Charging pile bidirectional energy management method and system for V2G scene

Abstract

The invention discloses a charging pile two-way energy management method and system for a V2G scene, and the method comprises the following steps: after a physical connection is established between a vehicle and a charging pile, executing the safety access handshake and user intention authentication, obtaining the state information of a vehicle battery, the user discharge intention and a minimum power-remaining threshold value, and carrying out the verification of the vehicle battery state information, the user discharge intention and the minimum power-remaining threshold value; the credibility of the user participating in the V2G is evaluated based on the historical travel data and the field behavior; a power grid side scheduling signal, a station load state and a vehicle dischargeable capability are accessed, a multi-target prediction scheduling engine is utilized to generate a short-term scheduling priority queue, the priority queue comprises an emergency frequency modulation response, a peak-valley arbitrage task and a station-level peak clipping task, and a scheduling instruction comprising a time window and a maximum charging and discharging amount is output. According to the invention, on the premise of ensuring user travel demands and battery life, valuable regulation service of a power grid is realized; according to the invention, through hierarchical coordination and high-performance power electronics, grid-connected impact and harmonic pollution are significantly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicle charging and smart grid interaction, in particular to a charging pile bidirectional energy management method and system for V2G scenarios. BACKGROUND

[0002] V2G (Vehicle-to-Grid) is a technology that enables bidirectional energy exchange between electric vehicles and the grid, which covers the G2V (Grid-to-Vehicle) process of charging vehicles from the grid, and the V2G (Vehicle-to-Grid) process of discharging vehicles to the grid, the latter of which can provide functions such as peak shaving, frequency regulation, and backup for the grid. To implement V2G, not only is a charging pile hardware with bidirectional energy flow capability required, but also a bidirectional energy management method (i.e. software and algorithms) that supports battery and vehicle health and grid demand.

[0003] The so-called "bidirectional energy management method for V2G scenarios" refers to a management method that, through algorithms, communication protocols, and control strategies, enables a charging pile to dynamically determine whether a vehicle is charging or discharging in reverse, and control the direction, amplitude, and timing of energy flow, according to grid demand, user demand, price changes, battery health, and vehicle-to-pole communication status. This method involves real-time control of bidirectional energy flow, energy allocation based on dispatch signals, vehicle-pole-grid three-end interaction, battery health management (SOH / SOC / SOE), grid connection safety and communication safety, and price and demand response optimization.

[0004] However, existing V2G bidirectional energy management technology still has many key technical problems in actual application, mainly in the following eight aspects: First, V2G dispatching demand is uncertain, and charging piles have difficulty obtaining high-reliability dispatching instructions. In existing technology, grid frequency regulation is typically on the order of milliseconds, price changes are on the order of minutes, and user departure behavior is unpredictable, with local load peak-valley differences varying unevenly. These factors make it difficult for charging piles to accurately respond to V2G instructions, making energy management strategies often lag or ineffective.

[0005] Second, it is difficult to balance user intent, battery health, and grid demand. Existing technology usually only focuses on grid dispatching demand and vehicle battery health (SOC / SOH), ignoring user travel strategies (such as whether they are willing to discharge, how much power they want to reserve, and when they want to leave). This one-sided management approach leads to obvious contradictions: excessive discharging can damage user range; excessive protection cannot provide sufficient V2G services; lack of trip prediction can lead to user complaints due to incorrect discharging.

[0006] Third, the battery degradation model lacks precision and cannot dynamically constrain V2G discharge depth. V2G discharge behavior can exacerbate battery aging, but existing battery degradation models are relatively crude and have weak applicability, making it difficult to assess the impact of battery health on V2G capacity in real time and lacking adaptive strategies for depth of discharge (DOD) control. This results in existing V2G operation modes that may compromise battery life, thereby reducing user willingness to participate.

[0007] Fourth, real-time control of bidirectional energy flow cannot meet the power quality requirements of the grid. Existing bidirectional power inverter modules have issues such as insufficient response speed, weak grid current harmonic suppression capability, and difficulty in maintaining stable power factor. In particular, in situations where the grid fluctuates greatly, multiple vehicles simultaneously output V2G, or the power electronic devices at the end of the pile lack adequate thermal management, the system has difficulty meeting the grid's requirements for low harmonics, low voltage flicker, and high response accuracy.

[0008] Fifth, coordinated control of multiple charging piles is difficult and can cause grid impact. In actual V2G scenarios, a site often contains dozens of charging piles, and multiple vehicles discharging in parallel can easily cause grid impact, making coordinated scheduling very difficult. Existing technologies lack power sharing strategies between multiple piles, hierarchical control architecture (station level-pile level-vehicle level), and dynamic constraints on grid point power limits, making it difficult to achieve coordinated discharge between multiple charging piles.

