Charging and discharging control guide generation method and system for v2g charging pile

Abstract

The application relates to the technical field of charging and discharging of electric vehicle charging piles, and discloses a charging and discharging control guide generation method and system of a V2G charging pile, which comprises the following steps: S1, collecting, in real time, power grid side operation data, charging pile group state data, in-network vehicle battery state data and user willingness parameters; wherein the power grid side operation data comprises real-time load of a transformer area, rated capacity of a transformer, peak-valley time-of-use electricity price, power grid allowed reverse feedback margin, voltage / current, power grid dispatching instruction and load rate; the charging pile group state data comprises online and offline state of each pile, maximum charging / discharging power of a single pile, equipment fault state and communication link state. The scheme overcomes one-sidedness of traditional single-target scheduling by constructing a multi-target optimization model covering power grid operation, user economy and battery health, can realize collaborative optimization of the interests of the three parties, effectively improves the enthusiasm of users participating in V2G, and simultaneously improves the regulation and control effect of the transformer area power grid.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging pile charging and discharging technology, and in particular to a charging and discharging control guidance generation method and system for V2G charging piles. Background Technology

[0002] With the continuous increase in the number of new energy vehicles, the problem of load fluctuation in the distribution network in the substations is becoming increasingly prominent. V2G bidirectional charging piles are key hardware for achieving coordinated regulation between vehicles and the grid. When vehicles are idle, they can serve as distributed energy storage, relieving grid pressure during peak hours and storing electrical energy during off-peak hours. Vehicle owners can also earn subsidies by relying on the peak-valley electricity price difference.

[0003] However, upon implementation on-site, it was discovered that the existing dispatching solutions on the market had significant flaws:

[0004] 1. Most optimization algorithms focus on a single metric, either solely on grid peak shaving or only on vehicle owner benefits, failing to simultaneously consider grid safety, vehicle operating costs, and battery life. Field trials often reveal two extremes: prioritizing profit from excessive discharge leads to rapid battery degradation, while strict control of battery wear leaves vehicle owners with no profit, resulting in low participation.

[0005] 2. The algorithm weights are all hardcoded in the program and will not automatically adjust according to the load of the transformer area, real-time electricity price, and vehicle SOC. When encountering sudden situations such as a sudden increase in load or temporary fluctuation in electricity price, the control effect will be directly reduced, and the adaptability is very poor.

[0006] To address these technical issues, we propose a V2G charge / discharge control guidance generation method and system. Summary of the Invention

[0007] To address the aforementioned issues, this application provides a method and system for generating charging and discharging control guidance for V2G charging piles.

[0008] On the one hand, the charging and discharging control guidance generation method for V2G charging piles provided in this application includes the following steps:

[0009] S1. The data acquisition layer collects real-time power grid operation data, charging pile group status data, connected vehicle battery status data, and user preference parameters.

[0010] Among them, the grid-side operation data includes real-time load of distribution areas, rated capacity of transformers, peak-valley time-of-use electricity price, grid allowable reverse feed-in margin, voltage / current, grid dispatch instructions, and load factor;

[0011] The charging pile group status data includes the online / offline status of each pile, the maximum charging / discharging power of a single pile, the equipment fault status, and the communication link status.

[0012] Battery status data for vehicles connected to the network includes real-time SOC, rated battery capacity, battery temperature, current maximum charge / discharge rate, remaining parking time, user-set target SOC, and battery cycle life parameters for each vehicle. , ;

[0013] User preference parameters include whether V2G discharge is allowed, selection mode (profit priority / battery priority / balanced mode), minimum reserved power, and whether to participate in grid emergency response.

[0014] The purpose is to solve the problem that traditional scheduling only collects single data points, resulting in incomplete information and inaccurate control.

[0015] S2. Construct a multi-objective optimization guided model, with the optimization objectives being to minimize grid load fluctuation, minimize overall user cost, and minimize battery life loss. Establish the objective function as follows:

[0016]

[0017] In the formula, The minimum value of the overall function represents the optimal comprehensive effect. This is a load fluctuation evaluation function, specifically a grid-side evaluation function, used to measure grid load fluctuations. This is a user economic cost function that measures the cost of income and expenditure for car owners. This is a battery degradation cost function that measures battery aging and degradation. , , These are the dynamic weight coefficients for the corresponding objectives, and satisfy the following conditions: ;

[0018] when At that time, 50% of the priority is given to grid stability, and the remaining 50% is allocated to vehicle owner benefits and battery protection.

