by more than a preset threshold during multiple consecutive reverse power supply processes, the current event is marked as a potential fault, and a local data acquisition and expert system diagnostic process is triggered. The steps for adjusting the deviation correction factor based on the diagnostic results of the diagnostic process include:

[0108] Obtain the battery type of the V2G vehicle and the communication protocol type of the V2G charging station;

[0109] Collect real-time operational data from both V2G vehicles and V2G charging stations;

[0110] Based on the real-time operating data, identify abnormal data patterns of V2G vehicles or V2G charging stations;

[0111] Based on the abnormal data pattern, combined with the battery type of the V2G vehicle and the communication protocol type of the V2G charging pile, the abnormal data pattern is compared with a preset fault feature database to obtain the abnormal fault comparison result.

[0112] Based on the abnormal fault comparison results, the type of potential fault is determined;

[0113] Based on the type of potential fault, determine the corresponding adjustment strategy for the deviation correction factor;

[0114] The deviation correction factor is adjusted according to the adjustment strategy described above.

[0115] Specifically, acquiring the battery type of the V2G vehicle and the communication protocol type of the V2G charging station refers to the system first acquiring key technical information related to the V2G vehicle and V2G charging station that has deviated when the diagnostic process is initiated. The battery type of the V2G vehicle (e.g., lithium iron phosphate, ternary lithium, etc.) affects its performance degradation characteristics and fault manifestations, while the communication protocol type of the V2G charging station (e.g., CHAdeMO, CCS, etc.) determines the interaction method of data acquisition and control commands. This information forms the basis for subsequent fault diagnosis and deviation correction factor adjustment.

[0116] The collection of real-time operational data from both V2G vehicles and V2G charging stations can be understood as follows: upon identifying a potential fault, the system initiates local data collection, acquiring detailed, high-frequency real-time operational data from both the V2G vehicles (e.g., battery voltage, current, temperature, SOH, etc.) and the V2G charging stations (e.g., output power, charging current, communication status, internal error codes, etc.). This data serves as the direct basis for analyzing abnormal patterns and diagnosing faults.

[0117] In practical applications, identifying abnormal data patterns of V2G vehicles or V2G charging stations based on the real-time operating data involves processing the collected real-time operating data using data analysis algorithms (such as statistical analysis and machine learning models) to identify abnormal patterns that do not conform to the normal operating mode. For example, continuous voltage fluctuations, abnormal temperature increases, unstable power output, or communication interruptions may all be identified as abnormal data patterns.

[0118] Furthermore, based on the abnormal data pattern, and in conjunction with the battery type of the V2G vehicle and the communication protocol type of the V2G charging station, the abnormal data pattern is compared with a preset fault feature database to obtain abnormal fault comparison results. This means that the system will match the identified abnormal data pattern with a pre-established database containing various known fault characteristics. During the comparison process, the battery type of the V2G vehicle and the communication protocol type of the V2G charging station will be fully considered, because different types of devices may exhibit different data characteristics when the same fault occurs. For example, a certain battery type may exhibit a specific voltage curve when overcharged, while a certain communication protocol will have a specific error code when the connection is interrupted.

[0119] Therefore, based on the abnormal fault comparison results, the type of potential fault is determined. By comparing with the fault feature database, the system can identify the most matching fault type, such as increased battery internal resistance, charging module failure, communication module malfunction, sensor failure, etc.

[0120] Based on this, the adjustment strategy for the corresponding deviation correction factor is determined according to the type of potential fault. Different fault types have different impacts on the actual performance of V2G vehicles, thus requiring different deviation correction factor adjustment strategies. For example, if the performance degradation is caused by battery aging, it may be necessary to linearly reduce the deviation correction factor related to energy output; if it is due to communication delay at the charging station, it may be necessary to adjust the deviation correction factor related to response time.

[0121] Finally, the deviation correction factor is adjusted according to the aforementioned adjustment strategy. The system will precisely modify the deviation correction factor previously used for standardization processing based on the determined specific adjustment strategy to more accurately reflect the actual performance of V2G vehicles in the current state, thereby ensuring the accuracy of the ancillary service quality assessment.