[0009] Sixth, communication protocols are not unified, and vehicle-pile-cloud information cannot form a closed loop. There is a serious problem of standard fragmentation in the industry, such as CHAdeMO, which supports V2G but lacks compatibility, ISO15118-20, which supports bidirectional charging but has low vehicle penetration, and private protocols from various manufacturers that are not fully compatible. This makes it difficult for vehicles to reliably report battery status and intentions, charging piles to accurately issue discharge instructions, and dispatching systems to control in real time, and the uncertainty at the communication layer makes it difficult for bidirectional energy management to form a closed loop.

[0010] Seventh, safety and privacy issues have not been systematically addressed. Existing solutions lack a blockchain-based transaction trust mechanism, dynamic permission control, and secure encryption transmission model. This poses multiple risks: on the grid side, improper V2G can cause reverse power; on the vehicle side, improper discharge can damage the BMS; on the data side, user travel, electricity price, and SOC data may be stolen; and on the control side, malicious control can cause large-scale power impact.

[0011] Eighth, the lack of optimization of the user, the grid, the operator of the three economic strategy. The benefit of V2G depends on the price model, the frequency market share, the user's willingness and the battery loss cost. The prior art has few methods to optimize the three interests comprehensively, lacks an algorithm for dynamically adjusting the strategy based on real-time electricity price, and the user incentive mechanism is not deeply bound with energy management. Therefore, a charging pile bidirectional energy management method and system for a V2G scenario are provided. SUMMARY

[0012] The purpose of the present application is to provide a charging pile bidirectional energy management method and system for a V2G scenario to solve the above problems existing in the prior art.

[0013] The first aspect: in order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is: a charging pile bidirectional energy management method for a V2G scenario, comprising the following steps: After the vehicle and the charging pile establish physical connection, perform safety access handshake and user intention authentication, obtain vehicle battery state information, user discharge willingness and minimum remaining power threshold, and evaluate the credibility of the user participating in V2G based on historical travel data and on-site behavior; Access the grid side dispatching signal, site load state and vehicle dischargeable capacity, use a multi-objective prediction scheduling engine to generate a short-term scheduling priority queue, the priority queue contains emergency frequency modulation response, peak-valley arbitrage task and station-level peak clipping task, and output a scheduling instruction containing time window and maximum charge-discharge capacity; Based on the hierarchical real-time control architecture, the station-level controller receives the scheduling instruction and issues the site total power limit, the charging pile local coordinator dynamically adjusts the upper and lower limits of the single pile power according to the real-time power in the field, and the vehicle-level execution layer executes the charge-discharge operation according to the issued strategy; Control the bidirectional converter in the charging pile to perform adaptive voltage and current control and harmonic suppression, respond to external frequency modulation instructions for closed-loop current injection or absorption, and record power quality indicators; Aggregate multiple charging piles and site energy storage systems into a virtual power plant unit, generate capacity summary and clearing strategy, preferentially schedule site energy storage to undertake high-frequency frequency modulation tasks, and schedule vehicles to undertake low-frequency discharge tasks.

[0014] Further preferably, the safety access handshake and user intention authentication comprises: Perform bottom-layer electrical safety detection, including insulation detection, contact resistance detection and ground detection; Exchange the vehicle voltage platform, state of charge and health state through a standardized communication protocol; If the user is not authorized to discharge or the evaluation result is unavailable, the system only allows one-way charging mode and prohibits bidirectional discharge mode.

[0015] Further preferably, the multi-target prediction scheduling engine adopts a conflict resolver to handle task conflicts: When the grid instruction, user contract agreement and battery health cost conflict, the weight of each factor is comprehensively calculated, the grid safety instruction and the user's minimum power demand are prioritized, and the final executable instruction set is output.

[0016] Further preferably, the charging pile local coordinator in the hierarchical real-time control architecture has a dynamic power peak shaving function: When it is detected that simultaneous discharging of multiple piles may cause voltage flicker at the grid connection point, the piles are controlled to gradually discharge or stop according to a predetermined smooth curve, and the grid voltage is maintained stable through time slice rotation output mode.

[0017] Further preferably, the bidirectional converter is integrated with adaptive control and protection functions: Supporting millisecond-level response to external frequency adjustment instructions; Having micro-short circuit detection and grid abnormality detection functions; Using soft switching technology to avoid switch impact, and cooperating with active filter to suppress harmonic components caused by multiple pile concurrency.

[0018] Further preferably, the virtual power plant unit also has off-grid operation capability: When grid anomalies are detected, automatically switch to local microgrid control mode, and use site energy storage and vehicle residual power to ensure power supply for local high-priority loads.

[0019] Further preferably, the privacy-protected online learning mechanism includes: The vehicle end and edge device use federated learning or differential privacy technology to upload only model update summaries to the cloud, not raw sensitive data; The cloud improves the user travel prediction model and battery aging estimation model based on the update summary, and pushes it to the edge device after A / B testing verification.