[0019] S3. Based on the adaptive weight adjustment algorithm, the weight coefficients of each target are dynamically updated according to the real-time grid load rate, peak and valley electricity price periods, and battery SOC range.

[0020] S4. Under the conditions of satisfying power constraints, SOC constraints, and time constraints, solve the multi-objective optimization guidance model to generate the charging and discharging power guidance sequence for each vehicle corresponding to each charging pile.

[0021] Power constraints: Maximum power of a single pile is 7kW / 20kW, and charging and discharging beyond the specified power is not allowed;

[0022] SOC constraint: The battery level cannot be lower than 20% or higher than 95%, and the battery level must be greater than or equal to the set target SOC when the user leaves.

[0023] Time constraints: Vehicles only participate in dispatching during parking periods and cease discharging before leaving the grid;

[0024] S5. The charging and discharging power guidance sequence is sent to each V2G charging pile through the vehicle-to-charging-pile communication link, and the charging pile executes the corresponding charging and discharging control.

[0025] As a preferred embodiment of the above technical solution, in S2, the load fluctuation evaluation function The expression is:

[0026]

[0027] In the formula, The total number of scheduling periods. For the t-th scheduling time slice, The base load power of the transformer area during time period t. Let represent the charging and discharging power of the i-th electric vehicle during time period t, with charging being positive and discharging being negative. The total charging and discharging power of all electric vehicles in the area at time t. To add the real-time total load to the V2G backend area, The average load power during the scheduling period. This represents the total number of vehicles registered on the network. The current load deviates from the average load by a factor of T. The square root of T represents the average load deviation over the entire period. .

[0028] By minimizing this evaluation function, the load curve of the transformer area throughout the day can be made smoother, achieving the effect of peak shaving and valley filling.

[0029] As a preferred embodiment of the above technical solution, in step S2, the user's economic cost function... The expression is:

[0030]

[0031] In the formula, The electricity price for charging during period t. The total charging amount for the i-th vehicle during time period t. , The electricity price for discharge during period t is the subsidy price. The charging power for the i-th vehicle during time period t. Let be the discharge power of the i-th vehicle during time period t. The duration of a unit of time period This represents the total discharge capacity of the vehicle. The values ​​in parentheses represent the expenses and revenue of a single vehicle during a single time period; these are summed to obtain the total comprehensive cost. .

[0032] Minimizing this function maximizes the net profit for car owners, and price arbitrage is achieved through peak-hour discharge and off-peak-hour charging.

[0033] As a preferred embodiment of the above technical solution, in S2, the battery loss cost function The expression is:

[0034]

[0035] In the formula, Let the battery purchase cost be for the i-th vehicle. The total charge and discharge capacity of the vehicle during time period t. For the battery's rated capacity, , The discharge depth during time period t. To determine the battery cycle life at the corresponding depth of discharge, a power function relationship must be satisfied. ,in, The larger the diameter, the deeper the discharge. , These are the battery characteristic fitting parameters, corresponding to fixed fitting parameters for the battery model.

[0036] The larger the fractional value, the higher the battery wear and tear caused by this charge-discharge cycle. The total battery wear and tear cost is obtained by summing the values ​​over all periods and multiplying them by the battery purchase cost. .

[0037] Minimizing this function can suppress high-power, deep charge-discharge behavior and reduce the battery degradation rate.

[0038] As a preferred embodiment of the above technical solution, the adaptive weight adjustment algorithm in S3 is specifically as follows:

[0039] When the grid load factor exceeds the preset upper limit threshold, increase The value of should be used to strengthen the priority of peak shaving control;

[0040] When electricity prices are at their peak and users have enabled the revenue-priority mode, increase Weighting of discharge revenue;

[0041] When the battery SOC is below 20% or above 90%, increase The value should prioritize protecting battery health;

[0042] On the other hand, a charging and discharging control guidance generation system for V2G charging piles is provided, including:

[0043] The data acquisition module is used to collect real-time power grid operation data, charging pile group status data, vehicle battery status data, and user preference parameters.

[0044] The model building module is used to construct a multi-objective optimization guided model, with the optimization objectives of minimizing grid load fluctuation, minimizing overall user cost, and minimizing battery life loss. Three-objective optimization function, and storage Calculation formula;

[0045] The adaptive weight adjustment module is used to dynamically update the weight coefficients of each optimization objective based on real-time operating conditions, and automatically calculate and update them. , , ;

[0046] The optimization solution module is used to solve the multi-objective optimization guidance model under multiple constraints and generate the charging and discharging power guidance sequence for each vehicle.