[0122] Optionally, the steps for identifying abnormal data patterns of V2G vehicles or V2G charging stations based on real-time operational data include:

[0123] Collect real-time power grid status data from the park's power grid;

[0124] Preliminary anomaly pattern identification is performed on the real-time operating data to obtain preliminary anomaly markers;

[0125] The real-time power grid status data is analyzed to obtain power grid fluctuation characteristics.

[0126] The preliminary anomaly markers are compared with the power grid fluctuation characteristics to obtain the anomaly feature comparison results. If the anomaly feature comparison results indicate that the preliminary anomaly markers are correlated with the power grid fluctuation characteristics, the coupling degree between the real-time operation data of V2G vehicles or V2G charging piles and the power grid fluctuation characteristics is analyzed.

[0127] Based on the degree of coupling, the preliminary anomaly markers can be distinguished as either an abnormal data pattern caused by equipment failure or an abnormal data pattern caused by normal power grid fluctuations.

[0128] If the initial anomaly marker is an abnormal data pattern caused by equipment failure, then the abnormal data pattern is identified.

[0129] Specifically, collecting real-time grid status data from the park's power grid refers to acquiring real-time operating parameters such as voltage, current, frequency, and power factor of the power grid through various sensors and monitoring equipment deployed within the power grid. This data reflects the real-time health and fluctuations of the power grid. Preliminary anomaly pattern identification is performed on the real-time operating data to obtain preliminary anomaly markers, which can be achieved using statistical analysis methods, machine learning models, or rule-based expert systems. For example, when a performance parameter (such as output power or response time) of a V2G vehicle or V2G charging station repeatedly exceeds its normal operating range, it is marked as a preliminary anomaly. Grid fluctuation characteristic analysis is then performed on the real-time grid status data to obtain grid fluctuation characteristics, which can include spectrum analysis, trend analysis, and anomaly event detection of the grid data. For example, identifying instantaneous voltage drops, frequency shifts, or harmonic distortions in the power grid. The preliminary anomaly markers are compared with the grid fluctuation characteristics to obtain anomaly characteristic comparison results, aiming to determine whether the preliminary equipment anomaly is related to grid fluctuations. For example, if the time of the equipment anomaly highly overlaps with the time of the grid fluctuation event, a correlation may exist. If the anomaly comparison results indicate a correlation between the initial anomaly marker and grid fluctuation characteristics, further analysis is conducted to determine the degree of coupling between the real-time operational data of V2G vehicles or V2G charging stations and grid fluctuation characteristics. The analysis of coupling can involve methods such as time series correlation analysis, Granger causality tests, or mutual information calculation to quantify the extent to which equipment behavior is affected by grid fluctuations. Based on the degree of coupling, the analysis distinguishes between anomaly patterns caused by equipment failure or those caused by normal grid fluctuations. For example, a high degree of coupling suggests that the equipment anomaly is likely caused by grid fluctuations; a low degree of coupling suggests that the equipment anomaly is more likely caused by equipment failure. If the initial anomaly marker is identified as an anomaly pattern caused by equipment failure, this anomaly pattern is ultimately identified and used as input for subsequent expert system diagnosis.

[0130] Optionally, the preliminary anomaly markers are compared with grid fluctuation characteristics to obtain anomaly feature comparison results. If the anomaly feature comparison results indicate a correlation between the preliminary anomaly markers and grid fluctuation characteristics, then the steps for analyzing the coupling degree between the real-time operating data of V2G vehicles or V2G charging piles and grid fluctuation characteristics include:

[0131] Time synchronization calibration is performed on the real-time operation data of V2G vehicles or V2G charging piles and the real-time power grid status data of the park power grid.

[0132] Extract the device anomaly feature vector from real-time operating data after time synchronization calibration;

[0133] Extract the power grid fluctuation feature vector from real-time power grid status data;

[0134] Calculate the nonlinear correlation index between the equipment anomaly feature vector and the power grid fluctuation feature vector;

[0135] Determine whether the nonlinear correlation index exceeds a preset threshold, and obtain the index threshold determination result;

[0136] If the threshold judgment result indicates that the nonlinear correlation index exceeds the preset threshold, then analyze the consistency of the energy distribution of the abnormal equipment feature vector and the power grid fluctuation feature vector at different frequency components.

[0137] Analyze the time-delay characteristics of the equipment in response to power grid fluctuations;

[0138] Based on the consistency of energy distribution and time delay characteristics, the coupling degree between the real-time operating data of V2G vehicles or V2G charging piles and the power grid fluctuation characteristics is quantified.