[0020] Further preferably, the dynamic pricing and revenue settlement includes: According to the grid compensation standard, discharge frequency and battery health cost, the compensation amount is calculated in real time, and is displayed through the user terminal; Based on smart contract, automatically execute revenue distribution and distribute the revenue to the user account.

[0021] Further preferably, the method further includes: updating the user travel prediction and battery aging model using the privacy-protected online learning mechanism, and recording the V2G transaction data based on the blockchain technology, and performing dynamic pricing and revenue settlement.

[0022] The second aspect: another technical solution adopted by the present application is: a charging pile bidirectional energy management system for a V2G scenario, applied to an energy management method, comprising: An access authentication module is configured to perform a secure access handshake and user intention authentication after a vehicle and a charging pile establish a physical connection, obtain vehicle battery state information, user discharge willingness and a minimum remaining power threshold, and assess the credibility of user participation in V2G based on historical travel data and on-site behavior; A prediction scheduling module is configured to access grid side scheduling signals, site load states and vehicle dischargeable capacity, generate a short-term scheduling priority queue by using a multi-objective prediction scheduling engine, the priority queue contains emergency frequency modulation response, peak-valley arbitrage tasks and station-level peak clipping tasks, and output scheduling instructions containing a time window and a maximum charge-discharge capacity; A hierarchical control module is configured to receive the scheduling instructions by a station-level controller based on a hierarchical real-time control architecture and issue a site total power limit, and a charging pile local coordinator dynamically adjusts the upper and lower limits of single pile power according to real-time power in the field, and a vehicle execution layer executes charge-discharge operations according to the issued strategy; A power electronic module is configured to control the bidirectional converter in the charging pile to perform adaptive voltage and current control and harmonic suppression, respond to external frequency modulation instructions for closed-loop current injection or absorption, and record power quality indicators; A market aggregation module is configured to aggregate multiple charging piles and site energy storage systems into a virtual power plant unit, generate capacity summary and clearing strategies, preferentially schedule site energy storage to undertake high-frequency frequency modulation tasks, and schedule vehicles to undertake low-frequency discharge tasks.

[0023] The third aspect: to solve the above technical problems, another technical solution adopted by the present application is: an electronic device, comprising a processor, a memory and a communication interface, the memory stores a computer program, and the processor executes the computer program to realize the steps of the above-mentioned any facing V2G scenario charging pile bidirectional energy management method.

[0024] The fourth aspect: to solve the above technical problems, another technical solution adopted by the present application is: a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the above-mentioned any facing V2G scenario charging pile bidirectional energy management method.

[0025] Compared with the prior art, the present application has the following advantages: The present application realizes valuable regulation services for the power grid under the premise of guaranteeing user travel demand and battery life; The present application significantly reduces grid connection impact and harmonic pollution through hierarchical coordination and high-performance power electronics; The application adopts site-level aggregation and VPP capability support to enter the capacity / frequency / energy market; The application adopts privacy-protected online learning and auditable settlement mechanism to improve user participation and ensure data security. The application adopts hierarchical architecture, modular hardware and standardized protocol to ensure good compatibility and deployment value of the scheme in the existing vehicle pile ecology. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0027] Figure 1 The figure is a flowchart of the charging pile bidirectional energy management method of the application facing the V2G scenario. Figure 2 The figure is a block diagram of the charging pile bidirectional energy management system of the application facing the V2G scenario. Figure 3 The figure is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] Figure 1 The figure is a flowchart of the charging pile bidirectional energy management method of the application facing the V2G scenario. It should be noted that the method of the present application is not limited to the order of the flowchart shown. Figure 1 Embodiment one

[0030] As Figure 1 The charging pile bidirectional energy management method of the application facing the V2G scenario includes the following steps: In step S10, after the vehicle and the charging pile establish physical connection, safety access handshake and user intention authentication are performed, vehicle battery state information, user discharge intention and minimum remaining power threshold are obtained, and the credibility of user participation in V2G is evaluated based on historical travel data and on-site behavior.

[0031] ​In the embodiments of the present disclosure, the secure access handshake and user intent authentication include: Performing underlying electrical safety detection, including insulation detection, contact resistance detection, and grounding detection; Exchanging vehicle voltage platform, state of charge, and state of health through a standardized communication protocol; If the user is not authorized to discharge or the evaluation result is unavailable, the system only allows one-way charging mode and prohibits two-way discharge mode.