[0047] The guidance and execution module is used to send the charging and discharging power guidance sequence to each V2G charging pile through the vehicle-to-charging pile communication link and execute the control.

[0048] As a preferred embodiment of the above technical solution, the data acquisition module includes:

[0049] The power grid data interface connects to the power bureau's distribution area terminal to read power grid load, electricity price, and dispatch instructions.

[0050] The charging pile group communication unit communicates with all V2G charging piles in the network and reads the operating status of the piles.

[0051] The vehicle-charging pile interaction unit enables interaction between the charging pile and the vehicle's BMS battery management system.

[0052] User interaction terminals, mobile apps, and screens receive user-set modes, target SOCs, and desired parameters.

[0053] The vehicle-to-pile interaction unit is based on the GB / T27930 protocol and extends the V2G dedicated message to realize the data interaction of battery SOC, maximum charge and discharge power, and discharge permit status, which corresponds to the vehicle-to-pile data transmission scope of IPC classification number B60L53 / 66.

[0054] As a preferred embodiment of the above technical solution, the optimization solution module adopts a hierarchical solution architecture:

[0055] The upper layer is the total power dispatch at the distribution area level, which determines the total charging and discharging power limit within the distribution area and locks the global power boundary from the grid side;

[0056] The middle layer is the power distribution at the pile group level, which distributes the total power to each charging pile;

[0057] The lower level operates at the vehicle-level power level, with the charging pile adjusting the output power in real time based on the guidance signal. The response speed is in the second range, which can adapt to the instantaneous changes in the vehicle's battery status.

[0058] By adopting a layered architecture, global optimization can be ensured while reducing cloud computing overhead and improving local response speed.

[0059] As a preferred embodiment of the above technical solution, the system further includes a safety verification module, which is used to perform reverse current verification, voltage over-limit verification, and battery safety boundary verification before issuing the guidance command.

[0060] Reverse current verification: to prevent large-scale vehicle discharge from back-feeding power and impacting the power grid transformer;

[0061] Voltage over-limit check: to prevent excessively high / low voltage in the transformer area due to charging and discharging;

[0062] Battery safety boundary verification: prevents damage to the battery from charging and discharging beyond the power or SOC range.

[0063] The reverse flow verification satisfies the following constraints:

[0064]

[0065] In the formula, Let be the total discharge power of all vehicles at time t. For the existing basic power load of the transformer area, This represents the allowable reverse feed-in power margin of the upstream power grid.

[0066] That is, the total discharge power of the vehicle shall not exceed the sum of the basic load of the transformer area and the allowable reverse feed-back capacity of the power grid.

[0067] In summary, this application includes the following beneficial technical effects:

[0068] 1. This invention overcomes the one-sidedness of traditional single-objective scheduling by constructing a multi-objective optimization model that covers power grid operation, user economy and battery health. It can achieve synergistic optimization of the interests of the three parties, effectively improve the enthusiasm of users to participate in V2G, and improve the regulation effect of the power grid in the distribution area.

[0069] 2. This invention introduces an adaptive weight dynamic adjustment strategy, which can switch the control focus in real time according to the load factor of the distribution area, the electricity price period and the battery SOC status. It enhances the peak shaving capability during the peak period of the power grid, focuses on user benefits during the peak electricity price period, and prioritizes battery safety when the SOC is in an extreme range, which greatly improves the adaptability under different operating conditions.

[0070] 3. This invention adopts a hierarchical solution architecture combined with an extended vehicle-pile communication protocol, achieving a time resolution of minutes for guidance commands. It balances the computational accuracy of global optimization with the response speed of local control, and has strong engineering practicality. Attached Figure Description

[0071] Figure 1This is a flowchart of the charging and discharging control guidance generation method for V2G charging piles of the present invention.

[0072] Figure 2 This is a schematic diagram of the overall architecture of the charging and discharging control guidance generation system for the V2G charging pile of the present invention.

[0073] Figure 3 This is a structural block diagram of the multi-objective optimization guidance model of the present invention;

[0074] Figure 4 This is a timing diagram of the hierarchical solution architecture of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments.