[0139] Time synchronization calibration refers to ensuring that real-time data from V2G vehicles, V2G charging stations, and the park's power grid are aligned on the timeline using high-precision clock sources or Network Time Protocol (NTP), eliminating time deviations caused by data acquisition or transmission delays. Its purpose is to provide an accurate time reference for subsequent feature extraction and correlation analysis. Equipment anomaly feature vectors can be understood as a set of values ​​extracted from the real-time operating data of V2G vehicles or V2G charging stations, characterizing potential abnormal states of the equipment, such as instantaneous values, rates of change, or statistical characteristics of parameters like current, voltage, power, temperature, and state of health (SOH). Power grid fluctuation feature vectors are a set of values ​​extracted from the real-time power grid status data of the park's power grid, characterizing power grid fluctuation characteristics, such as frequency deviation, voltage sags / sags, harmonic content, and power fluctuations. The calculation of nonlinear correlation indices aims to measure whether a nonlinear correlation exists between the equipment anomaly feature vector and the power grid fluctuation feature vector. For example, methods such as mutual information, kernel function correlation analysis, or deep learning-based feature correlation models can be used for calculation. Its purpose is to discover complex correlations that may be missed by traditional linear correlation analysis. A preset threshold is a critical value determined based on historical data, expert experience, or simulation analysis. It is used to judge whether the nonlinear correlation index is high enough to indicate a significant correlation between equipment operating data and grid fluctuations. Energy distribution consistency analysis involves using signal processing techniques such as Fourier transform and wavelet transform to decompose the equipment anomaly feature vector and grid fluctuation feature vector into different frequency components, and then comparing their energy distributions across these components. Its purpose is to identify the frequency characteristics of equipment response to grid fluctuations. Time-delay characteristic analysis involves using methods such as cross-correlation functions or Granger causality tests to analyze whether changes in the equipment anomaly feature vector lag or lead the changes in the grid fluctuation feature vector in time. Its purpose is to determine the equipment's response speed and causal relationship to grid fluctuations. Quantitative coupling degree refers to comprehensively considering energy distribution consistency and time-delay characteristics, providing a numerical value or level to accurately describe the correlation strength and response pattern between the real-time operating data of V2G vehicles or V2G charging piles and grid fluctuation characteristics.

[0140] This application also discloses a park reverse charging energy trading management system for performing park reverse charging energy trading management, combined with... Figure 3 As shown, the park's reverse charging energy trading management system 1 includes:

[0141] The data synchronization acquisition module 11 is used to acquire power grid status data and V2G vehicle reverse power supply behavior data in the park.

[0142] The quality assessment execution module 12 is used to assess the quality of ancillary services provided by V2G vehicles based on the power grid status data and the V2G vehicle reverse power supply behavior data, and obtain ancillary service quality assessment results.

[0143] The battery loss assessment module 13 is used to acquire V2G vehicle battery information and assess the loss of V2G vehicle battery based on the V2G vehicle reverse power supply behavior data to obtain battery loss assessment results.

[0144] The real-time incentive generation module 14 is used to generate a real-time incentive unit price based on the auxiliary service quality assessment result, the battery loss assessment result, and the base electricity price.

[0145] The transaction clearing and settlement module 15 is used to obtain the actual power supply of the V2G vehicle and perform transaction clearing and settlement according to the real-time incentive unit price;

[0146] The incentive strategy modulation module 16 is used to adaptively adjust the incentive strategy parameters related to the generation process of the real-time incentive unit price based on V2G dispatch response data and power grid operation feedback after transaction clearing and settlement.

[0147] The data synchronization acquisition module is used to acquire power grid status data and V2G vehicle reverse power supply behavior data within the park. The specific methods for acquiring these data have already been described in the above embodiments and will not be repeated here. It is important to emphasize that the data synchronization acquisition module can be configured to acquire data through various interfaces and protocols. For example, this module can be a standalone hardware unit integrating wired or wireless communication interfaces to connect to smart meters, various sensors, and V2G charging piles within the park. This allows for real-time collection of power grid status data such as voltage, current, frequency, and load, and the reception of behavioral data uploaded by the V2G charging piles, including reverse power supply power, power supply duration, and power supply quantity for each V2G vehicle. As one implementation, the data synchronization acquisition module can be a software service running on a server, interacting with existing SCADA or EMS systems via standard APIs to acquire the required power grid status data. For V2G vehicle reverse power supply behavior data, this module can be designed to interact with the vehicle's OBD interface or an API provided by the vehicle manufacturer, directly acquiring power supply behavior information from the vehicle.