[0032] In the present embodiment, when the new energy vehicle establishes physical connection with the charging pile, the system first enters the access handshake stage. This stage adopts a multi-level safety detection, communication verification, and permission authentication process, and the specific implementation manner is as follows: After detecting that the vehicle is plugged in, the charging pile automatically performs basic electrical safety inspection. The inspection content includes grounding continuity detection, insulation impedance detection, port temperature rise detection, and interface contact resistance judgment. If the inspection result does not meet the preset safety threshold, the system will refuse to enter the next step and prompt the user to handle it.

[0033] After the physical layer detection is qualified, the charging pile and the vehicle establish a communication channel through the vehicle-pile communication protocol supporting ISO 15118, DIN SPEC, or national standards. This channel is used to exchange key state parameters of the vehicle, including vehicle voltage platform, current state of charge of the battery, health status summary, maximum allowed charging and discharging power, and battery threshold values that need to be protected.

[0034] The charging pile reads the user's authorization settings for this session through interaction with the cloud or local APP interface. The content includes: whether the vehicle is allowed to participate in V2G discharge, the user's set minimum remaining power threshold, the user's estimated departure time, and the user's income sharing preference (such as whether to start the discharge reward).

[0035] The system is internally configured with an "intent credibility evaluator" to check the credibility of the user's instruction to allow V2G. The evaluation process refers to the following dimensions: the user's regular driving path and travel time regularity, whether the vehicle is a shared vehicle (such as a ride-hailing or freight vehicle), whether there has been a history of canceling discharge behavior, and whether the user's identity authentication meets the safety policy. The evaluator determines whether to allow participation in discharge this time according to the result, and can automatically reduce the discharge power or shorten the discharge duration.

[0036] Based on the vehicle state, user authorization, and credibility results, the system sets a safety policy for this session, including: only allowing charging, allowing charging and small power discharge, allowing full V2G discharge task, or prohibiting any energy exchange (for example, when a hazard risk is detected).

[0037] All access data, authorization information, and policy-making results are recorded in the local trusted execution environment and synchronized to the cloud as the basis for subsequent V2G transaction settlement, responsibility identification, and abnormal source tracing.

[0038] For example, in the scenario of a public charging station in a city business district, a user's vehicle enters the charging station at 17:00 in the afternoon.

[0039] The charging pile detects that the grounding continuity is normal, the interface insulation resistance meets the standard, and there is no overheating sign at the gun port, allowing the communication phase to enter.

[0040] The vehicle return information shows that the current SOC is 72%, the SOH is 96%, the current maximum dischargeable power is 18kW, and the minimum protection power is 30%.

[0041] The user sets the APP to allow V2G, with a minimum SOC retention of 45%, plans to leave at 19:00, and selects the "participate in peak clipping and frequency modulation to obtain compensation" mode.

[0042] The system queries historical information to show that the user usually leaves at 18:50, has no temporary cancellation history, and is a private vehicle, determining that it is a "highly trusted intention".

[0043] The system sets the maximum dischargeable amount to 27% of the current energy storage.

[0044] Considering that the site power grid needs 100kW peak clipping support in the evening, the vehicle is granted the "permission to participate in medium-power V2G discharge" and the maximum discharge power is limited to 10kW.

[0045] The above information is recorded in the TEE and the cloud.

[0046] In step S20, the grid side dispatching signal, site load state, and vehicle dischargeable capacity are accessed, and a multi-objective predictive scheduling engine is used to generate a short-term scheduling priority queue, which includes emergency frequency modulation response, peak valley arbitrage tasks, and site level peak clipping tasks, and outputs a scheduling instruction containing a time window and a maximum charge and discharge amount.

[0047] In the embodiments of the present disclosure, the multi-objective predictive scheduling engine uses a conflict resolver to handle task conflicts: When the grid instruction, user contract agreement, and battery health cost conflict, the weights of each factor are calculated comprehensively, the grid safety instruction and the user's minimum power retention requirement are prioritized, and the final executable instruction set is output.

[0048] In this embodiment, a set of multi-dimensional energy flow constraint parameters is constructed, and the system organizes all parameters that may affect the power flow into a calculable constraint set, including: Vehicle layer constraints: SOC range, allowed charge and discharge power, temperature, reservation travel time window, etc.

[0049] Lifetime limit, over-discharge limit, charge-discharge rate limit.

[0050] Maximum input / output power, temperature limit, protection device action threshold.

[0051] Transformer load rate, allowed feedback power, load peak shaving demand, time-of-use electricity price.

[0052] Minimum available power, maximum allowed V2G energy, benefit preference.

[0053] The system calculates the current allowable maximum charging power range and maximum discharging power range based on the above constraints, and the unexecutable power interval.

[0054] For example, if the maximum allowed reverse power of the power distribution network is 10 kW, and the vehicle allows discharging capacity of 20 kW, the system takes 10 kW as the upper limit of execution.