[0076] Example 1: Charging and discharging guidance in a peak-valley arbitrage scenario in a residential transformer area

[0077] This embodiment applies to a V2G charging pile group in a residential community. A typical configuration consists of 10 7kW bidirectional AC charging piles, a transformer capacity of 400kVA, and residential peak-valley time-of-use electricity pricing. During peak hours (10:00-15:00 and 18:00-21:00), the price is 1.2 yuan / kWh; during off-peak hours (23:00-7:00 the next day), the price is 0.3 yuan / kWh; and during the rest of the time, the price is 0.7 yuan / kWh.

[0078] S1: Data Acquisition

[0079] During the evening peak hours at 19:00, the basic load of the transformer area The load factor reached 70%, which is in the high-level operating range. Currently, there are 8 vehicles connected to the network, each with a rated battery capacity of 60kWh. The initial SOCs are 65%, 58%, 72%, 45%, 80%, 35%, 60%, and 50%, respectively. All users have enabled V2G participation mode and set the off-grid time to 7:00 the next day, with a target SOC of no less than 80%.

[0080] S2: Constructing a multi-objective optimization model

[0081] Set the scheduling period T=12 hours (19:00 to 7:00 the next day), unit time period There are a total of 48 time slots.

[0082] The objective function is as follows The initial weights are set to , , .

[0083] The load fluctuation evaluation function is:

[0084]

[0085] in, This represents the average load of the transformer area during that period.

[0086] The user's economic cost function is expressed as follows:

[0087]

[0088] The peak discharge subsidy is calculated at RMB 1.0 / kWh, and the off-peak charging price is RMB 0.3 / kWh.

[0089] The battery loss cost function is expressed as follows:

[0090]

[0091] Battery purchase cost 10,000 yuan, cycle life parameters , .

[0092] S3: Adaptive Weight Adjustment

[0093] The system is currently in the high-load range of the evening peak, with a load factor > 65%, triggering the weight adjustment rule: increase... Up to 0.5, Keep it at 0.35. Maintain a value of 0.15 and strengthen the priority of peak shaving control.

[0094] S4: Constraints and Optimization Solution

[0095] The constraints include:

[0096] Power constraint: The maximum charging and discharging power of a single pile is 7kW, i.e. ;

[0097] SOC constraints: Offline time ;

[0098] Power change rate constraint: Power change between adjacent time periods shall not exceed 50% of rated power;

[0099] Reverse current constraint: The total discharge power shall not exceed 80% of the base load of the transformer area, i.e. .

[0100] A mixed-integer programming method was used to solve the problem and obtain the charging and discharging power guidance sequence for each vehicle. The calculation results show that:

[0101] During the peak period from 19:00 to 21:00, vehicles with high SOC (5 vehicles with SOC > 60%) discharged at an average power of 5kW, accumulating a total discharge of approximately 62kWh. The peak load of the distribution area decreased by approximately 25kW, and the load factor dropped to 63.75%.

[0102] During the off-peak period from 23:00 to 7:00 the next day, all vehicles are charged at an average power of 6kW to replenish the battery to the target SOC.

[0103] The average revenue per user per discharge is approximately 48 yuan, the charging cost is approximately 21 yuan, and the net profit is approximately 27 yuan.

[0104] The battery discharge depth is controlled within 15%, and the loss per cycle accounts for approximately 0.012% of the total lifespan.

[0105] The above results show that the proposed solution can effectively achieve peak shaving and valley filling in residential transformer substation scenarios, while also balancing user economic benefits and battery life protection.

[0106] Example 2: Charging and discharging guidance in emergency peak shaving scenarios in industrial and commercial transformer areas

[0107] This example is applied to a commercial and industrial distribution area in an industrial park, equipped with 20 20kW DC V2G charging piles and a 1600kVA transformer in the area. At 2:00 PM one afternoon, a grid dispatch instruction was received that the distribution area needed to reduce its load by 150kW over the next two hours to address a regional grid shortage.

[0108] S1: Data Acquisition

[0109] The current base load in the front area is 1250kW, with a load factor of 78.1%. There are 16 vehicles connected to the network, with battery capacities ranging from 50 to 100kWh and an average SOC of 68%. All users have signed emergency response agreements.

[0110] S2-S3: Model and Weight Adjustment

[0111] Emergency peak shaving mode is triggered, and the weight is adjusted to , , The primary goal is to reduce load while ensuring users receive emergency subsidies.

[0112] S4: Optimization Solution

[0113] The scheduling period is set to 2 hours, with a unit time period of 5 minutes. In the objective function... Replace with the target of maximizing load reduction:

[0114]

[0115] A new constraint has been added: the maximum depth of emergency discharge is 30% for a single emergency discharge, ensuring that the vehicle's remaining range meets basic travel needs.