[0148] The quality assessment execution module is used to evaluate the quality of ancillary services provided by V2G vehicles based on grid status data and V2G vehicle reverse power supply behavior data, and obtain ancillary service quality assessment results. The specific methods for evaluating the quality of ancillary services provided by V2G vehicles have been described in the above embodiments and will not be repeated here. It is important to emphasize that the quality assessment execution module can be configured to execute a preset evaluation algorithm to quantify the quality of ancillary services provided by V2G vehicles. For example, this module can be a software component with an embedded rule-based evaluation engine. This engine compares and analyzes the real-time power supply data of V2G vehicles with grid status data based on predefined indicators such as the response speed of V2G vehicles, the stability of power supply, and the effect of regulating grid frequency or voltage, thereby calculating its contribution to grid stability. Specifically, the quality assessment execution module can quantify the frequency regulation service quality based on the power support provided by V2G vehicles when grid frequency fluctuates; or quantify the voltage support service quality based on the reactive power support provided when grid voltage is abnormal.

[0149] The battery degradation assessment module is used to acquire V2G vehicle battery information and assess the degradation of the V2G vehicle battery based on V2G vehicle reverse power supply behavior data, thereby obtaining a battery degradation assessment result. The specific methods for assessing V2G vehicle battery degradation have already been described in the above embodiments and will not be repeated here. It is important to emphasize that the battery degradation assessment module can be configured to acquire V2G vehicle battery information and assess V2G vehicle battery degradation based on V2G vehicle reverse power supply behavior data. For example, this module can be an independent computing unit that communicates with the vehicle's battery management system (BMS) to acquire information such as the battery's current state of charge (SOC), state of health (SOH), number of cycles, and temperature. Combining the V2G vehicle's reverse power supply behavior data, such as power supply power, power supply duration, and depth of charge / discharge, the battery degradation assessment module can use a preset battery aging model or empirical formula to calculate the impact of this reverse power supply on battery life, for example, estimating the amount of battery capacity degradation based on the number of charge / discharge cycles and the depth of each cycle.

[0150] The real-time incentive generation module generates a real-time incentive unit price based on the ancillary service quality assessment results, battery degradation assessment results, and the base electricity price. The specific method for generating the real-time incentive unit price has already been described in the above embodiments and will not be repeated here. It is important to emphasize that the real-time incentive generation module can be configured to generate a real-time incentive unit price based on the ancillary service quality assessment results, battery degradation assessment results, and the base electricity price. For example, this module can be a software service with embedded pricing logic. This logic sets a base electricity price and, based on the ancillary service quality assessment results output by the quality assessment execution module, increases the base electricity price to reward the high-quality ancillary services provided by V2G vehicles. Simultaneously, based on the battery degradation assessment results output by the battery degradation assessment module, a compensatory increase is made to the base electricity price to compensate for losses incurred by vehicle owners due to battery degradation. Specifically, the real-time incentive generation module can set an ancillary service contribution coefficient and a battery degradation compensation coefficient, and calculate the real-time incentive unit price by weighting the base electricity price with these two coefficients and the assessment results.

[0151] The transaction clearing and settlement module is used to obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price. The specific methods for transaction clearing and settlement have been described in the above embodiments and will not be repeated here. It is important to emphasize that the transaction clearing and settlement module can be configured to obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price. For example, this module can be an independent transaction processing unit that receives precisely measured actual power supply from each V2G vehicle during the reverse power supply period from the V2G charging pile. After the power supply ends, the transaction clearing and settlement module multiplies the actual power supply by the real-time incentive unit price to calculate the total revenue of this transaction. Then, the module can settle the revenue to the V2G vehicle owner through a preset payment interface, such as a mobile payment platform or bank transfer.