[0055] The system takes the signal from the power distribution network dispatching side as the first priority decision basis (such as peak shaving signal priority discharging, valley filling signal priority charging), combined with the electricity price fluctuation trend. However, the system must meet the rigid constraints of the vehicle and user side, such as refusing to discharge request when the SOC is close to the minimum threshold.

[0056] The system generates a strategy including power flow direction (charging or discharging), executable power value (such as discharging 7.3 kW), and power execution mode (constant, step, ramp, or dynamic response mode), and contains auxiliary information such as safety check parameters.

[0057] Before the strategy is issued, the system performs a quick verification, including whether it causes battery overheating, triggers protection, exceeds the power distribution network limit, violates user intention, or affects travel demand.

[0058] For example: At 6 pm, the resident community power consumption peak period, the power grid requests 5~15 kW peak shaving. The vehicle SOC is 70% (allowing discharging to 55%), the maximum discharging capacity is 20 kW; the charging pile feedback upper limit is 12 kW; the transformer allows injection of maximum 8 kW; the user allows a maximum contribution of 10 kW; the electricity price is at peak time.

[0059] After the system comprehensively considers the constraints, the maximum executable discharging power is 8 kW. Due to the power grid peak shaving request, user intention, and high electricity price, the system determines the flow direction to be discharging, the execution power to be 8 kW, and the continuous output mode to be adopted. After verification that there is no safety risk, the system issues the instruction "start discharging to the power grid at 8 kW".

[0060] In step S30, based on the hierarchical real-time control architecture, the station-level controller receives the scheduling instruction and issues a site total power limit, the charging pile local coordinator adjusts the single pile power upper and lower limits according to the real-time power in the field, and the vehicle-level execution layer executes the charging and discharging operation according to the issued strategy.

[0061] In the embodiments of the present disclosure, the charging pile local coordinator in the hierarchical real-time control architecture has a dynamic power peak clipping function. When it is detected that simultaneous discharging of multiple piles may cause voltage flicker at the grid connection point, the piles are controlled to gradually discharge or be cut off according to a predetermined smooth curve, and the grid voltage is maintained stable through time slice rotation output.

[0062] It is responsible for interacting with the grid dispatching center, receiving peak clipping and valley filling requests, issuing site total power limits, preventing transformer overload, and having a millisecond-level heartbeat mechanism.

[0063] The single pile power is determined under the station-level limit, the pile constraints are processed, the time slice rotation or priority scheduling is adopted, and the dynamic smooth control is provided.

[0064] According to the instruction execution power, the BMS is responsible for safety judgment, real-time feedback of state, and built-in overcurrent and overtemperature protection.

[0065] The station level issues overall targets (such as 50kW of injection within 10 minutes); the pile-level coordinator decomposes tasks according to the state of each pile (such as temperature, idle condition), and introduces a smooth curve to avoid sudden changes; and the vehicle-level execution layer follows the instructions for execution, and uploads the state in real time to realize closed loop.

[0066] It has a pile-level fault rapid cut-out mechanism, a vehicle active exit mechanism (BMS determines unsafe), and a power grid emergency signal priority mechanism (rapid response in emergency peak clipping).

[0067] For example, a small V2G station beside a subway station has four bidirectional piles, the upper limit of the inverter is 60kW, the grid requests 50kW of peak clipping, and the station level issues a target of 50kW.

[0068] The pile-level coordination allocation is as follows: pile A (normal) 15kW, pile B (temperature slightly high) 10kW, pile C (idle) 15kW, and pile D (just switched) slow start to 10-12kW. The comprehensive allocation meets 50kW, and each pile climbs according to a smooth curve. If the transformer load approaches the upper limit, the station level issues a reduction instruction, and the pile level reallocates.

[0069] In step S40, the bidirectional converter in the charging pile is controlled to perform adaptive voltage and current control and harmonic suppression, respond to external frequency modulation instructions for closed-loop current injection or absorption, and record power quality indicators.

[0070] In the embodiments of the present disclosure, the bidirectional converter is integrated with adaptive control and protection functions. Supporting millisecond-level response to external frequency adjustment instructions. Possessing micro-short circuit detection and grid-connected abnormality detection functions. Using soft switching technology to avoid switching impact, and cooperating with active filter to suppress harmonic components caused by multiple piles.

[0071] The embodiment configures an integrated bidirectional conversion module, which has rectification and inversion functions, supports power smooth adjustment and rapid energy reversal. The controller uses millisecond-level sampling for real-time adjustment, ensuring that the direction conversion has no impact.

[0072] Real-time monitoring of distortion, injection of reverse compensation current to eliminate high-order harmonics.

[0073] During discharging, the inverter and the grid are completely synchronized in voltage phase, frequency and amplitude, and are re-locked in milliseconds.

[0074] Automatically adjusting the power factor according to the grid demand.