[0116] Solution results:

[0117] All 16 vehicles participated in the discharge, with an average discharge power of 10.5kW and a total discharge power of 168kW, which met the peak shaving requirement of 150kW.

[0118] The vehicle discharged continuously for 2 hours, with a total discharge capacity of 336 kWh. Users received an emergency subsidy of 0.8 yuan / kWh, with an average revenue of approximately 16.8 yuan per vehicle.

[0119] The average SOC of the vehicles decreased by about 12 percentage points, remaining above 50%, and the low battery protection was not triggered.

[0120] It is evident that this invention possesses excellent rapid response capability and load regulation effect in power grid emergency peak shaving scenarios.

[0121] Example 3: Charge and discharge guidance in battery health priority mode

[0122] This embodiment is for vehicles within the battery warranty period. When the user selects the "Battery Health Priority" mode, the system takes minimizing battery loss as the core control objective.

[0123] S1: Data collection: A vehicle has a battery capacity of 75kWh, current SOC of 55%, planned to be parked for 8 hours, target SOC of 90% off-grid, battery cycle life parameters: , The current battery temperature is 25℃.

[0124] S2-S3: Model and Weight Adjustment

[0125] Weights set to , , Battery loss has the highest weight. A battery loss correction coefficient is introduced, and the temperature acceleration factor is calculated based on the Arrhenius temperature model:

[0126]

[0127] In the formula, For activation energy, The gas constant is... This refers to the absolute temperature of the battery.

[0128] In this embodiment, Temperature acceleration factor If the battery temperature rises to 35℃ (308.15K), then As losses and costs increase significantly, the system will automatically reduce discharge power and depth.

[0129] S4: Optimization Solution

[0130] Battery loss constraints:

[0131] Depth of discharge (DOD) ≤ 20%;

[0132] Charge / discharge rate ≤ 0.3C;

[0133] Avoid prolonged float charging within the high voltage range of 80%-100% SOC.

[0134] The charging and discharging strategy is obtained by solving:

[0135] During the initial period of the off-peak period, charge slowly at a low power of 0.2C to 80% SOC;

[0136] The peak segment is shallowly discharged at a rate of 0.25C, and the DOD is controlled within 10%.

[0137] One hour before disconnecting from the grid, charge the battery to 90% SOC with a 0.3C charge.

[0138] Calculations show that this strategy reduces battery cycle loss by about 42% compared to the "economy-first" mode, effectively extending battery life. Although user benefits decrease by about 18%, it significantly reduces the risk of battery degradation.

[0139] This embodiment demonstrates the invention's ability to finely regulate battery health protection, adapting to the diverse usage needs of different users.

[0140] Example 4: System Architecture and Workflow

[0141] Combination Figure 1 As shown, the method flow includes: data acquisition and preprocessing, adaptive weight calculation, multi-objective model construction, constraint loading, optimization solution, security verification, guidance instruction issuance, and execution status feedback loop.

[0142] Combination Figure 2 As shown, the V2G charging and discharging control guidance generation system of the present invention adopts a three-level architecture of cloud, edge, and terminal:

[0143] Cloud-based scheduling layer: Deploys a multi-objective optimization engine and a weighted adaptive module to receive grid scheduling instructions and electricity price signals, and generate a total power guidance plan at the distribution area level.

[0144] Edge pile group layer: Deployed on the edge controller of the transformer area, it is responsible for pile group power distribution, local security verification, real-time data aggregation, and response latency <100ms.

[0145] Terminal charging pile vehicle layer: V2G charging piles interact with the vehicle BMS, execute power guidance commands, collect battery status data and send it up.

[0146] Combination Figure 3 As shown, the multi-objective optimization model includes three objective functions, four types of constraints, and a weight regulator. The input is multi-source operating data, and the output is the charging and discharging power guidance sequence.

[0147] Combination Figure 4 As shown, the hierarchical solution timing is as follows: global optimization is performed every 15 minutes, pile group redistribution is performed every 1 minute, and single vehicle power closed-loop adjustment is performed every 1 second, achieving an organic combination of global optimization and local fast response.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it.