[0152] The incentive strategy modulation module is used to adaptively adjust the incentive strategy parameters related to the generation process of the real-time incentive unit price based on V2G dispatch response data and grid operation feedback after transaction clearing and settlement. The specific methods for adaptively adjusting the incentive strategy parameters have been described in the above embodiments and will not be repeated here. It is important to emphasize that the incentive strategy modulation module can be configured to adaptively adjust the incentive strategy parameters related to the generation process of the real-time incentive unit price based on V2G dispatch response data and grid operation feedback after transaction clearing and settlement. For example, this module can be an intelligent control unit that collects V2G dispatch response data such as the response time of V2G vehicles to dispatch commands and the deviation between actual power supply and commanded power, as well as grid operation feedback data such as the improvement in frequency and voltage stability of the grid after V2G vehicles provide reverse power supply. Based on this data, the incentive strategy modulation module can evaluate the effectiveness of the current incentive strategy. If it is found that the participation of vehicle owners is not high or the improvement in grid operation is not significant, the incentive strategy parameters can be adjusted, such as increasing the ancillary service contribution coefficient or the battery loss compensation coefficient, in order to achieve a better incentive effect.

[0153] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for managing reverse charging energy transactions in a park, characterized in that, include: Acquire data on the power grid status of the park and data on the reverse power supply behavior of V2G vehicles; Based on the power grid status data and the V2G vehicle reverse power supply behavior data, the quality of ancillary services provided by the V2G vehicle is evaluated, and the ancillary service quality evaluation result is obtained. Obtain V2G vehicle battery information and, based on the V2G vehicle reverse power supply behavior data, assess the V2G vehicle battery loss to obtain battery loss assessment results. Based on the ancillary service quality assessment results, the battery loss assessment results, and the base electricity price, a real-time incentive unit price is generated. Obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price; After transaction clearing and settlement, the incentive strategy parameters related to the generation process of the real-time incentive unit price are adaptively adjusted based on V2G dispatch response data and power grid operation feedback. The step of generating a real-time incentive unit price based on the ancillary service quality assessment results, the battery loss assessment results, and the base electricity price includes: Based on the power grid status data and the V2G vehicle reverse power supply behavior data, the urgency of the power grid for ancillary services is determined, and the contribution weight of the V2G vehicle is determined in combination with the urgency. Based on the V2G vehicle battery information and the V2G vehicle reverse power supply behavior data, the compensation weight for battery loss is determined. The base electricity price, the ancillary service quality assessment results, and the battery loss assessment results are weighted and summed according to the contribution weight and the compensation weight to obtain the initial real-time incentive unit price. The initial real-time incentive unit price is compared with the car owner's expected revenue to obtain the expected comparison result. The initial real-time incentive unit price is then adjusted based on the expected comparison result to obtain the real-time incentive unit price.

2. The method for managing reverse charging energy transactions in a park according to claim 1, characterized in that, The step of adaptively adjusting the incentive strategy parameters related to the generation process of the real-time incentive unit price, based on V2G dispatch response data and power grid operation feedback after transaction clearing and settlement, includes: Acquire the performance data of the reverse charging pile itself, similar data of nearby reverse charging piles, and power grid status data of nearby power grid sensors; A preliminary confidence assessment is conducted on the performance data of the reverse charging pile itself, the similar data of nearby reverse charging piles, and the power grid status data of nearby power grid sensors to obtain a preliminary confidence label. The preliminary confidence level is corrected based on contextual information to obtain the corrected confidence level. Based on the modified confidence level, the quality of auxiliary services provided by V2G vehicles is evaluated to obtain the auxiliary service quality evaluation results and generate a service premium factor. Based on the service premium factor, the ancillary service quality assessment results, the battery loss assessment results, and the base electricity price, and in conjunction with the corrected confidence level, a real-time incentive unit price is generated; The system displays the real-time incentive unit price and provides adjustment information for data points with a confidence level below a preset threshold.

3. The method for managing reverse charging energy transactions in a park according to claim 2, characterized in that, The steps of evaluating the quality of auxiliary services provided by V2G vehicles based on the modified confidence level, obtaining the auxiliary service quality evaluation result, and generating a service premium factor include: Identify the types of ancillary service needs and priorities of the power grid; Based on the V2G vehicle reverse power supply behavior data and the adjusted confidence level, evaluate the contribution score of V2G vehicles in each ancillary service demand type. Based on the priority of the auxiliary services, assign weights to each contribution score; Based on the weight of each contribution score, the weighted scores of each contribution are aggregated to obtain the comprehensive auxiliary service quality assessment result; Based on the comprehensive ancillary service quality assessment results and the urgency of the power grid for ancillary services, a service premium factor is generated.