[0075] Possessing rapid circuit breaking protection (abnormal current is immediately disconnected), soft switching technology (to avoid current mutation), and grid-connected abnormality detection (voltage drop, frequency deviation, etc. to automatically reduce power or stop).

[0076] Using a unified grid-connected point power scheduling interface to limit the total amount and the rising speed, sharing active filtering strategies, and power climbing strategies to avoid harmonic superposition and oscillation.

[0077] For example, at 8 pm, the grid frequency drops, requesting 30 kW of active support. The station-level allocates 7.5 kW per pile (4 piles in total). The pile-level issues inversion instructions, the converter enters inversion mode, completes phase synchronization and suppresses harmonics, and completes switching within hundreds of milliseconds, with no obvious voltage flicker.

[0078] In step S50, a plurality of charging piles and a station energy storage system are aggregated into a virtual power plant unit, capacity aggregation and clearing strategies are generated, the station energy storage is preferentially dispatched to undertake high-frequency frequency modulation tasks, and the vehicle is dispatched to undertake low-frequency discharging tasks.

[0079] In the embodiments of the present disclosure, the virtual power plant unit also has off-grid operation capability: When detecting grid abnormalities, automatically switch to local microgrid control mode, and use station energy storage and vehicle residual capacity to ensure power supply for local high-priority loads.

[0080] The system collects all adjustable resources (charging piles, vehicle batteries, station energy storage ESS, photovoltaic and loads), establishes an "adjustable capacity pool". A priority mechanism is used: ESS is preferentially used to participate in high-frequency frequency modulation tasks; vehicle batteries are responsible for low-frequency, predictable peak clipping and valley filling tasks.

[0081] Responsible for aggregating capacity, dynamically generating capacity reporting, and managing risk. The system monitors grid demand, electricity price, load, and vehicle departure time, generates capacity declaration, task clearing, and actual execution strategy.

[0082] Intelligent contract is used to record vehicle energy contribution, time period, and minimum energy setting, etc. The protocol is automatically generated and settled after the task, and the income is automatically distributed.

[0083] It has island operation mode (off-grid operation when grid failure), local microgrid regulation (supporting critical load) and automatic reconnection function.

[0084] For example: the parking lot of a shopping mall is networked as a VPP. The grid requests 200kW peak shaving during the morning peak. The VPP calculates that the ESS can provide 120kW and the vehicles can provide 80kW. The system generates a contract and executes it, and if the vehicle leaves, the ESS output is increased to compensate. When the grid fails, it enters island mode to maintain lighting and monitoring power supply.

[0085] In other embodiments, the method further comprises updating user travel prediction and battery aging models using a privacy-protected online learning mechanism, and recording V2G transaction data based on blockchain technology, and performing dynamic pricing and income settlement In this embodiment, specifically, the privacy-protected online learning mechanism includes: The vehicle end and the edge device use federated learning or differential privacy technology to upload only model update summaries to the cloud, not raw sensitive data; The cloud improves the user travel prediction model and battery aging estimation model based on the update summary, and pushes it to the edge device after A / B testing verification.

[0086] In this embodiment, specifically, the dynamic pricing and income settlement includes: The compensation amount is calculated in real time according to the grid compensation standard, discharge frequency and battery health cost, and displayed through the user terminal; Based on the smart contract, the income distribution is automatically executed, and the income is distributed to the user account; Deploy an agent in the controller for local data collection and caching (without uploading raw data), execute local training to generate model gradients or summaries, and upload them after differential privacy processing and gradient compression through the security module.

[0087] Deploy a federated learning coordinator to manage models, aggregate parameters based on FedAvg / FedProx, evaluate client contribution, and evaluate model quality and rollback.

[0088] Deploy a trusted ledger layer to record tamper-proof V2G transaction data, user authorization, and federated learning contribution. Smart contracts automatically execute settlement, incentive distribution, and exception rollback, enabling cross-agency audits.

[0089] Build an economic incentive model: including dynamic price compensation calculation (based on real-time price, load, and battery wear cost), user contribution reward (binding federated learning participation), and incentive visualization and user selectability (converted to cash / points, etc.).

[0090] New strategies need to go through gray release (such as 10% of users), monitor key indicators, automatically rollback in case of exceptions, and finally release to the entire network.