Claims

1. A method for generating charging and discharging control guidance for V2G charging piles, characterized in that, Includes the following steps: S1. The data acquisition layer collects real-time power grid operation data, charging pile group status data, connected vehicle battery status data, and user preference parameters. S2. Construct a multi-objective optimization guided model, with the optimization objectives being to minimize grid load fluctuation, minimize overall user cost, and minimize battery life loss. Establish the objective function as follows: In the formula, For load fluctuation evaluation function, For the user's economic cost function, Let the battery loss cost function be used. , , These are the dynamic weight coefficients for the corresponding objectives, and satisfy the following conditions: ; S3. Based on the adaptive weight adjustment algorithm, the weight coefficients of each target are dynamically updated according to the real-time grid load rate, peak and valley electricity price periods, and battery SOC range. S4. Under the conditions of satisfying power constraints, SOC constraints, and time constraints, solve the multi-objective optimization guidance model to generate the charging and discharging power guidance sequence for each vehicle corresponding to each charging pile. S5. The charging and discharging power guidance sequence is sent to each V2G charging pile through the vehicle-to-charging-pile communication link, and the charging pile executes the corresponding charging and discharging control.

2. The charging and discharging control guidance generation method for V2G charging piles according to claim 1, characterized in that: In S2, the load fluctuation evaluation function The expression is: In the formula, The total number of scheduling periods. The base load power of the transformer area during time period t. Let represent the charging and discharging power of the i-th electric vehicle during time period t, with charging being positive and discharging being negative. The average load power during the scheduling period. This represents the total number of vehicles registered on the network.

3. The charging and discharging control guidance generation method for V2G charging piles according to claim 1, characterized in that: In step S2, the user's economic cost function The expression is: In the formula, The electricity price for charging during period t. The electricity price for discharge during period t is the subsidy price. The charging power for the i-th vehicle during time period t. Let be the discharge power of the i-th vehicle during time period t. The duration is for a single time period.

4. The charging and discharging control guidance generation method for V2G charging piles according to claim 1, characterized in that: In S2, the battery loss cost function The expression is: In the formula, Let the battery purchase cost be for the i-th vehicle. For the battery's rated capacity, The discharge depth during time period t. To determine the battery cycle life at the corresponding depth of discharge, a power function relationship must be satisfied. ,in , These are the parameters for fitting battery characteristics.

5. The charging and discharging control guidance generation method for V2G charging piles according to claim 1, characterized in that: The adaptive weight adjustment algorithm in S3 is as follows: When the grid load factor exceeds the preset upper limit threshold, increase The value of should be used to strengthen the priority of peak shaving control; When electricity prices are at their peak and users have enabled the revenue-priority mode, increase Weighting of discharge revenue; When the battery SOC is below 20% or above 90%, increase The value of [value] should prioritize protecting battery health.

6. A charging and discharging control and guidance generation system for V2G charging piles, characterized in that, include: The data acquisition module is used to collect real-time power grid operation data, charging pile group status data, vehicle battery status data, and user preference parameters. The model building module is used to build a multi-objective optimization guided model with the optimization objectives of minimizing grid load fluctuation, minimizing user comprehensive cost, and minimizing battery life loss. The weight adaptive adjustment module is used to dynamically update the weight coefficients of each optimization objective based on real-time operating conditions. The optimization solution module is used to solve the multi-objective optimization guidance model under multiple constraints and generate the charging and discharging power guidance sequence for each vehicle. The guidance and execution module is used to send the charging and discharging power guidance sequence to each V2G charging pile through the vehicle-to-charging pile communication link and execute the control.

7. The charging and discharging control and guidance generation system for V2G charging piles according to claim 6, characterized in that: The data acquisition module includes a power grid data interface, a charging pile group communication unit, a vehicle-charging pile interaction unit, and a user interaction terminal. The vehicle-to-pile interaction unit extends the V2G dedicated message based on the GB / T27930 protocol to realize data exchange of battery SOC, maximum charge and discharge power, and discharge permit status.

8. The charging and discharging control and guidance generation system for V2G charging piles according to claim 6, characterized in that: The optimization solution module adopts a hierarchical solution architecture: The upper layer is the total power dispatch at the distribution area level, which determines the total charging and discharging power limit within the distribution area; The middle layer is the power distribution at the pile group level, which distributes the total power to each charging pile; The lower level operates at the vehicle-level power level, with the charging pile adjusting the output power in real time based on guidance signals.

9. The charging and discharging control and guidance generation system for V2G charging piles according to claim 6, characterized in that: The system also includes a safety verification module, which is used to perform reverse current verification, voltage over-limit verification and battery safety boundary verification before issuing the guidance command; The reverse flow verification satisfies the following constraints: In the formula, This represents the allowable reverse feed-in power margin of the upstream power grid.

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