4. The method for managing reverse charging energy transactions in a park according to claim 1, characterized in that, The step of evaluating the quality of ancillary services provided by V2G vehicles based on the power grid status data and the V2G vehicle reverse power supply behavior data, and obtaining the ancillary service quality evaluation result, includes: Obtain at least one of the following data from the V2G vehicle: brand, model, battery type, rated power, and maximum charge / discharge rate, and store it as vehicle technical parameters; Obtain at least one of the following data from the V2G charging pile: brand, model, maximum output power, communication protocol type, and control response delay, and store it as the charging pile's technical parameters. Based on the vehicle technical parameters and the charging pile technical parameters, adjust the weighting coefficients and benchmark thresholds in the auxiliary service quality assessment process; The performance data of V2G vehicles of different brands and models in the actual reverse power supply process were collected and compared with the actual needs and response effects of the power grid to obtain the performance comparison results. Based on the performance comparison results, performance characteristic curves and deviation correction factors are generated for each brand and model of V2G vehicle and charging pile combination. When evaluating the quality of auxiliary services provided by V2G vehicles based on the adjusted weighting coefficients and benchmark thresholds, the corresponding performance characteristic curves and deviation correction factors are invoked according to the brand and model of the V2G vehicle and the brand and model of the V2G charging pile. The original behavioral data is then standardized to obtain the auxiliary service quality evaluation results.

5. The method for managing reverse charging energy transactions in a park according to claim 4, characterized in that, The steps for standardizing the raw behavioral data based on the brand and model of the V2G vehicle and the brand and model of the V2G charging station, by calling the corresponding performance characteristic curves and deviation correction factors, to obtain the auxiliary service quality assessment results include: When a V2G vehicle or V2G charging station undergoes a firmware upgrade, it automatically triggers a collection and comparison of performance data. Based on the corresponding performance comparison results, it generates a new performance characteristic curve and a deviation correction factor, and then standardizes the original behavioral data based on the new performance characteristic curve and deviation correction factor. When the cumulative running time of a V2G vehicle or V2G charging station reaches a preset threshold, or when the battery health status is detected to have decreased by more than a preset percentage, an aging assessment process is automatically initiated. Based on the results of the aging assessment process, the existing performance characteristic curves and deviation correction factors are attenuated and adjusted. Based on the attenuated performance characteristic curves and deviation correction factors, the original behavioral data is standardized. When a V2G vehicle or V2G charging station experiences a deviation exceeding a preset threshold between its actual performance data and the current standardized processing results during multiple consecutive reverse power supply processes, the current event is marked as a potential fault, triggering a local data acquisition and expert system diagnostic process. Based on the diagnostic results of the process, the deviation correction factor is adjusted, and the original behavioral data is standardized based on the adjusted deviation correction factor.

6. The method for managing reverse charging energy transactions in a park according to claim 5, characterized in that, When a V2G vehicle or V2G charging station experiences a deviation exceeding a preset threshold between its actual performance data and the current standardized processing results during multiple consecutive reverse power supply processes, the current event is marked as a potential fault, triggering a local data acquisition and expert system diagnostic process. The step of adjusting the deviation correction factor based on the diagnostic results of the diagnostic process includes: Obtain the battery type of the V2G vehicle and the communication protocol type of the V2G charging station; Collect real-time operational data from both V2G vehicles and V2G charging stations; Based on the real-time operating data, identify abnormal data patterns of V2G vehicles or V2G charging stations; Based on the abnormal data pattern, combined with the battery type of the V2G vehicle and the communication protocol type of the V2G charging pile, the abnormal data pattern is compared with a preset fault feature database to obtain the abnormal fault comparison result. Based on the abnormal fault comparison results, the type of potential fault is determined; Based on the type of potential fault, determine the corresponding adjustment strategy for the deviation correction factor; The deviation correction factor is adjusted according to the adjustment strategy described above.