[0091] For example: In a 30-day pilot in a certain city, 500 vehicles participate. After charging daily, the vehicle side trains locally and uploads summaries, and the original data does not leave the vehicle. The cloud-side aggregation improves user off-site prediction accuracy by 15%. The vehicle owner discharges 6kWh during peak hours, and the smart contract records the income (such as discharge income and wear compensation), and the system automatically settles 15 yuan. Users can see the income in real time through the APP, and the willingness to participate increases by 17%. Embodiment Two

[0092] As Figure 2 shown to solve the above technical problems, on the basis of embodiment one, another technical solution adopted by the present application is: a charging pile bidirectional energy management system for V2G scenarios, comprising: An access authentication module is configured to perform a secure access handshake and user intent authentication after a vehicle and a charging pile establish a physical connection, obtain vehicle battery state information, user discharge willingness, and minimum remaining power threshold, and assess the credibility of users participating in V2G based on historical travel data and on-site behavior; A prediction and scheduling module is configured to access grid-side scheduling signals, site load status, and vehicle dischargeable capacity, generate a short-term scheduling priority queue using a multi-objective prediction and scheduling engine, the priority queue containing emergency frequency modulation response, peak-valley arbitrage tasks, and site-level peak shaving tasks, and output scheduling instructions containing time windows and maximum charge and discharge capacity; A hierarchical control module is configured to receive the scheduling instructions and issue site-level controller total power limits based on a hierarchical real-time control architecture, and local coordinators adjust single-pile power upper and lower limits based on real-time power within the site, and vehicle-level execution layers perform charge and discharge operations according to the issued strategies; A power electronic module is configured to control the bidirectional converter in the charging pile to perform adaptive voltage and current control and harmonic suppression, respond to external frequency modulation instructions for closed-loop current injection or absorption, and record power quality indicators; The market aggregation module is configured to aggregate the plurality of charging piles and the site energy storage system as a virtual power plant unit, generate a capacity summary and a clearing strategy, and preferentially schedule the site energy storage to undertake a high-frequency frequency modulation task and schedule the vehicle to undertake a low-frequency discharge task.

[0093] For other details of the implementation of the technical solutions of the modules in the system of the above embodiment one, refer to the description in the method for bidirectional energy management of charging piles in a V2G scenario in the above embodiment, which will not be repeated here.

[0094] It should be noted that each of the embodiments in the present specification adopts a progressive manner for description, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the part of the method embodiment. Embodiment three

[0095] As Figure 3 A structural schematic diagram of an electronic device is provided for the embodiments of the present disclosure. It shows a structural schematic diagram suitable for implementing the electronic device in the embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

[0096] As Figure 3 An electronic device is shown, which includes a processor, a memory and a communication interface. The memory stores a computer program, and the processor executes the computer program to implement the method for bidirectional energy management of charging piles in a V2G scenario of the embodiments of the present disclosure. The electronic device can exchange data with other devices or systems through the communication interface to realize real-time updating and sharing of drug information.

[0097] The processor in the above electronic device is the core of the electronic device. The processor is responsible for executing the computer program stored in the memory to realize various functions of the intelligent diagnosis method of the paperless conference terminal. The processor can use a high-performance multi-core CPU or a special-purpose chip to meet the needs of complex calculations and real-time processing. The memory is used to store the operating system, application programs, data and computer programs, etc. In the present embodiment, the memory stores the computer program for implementing the intelligent diagnosis method of the paperless conference terminal. The memory can be RAM, ROM, Flash memory or other types of non-volatile memory. The communication interface is used to connect the electronic device with other devices or networks to realize the transmission and exchange of data. In the present embodiment, the communication interface supports multiple communication protocols and interface standards, such as Wi-Fi, Bluetooth, USB, Ethernet, etc., to meet the communication needs in different scenarios.

[0098] Detailed description of the present embodiment can refer to the corresponding description in the foregoing embodiments, which will not be repeated here. Embodiment four

[0099] According to the embodiment of the present disclosure, a computer readable storage medium is provided. The computer readable storage medium stores a computer program. When the computer program is executed by a processor, each function of the V2G scenario-oriented charging pile bidirectional energy management method of the present disclosure is realized.

[0100] The computer readable storage medium includes, but is not limited to, an optical storage medium (for example, CD-ROM and DVD), a magneto-optical storage medium (for example, MO), a magnetic storage medium (for example, a magnetic tape or a mobile hard disk), a medium with a built-in rewritable non-volatile memory (for example, a memory card), and a medium with a built-in ROM (for example, a ROM cartridge).

[0101] Detailed description of the present embodiment can refer to the corresponding description in the foregoing embodiments, which will not be repeated here.

[0102] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A bidirectional energy management method for charging piles in V2G scenarios, characterized in that, The method comprises the following steps: After the vehicle establishes a physical connection with the charging pile, a secure access handshake and user intention authentication are performed, vehicle battery state information, user discharge willingness, and minimum remaining power threshold are obtained, and the credibility of the user participating in V2G is evaluated based on historical travel data and on-site behavior; Access to grid-side dispatching signals, site load status, and vehicle dischargeable capacity, a multi-objective predictive scheduling engine is used to generate a short-term scheduling priority queue, which includes emergency frequency modulation response, peak-valley arbitrage tasks, and station-level peak clipping tasks, and output scheduling instructions containing time windows and maximum charge-discharge capacity; Based on a hierarchical real-time control architecture, the station-level controller receives the scheduling instructions and issues a site total power limit, the charging pile local coordinator dynamically adjusts the single-pile power upper and lower limits based on real-time power in the field, and the vehicle execution layer performs charge-discharge operations according to the issued strategy; The bidirectional converter in the charging pile performs adaptive voltage and current control and harmonic suppression, responds to external frequency modulation instructions for closed-loop current injection or absorption, and records power quality indicators; A plurality of charging piles and site energy storage systems are aggregated into a virtual power plant unit to generate capacity summaries and clearing strategies, with station energy storage prioritized to undertake high-frequency frequency modulation tasks, and vehicles prioritized to undertake low-frequency discharge tasks.

2. The method of claim 1, wherein, The secure access handshake and user intention authentication comprises: Performing bottom-layer electrical safety detection, including insulation detection, contact resistance detection, and ground detection; Exchanging vehicle voltage platform, state of charge, and health status through standardized communication protocols; If the user is not authorized to discharge or the evaluation result is unavailable, the system only allows one-way charging mode and prohibits two-way discharge mode.

3. The method of claim 1, wherein, The multi-objective predictive scheduling engine uses a conflict resolver to handle task conflicts: When the grid instruction, user contract agreement, and battery health cost conflict, the weights of each factor are calculated comprehensively to prioritize grid safety instructions and user minimum remaining power requirements, and output the final executable instruction set.

4. The method of claim 1, wherein, The charging pile local coordinator in the hierarchical real-time control architecture has a dynamic power peak clipping function: When it is detected that simultaneous discharge of multiple piles may cause grid point voltage flicker, each pile is controlled to discharge or be cut off gradually according to a predetermined smoothing curve, and the grid voltage is maintained stable through time slice rotation output.

5. The method of claim 1, wherein, The bidirectional converter integrates adaptive control and protection functions: Supporting millisecond-level response to external frequency adjustment instructions; Having micro-short circuit detection and grid abnormality detection functions; Using soft switching technology to avoid switch impact and cooperating with an active filter to suppress harmonic components caused by multiple pile concurrency.

6. The method of claim 1, wherein, The virtual power plant unit also has off-grid operation capability: When grid abnormalities are detected, it automatically switches to local microgrid control mode and uses site energy storage and vehicle residual power to ensure power supply for local high-priority loads.

7. The method of claim 1, wherein, The method further comprises updating user travel prediction and battery aging models using a privacy-protected online learning mechanism, recording V2G transaction data based on blockchain technology, and performing dynamic pricing and revenue settlement.

8. The method of claim 7, wherein, The privacy-protected online learning mechanism comprises: The vehicle end and edge device use federated learning or differential privacy technology to upload only model update summaries to the cloud, not raw sensitive data; The cloud improves the user travel prediction model and the battery aging estimation model based on the update summary, and pushes them to the edge device after A / B testing.

9. The method of claim 7, wherein, The dynamic pricing and revenue settlement includes: According to the grid compensation standard, the discharge frequency and the battery health cost, the compensation amount is calculated in real time, and is displayed through the user terminal; Based on the smart contract, the income distribution is automatically executed, and the income is distributed to the user account.

10. A charging pile bidirectional energy management system for V2G scenarios, applied to the energy management method of any one of claims 1-9, characterized in that, It includes: The access authentication module is configured to perform security access handshake and user intent authentication after the vehicle and the charging pile establish physical connection, obtain vehicle battery state information, user discharge willingness and minimum remaining power threshold, and evaluate the credibility of user participation in V2G based on historical travel data and on-site behavior; The prediction scheduling module is configured to access the grid side scheduling signal, the station load state and the vehicle dischargeable capacity, generate a short-term scheduling priority queue using a multi-objective prediction scheduling engine, the priority queue contains emergency frequency modulation response, peak valley arbitrage task and station level peak clipping task, and output scheduling instructions containing time window and maximum charge and discharge capacity; The hierarchical control module is configured to receive the scheduling instructions by the station level controller based on the hierarchical real-time control architecture, and publish the total power limit of the station, the local coordinator of the charging pile adjusts the upper and lower limits of the single pile power according to the real-time power in the field, and the vehicle level execution layer executes the charge and discharge operation according to the issued strategy; The power electronic module is configured to control the bidirectional converter in the charging pile to perform adaptive voltage and current control and harmonic suppression, respond to external frequency modulation instructions for closed-loop current injection or absorption, and record power quality indicators; The market aggregation module is configured to aggregate multiple charging piles and station energy storage systems into virtual power plant units, generate capacity summary and clearing strategies, preferentially schedule station energy storage to undertake high-frequency frequency modulation tasks, and schedule vehicles to undertake low-frequency discharge tasks.

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