7. A method for managing reverse charging energy transactions in a park according to claim 6, characterized in that, The step of identifying abnormal data patterns of V2G vehicles or V2G charging stations based on the real-time operating data includes: Collect real-time power grid status data from the park's power grid; Preliminary anomaly pattern identification is performed on the real-time operating data to obtain preliminary anomaly markers; The real-time power grid status data is analyzed to obtain power grid fluctuation characteristics. The preliminary anomaly markers are compared with the power grid fluctuation characteristics to obtain the anomaly feature comparison results. If the anomaly feature comparison results indicate that the preliminary anomaly markers are correlated with the power grid fluctuation characteristics, the coupling degree between the real-time operation data of V2G vehicles or V2G charging piles and the power grid fluctuation characteristics is analyzed. Based on the degree of coupling, the preliminary anomaly markers can be distinguished as either an abnormal data pattern caused by equipment failure or an abnormal data pattern caused by normal power grid fluctuations. If the initial anomaly marker is an abnormal data pattern caused by equipment failure, then the abnormal data pattern is identified.

8. A method for managing reverse charging energy transactions in a park according to claim 7, characterized in that, The step of comparing the preliminary anomaly marker with the power grid fluctuation characteristics to obtain anomaly feature comparison results, and if the anomaly feature comparison results indicate a correlation between the preliminary anomaly marker and the power grid fluctuation characteristics, then the step of analyzing the coupling degree between the real-time operating data of V2G vehicles or V2G charging piles and the power grid fluctuation characteristics includes: Time synchronization calibration is performed on the real-time operation data of V2G vehicles or V2G charging piles and the real-time power grid status data of the park power grid. Extract the device anomaly feature vector from real-time operating data after time synchronization calibration; Extract the power grid fluctuation feature vector from real-time power grid status data; Calculate the nonlinear correlation index between the equipment anomaly feature vector and the power grid fluctuation feature vector; Determine whether the nonlinear correlation index exceeds a preset threshold, and obtain the index threshold determination result; If the threshold judgment result indicates that the nonlinear correlation index exceeds the preset threshold, then analyze the consistency of the energy distribution of the abnormal equipment feature vector and the power grid fluctuation feature vector at different frequency components. Analyze the time-delay characteristics of the equipment in response to power grid fluctuations; Based on the consistency of energy distribution and time delay characteristics, the coupling degree between the real-time operating data of V2G vehicles or V2G charging piles and the power grid fluctuation characteristics is quantified.

9. A park reverse charging energy trading management system, used to execute park reverse charging energy trading management, characterized in that, include: The data synchronization acquisition module is used to acquire power grid status data and V2G vehicle reverse power supply behavior data in the park. The quality assessment execution module is used to assess the quality of ancillary services provided by V2G vehicles based on the power grid status data and the V2G vehicle reverse power supply behavior data, and obtain the ancillary service quality assessment result. The battery loss assessment module is used to acquire V2G vehicle battery information and assess the battery loss of V2G vehicle based on the V2G vehicle reverse power supply behavior data, and obtain the battery loss assessment result. The real-time incentive generation module is used to generate a real-time incentive unit price based on the ancillary service quality assessment results, the battery loss assessment results, and the base electricity price. The step of generating a real-time incentive unit price based on the ancillary service quality assessment result, the battery loss assessment result, and the base electricity price includes: determining the urgency of the power grid for ancillary services based on the power grid status data and the V2G vehicle reverse power supply behavior data, and determining the contribution weight of the V2G vehicle based on the urgency; determining the compensation weight for battery loss based on the V2G vehicle battery information and the V2G vehicle reverse power supply behavior data; weighting and summing the base electricity price, the ancillary service quality assessment result, and the battery loss assessment result according to the contribution weight and the compensation weight to obtain an initial real-time incentive unit price; comparing the initial real-time incentive unit price with the vehicle owner's expected benefits to obtain an expected comparison result, and adjusting the initial real-time incentive unit price according to the expected comparison result to obtain the real-time incentive unit price; The transaction clearing and settlement module is used to obtain the actual power supply of V2G vehicles and perform transaction clearing and settlement based on the real-time incentive unit price. The incentive strategy modulation module is used to adaptively adjust the incentive strategy parameters related to the generation process of the real-time incentive unit price based on V2G dispatch response data and power grid operation feedback after transaction clearing and settlement.

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